One counts the conversations that ended inside the assistant. The other counts the customers who did not have to come back. A program reported on the first number can look successful while quietly generating the repeat contacts that make it expensive.
Year one has a sponsor, a mandate and a set of visible wins. Year two has neither the novelty nor the owner, and the seams between functions quietly go back to being nobody’s responsibility — which is where the experience actually lives.
AI service projects fail on knowledge, not on the model.
The model is rarely the constraint. What limits the answer a customer receives is content that is scattered across systems, contradicts itself, is out of date, and belongs to nobody in particular. Fix that and a mid-range model performs; skip it and the best one cannot.
Web, app, contact center and branch each have a name, a budget and a target — and the targets fight each other. The path the customer actually walks runs across all four, and it is the one thing on the page with nobody accountable for it.
A pilot is production-shaped before anyone knows whether the idea works, so by the time it fails it has integrations, training and a launch date attached — and gets defended instead of stopped. Two weeks and a disposable build answers the same question for a fraction of the commitment.
Revenue retained on the journeys you invested in, cost to serve split by resolution rather than deflection, and effort at the moments that decide whether a customer stays. Relationship scores and volume metrics still have a place — just not as the headline.
Containment against resolution — hover a barThe stall — hover the curveThe constriction — hover a sectionOwnership — hover a columnTwo routes — hover a trackThe board view — hover a measure
Frequently asked
All 38 questions,
answered properly.
Customer experience strategy, innovation, AI in the
customer path, measurement, governance and how we work — answered the way we would
answer them in a working session rather than in a brochure. Search it, filter it, or read
straight through.
Showing 38 questions
Customer experience is the sum of every interaction a customer has with you, weighted by how much each one mattered to them at the time. In an enterprise that definition is inconvenient, because those interactions are owned by different functions with different budgets and different measures of success.
The practical consequence: CX is rarely broken inside one team. It breaks in the seams — between the claim decision and the notification, between the sale and the onboarding, between what marketing promised and what service can deliver.
Four owners, four budgets, one customer. Most CX problems live in the gaps between these boxes rather than inside them.
The working definition we use
Customer experience is a portfolio of decisions — who gets told what, when, by whom, and with what authority. That framing is useful because decisions have owners and bottlenecks. “Experience” on its own does not.
No, and conflating the two is the single most common reason CX programs underdeliver. Customer service is one channel through which experience is delivered — usually the one that absorbs the failures created everywhere else.
Customer service
Owns the contact once it arrives
Measured on handle time, quality, and satisfaction
Can only fix what reaches it
Customer experience
Owns whether the contact needed to happen at all
Measured on resolution, effort, and repeat contact
Can change the upstream cause
If your CX program lives entirely inside the contact center, it is a service improvement program. That is a legitimate thing to run — it is just not the same thing, and it will hit a ceiling defined by decisions made in other functions.
Four patterns, in roughly this order of frequency.
The portfolio is organized around channels and tools, not customer decisions. Initiatives are named after platforms. Nobody can point to what gets better for a customer.
Pilots have no path onto the floor. A promising thing demos well and never acquires an owner, a budget line, or an operating model.
Governance arrives last. Data, privacy, and brand questions surface after commitments are public, which is the most expensive possible moment.
There is no shared vocabulary. The board hears capability, service hears headcount risk, finance hears cost. Three pictures of the same program.
Where the stalls actually come from, in our experience. Note how little of it is technical — illustrative of the pattern we see, not a market study.
The tell
If your CX roadmap can be read by an outsider without them learning anything about your customers, it is a technology roadmap wearing a CX title.
Go deeper
Yes, but only if the map ends in a decision. Most journey mapping exercises produce a beautiful artifact and no change, because they document the journey without ever asking who owns each moment and what they are waiting for.
Map the journeys that carry the volume and the value, not all of them
Put the cost to serve on each stage — it changes which stages people care about
Name the decision owner at each moment where the customer waits
End with a ranked list, not a poster
A three-journey map that leads to two funded changes beats a twenty-journey map that leads to a workshop readout.
Someone with cross-functional authority and a budget. That is the entire answer, and it is why the question is hard — most CX leaders have one and not the other.
How far a CX mandate typically gets, by the authority behind it. Illustrative of the pattern rather than survey data.
Where that authority genuinely does not exist yet, a governance forum with written decision rights is the workable substitute — provided the rights are actually written down and the forum can stop things as well as start them.
Go deeper
It helps most where the work is high-volume, well-bounded, and slowed by retrieval or coordination rather than by judgment. It helps least — and hurts fastest — where an answer carries regulatory, financial, or emotional consequence.
The two dimensions that decide it. Anything low on the vertical axis can still be recognized by AI — it just must not be resolved by it.
The rule that survives contact with reality
Recognizing an intent and refusing to resolve it is a designed outcome, not a failure. The best assistants we have seen are defined as much by what they hand off as by what they answer.
Go deeper
Containment counts contacts that ended without a human. Resolution counts problems that stopped happening. They diverge exactly where it is expensive: the customer who gives up, calls back tomorrow, and arrives angry.
The only shape worth reporting: resolution climbing while repeat contact falls. Containment alone can rise while both of these get worse.
Define resolution as no repeat on the same driver within seven days
Report repeat contact in the same view as cost to serve, permanently
Split containment by intent — the average hides the harmful cases
The number to distrust
A containment rate quoted without a repeat-contact figure beside it is a marketing number. Ask for both, every time.
Go deeper
An agentic system does not just answer — it takes steps. It reads from your systems, decides what to do next, and in some designs writes back. That is the whole difference, and it is why the governance conversation changes shape entirely.
A chatbot stops at the first box. An agent moves through all four, which is where the value and the risk both live.
Read paths are relatively cheap to govern — the worst case is a wrong answer.
Write paths change something in the world, and need approval design, logging, and a rollback story before they go anywhere near a customer.
Orchestration is where most real engineering effort goes, and it is almost never what gets demoed.
A practical starting point
Separate read and write paths from day one. It is the cheapest risk control available and it lets you ship the useful half while the harder half gets designed.
Go deeper
Because retrieval quality sets the ceiling, and most enterprise knowledge was written for people to browse rather than for a system to retrieve. Duplication and contradiction are the real killers: retrieval returns one of two conflicting articles at random and nothing downstream can tell which is current.
The knowledge audit findings that matter, in the order they usually surprise people. Illustrative of a typical enterprise starting point.
No prompt change fixes a contradiction. This is why we treat knowledge as a sequencing question — it decides what can launch and when, rather than being a parallel workstream.
The single most useful number
Count how many live articles contradict another live article. It is usually the finding that rewrites the roadmap.
Go deeper
You cannot reduce that risk to zero, so the design question is what happens when it occurs and how quickly you find out. Four controls do most of the work.
Scope. A never-automate list agreed with risk before design starts — the intents where a wrong answer is a regulated event rather than a bad experience.
Grounding. Answers cited to a knowledge source, with a confidence threshold below which the assistant says nothing rather than guessing.
Evaluation. A maintained test set including adversarial and ambiguous cases, scored per category rather than on average.
Escalation. A designed path to a human that carries full context, so recovery is cheap when it does happen.
An evaluation set scored by category. The last line is the one that must be 100% — averaging it with the others is how a dangerous failure hides.
Score categories, never averages
An average lets one dangerous category hide behind four safe ones. Every gate is passed or the launch does not happen.
Go deeper
Almost always buy the platform and own the design. The differentiating asset is not the model or the runtime — it is your journey design, your knowledge, your escalation logic, and your evaluation set. Those are the things that make the same platform perform differently at two companies.
Where the differentiated value actually sits. The bottom line is the part most procurement processes spend the most time on.
The clause people forget
Score exit and data portability explicitly. A model tuned on your interactions that cannot travel is a larger five-year cost than the license gap you are negotiating.
Go deeper
In five ways that matter, roughly in the order most enterprises realize them. Notice that the first two involve no customer-facing AI at all — which is why they are usually the fastest to land.
The sequence that compounds. Each stage makes the next one cheaper and safer — starting at the bottom of this chart is the classic expensive mistake.
Assist — cited answers in the flow of the conversation, so the agent stops searching and starts resolving.
See — every interaction analyzed rather than a two percent QA sample, which turns opinion into evidence.
Prevent — proactive notification on the drivers that generate avoidable contact. Frequently the largest single win, and it requires no assistant.
Resolve — agentic handling of the bounded, high-volume intents, with designed escalation for everything else.
Personalize — next-best-action grounded in a unified view, once the foundation actually supports it.
The counter-intuitive part
The largest measured wins we see are usually contact removal and agent assist, not customer-facing automation. They are also the least exciting to announce.
Go deeper
With evidence, not with a platform. Three weeks of honest diagnosis will change what you buy, and it costs a fraction of the first wrong purchase.
Read the contact drivers — volume, handle time, repeat rate, cost to serve
Audit knowledge, data access, and platform reality before designing anything
Rank candidates on value, feasibility, and risk with weights agreed in advance
Sequence so the first phase unblocks the second, and gate each phase on evidence
Most organizations attempt box four first. It is the most visible and the most likely to stall.
If you only do one thing
Measure repeat contact on your two most automated journeys. It is cheap, it takes days, and it usually reframes the entire conversation.
Go deeper
No, but it decides the order. Fragmentation mostly affects two things: whether you can recognize the same customer across channels, and whether an assistant can read the systems it would need to answer honestly.
What each level of integration unlocks. Plenty of value is available at the top two levels, which is where most enterprises actually are.
A great deal of value sits above the integration line you already have. Agent assist, summarization, quality scoring, and conversation intelligence need very little from your core systems. Personalization and transactional automation need a lot.
Go deeper
Yes, and regulated organizations often do this better than unregulated ones — because they are used to designing controls up front rather than retrofitting them.
Separate the interactions that carry regulatory consequence from the ones that do not, explicitly and in writing
Put risk and legal in the design forum with standing membership, not at the review gate
Write boundaries with an enforcement mechanism, an evidence log, and a named owner
Agree launch gates in advance so approval is a measurement rather than an opinion
Every boundary carries all four. A boundary without an owner is a sentence, not a control.
The expensive pattern
The most costly stalls we see are not refusals — they are late refusals, where the commitment was public before risk was consulted.
Go deeper
It can be, and pretending otherwise is the fastest way to lose the floor. The honest position is that the work changes shape before it changes size: the simple contacts leave first, which means the remaining mix is harder, not easier.
What usually goes
Repetitive status and balance inquiries
Manual wrap-up and note taking
Hunting through knowledge mid-call
What grows
Judgment, exception, and emotional load
Coaching from complete quality coverage
Design and oversight of the automated paths
The communication principle that works
Say what changes about the job before you say what the tool does — and have leadership state their headcount intent in the same session. Uncertainty left unaddressed gets filled in with the worst case.
Go deeper
Depends entirely on where the constraint sits, but a rough shape from a standing start:
A common shape rather than a promise. The foundation bar is the one that moves most between organizations.
Weeks: an honest diagnosis and a ranked plan you can fund
One quarter: measurable movement on the service floor — assist, wrap-up, quality coverage
Two to three quarters: a customer-facing journey live, gated on evidence
A year: a portfolio with owners, governance, and a measurement layer that holds
The honest caveat
Anyone promising a customer-facing launch in six weeks is either working on something trivial or skipping the evaluation gate. Both end badly.
Three, and none of them is a satisfaction score on its own. A board weighs revenue, cost, and risk — so the CX numbers that travel are the ones that translate into those terms.
Measure
What it answers
Why a board cares
Resolution rate
Did the problem stop happening?
Cost that does not come back
Repeat contact
Are we counting savings twice?
Proves the saving is real
Cost to serve
What does each driver cost us?
Direct P&L line
Experience index
Is it getting better for customers?
Retention and revenue
Open risk items
What could stop this?
Downside exposure
The pairing rule
Never report a cost number without the repeat-contact number beside it. Either one alone can be made to look good, and boards eventually work that out.
Go deeper
As trend indicators, yes. As management instruments, only when they are decomposed. A single score tells you the temperature; it never tells you what to change on Monday.
Break the score down by driver and journey stage, not by team
Pair it with a behavioral measure — repeat contact, effort, abandonment
Set a movement threshold that opens an owned item, rather than a color that opens nothing
Reconcile the sample with your actual contact mix before trusting a shift
The trap
A score that moves two points and gets reported as a win, with no accompanying behavioral change, teaches a board to discount every CX number you present after it.
Baseline first, then instrument, then change one thing at a time. The reason ROI arguments fail at review is almost never the arithmetic — it is that no credible before-picture was taken.
Box four is the one teams skip. A number finance has not seen before the board meeting will not survive it.
Revenue side: retention on affected journeys, conversion where advice improved
Risk side: escalations, complaints, and regulatory events avoided
Honesty side: what you stopped, and what it cost to find out
The credibility move
Include the initiative you stopped and what it cost. A CX business case with no failures in it reads as marketing; one that names them reads as management.
Go deeper
Because a two percent sample can tell you the average and can never tell you the exception — and in service, the exceptions are where the cost and the risk live.
Moving from a sampled QA program to complete coverage. The remaining few percent are excluded by consent or retention rules, and that exclusion should be documented rather than quietly dropped.
Rare-but-expensive failure modes become visible instead of statistically invisible
Coaching attaches to a specific moment rather than to a monthly average
Driver analysis becomes reliable enough to schedule work against
Quality stops being a sampling argument between QA and operations
Go deeper
Insight programs die of “noted”, not of bad analysis. The fix is structural and slightly boring: give every finding exactly two possible exits.
A weekly loop with two exit states and nothing in between.
A finding leaves owned with a date, or closed with a reason. There is no third state
Aging is tracked — findings that quietly get old are the leading failure indicator
Movement is checked against the same measure that raised the finding
The chair is accountable for the absence of a third state, not for the fix
Go deeper
At design, with standing membership of the forum — not at a review gate. This is the single highest-return change available to most enterprise AI programs, and it costs nothing but a calendar invitation.
Risk at the review gate
Asked to approve a finished, public commitment
The only safe answer available is no
Rework is expensive and visible
Approval becomes an opinion under pressure
Risk in the design forum
Shapes the boundaries while change is cheap
Can say yes to a scoped version
Evidence is built as you go
Approval becomes a measurement against agreed gates
Go deeper
Four things per boundary. If any of the four is missing, you have a policy sentence rather than a control.
Element
The question it answers
Failure mode without it
Boundary
What must it never do?
Ambiguity at the worst moment
Enforcement
How is that made true?
A rule nothing implements
Evidence
What gets logged?
Nothing to show an auditor
Owner
Who answers for it?
A control nobody maintains
A worked example
“The assistant must not state what a policy covers.” Enforcement: coverage intents are on the never-automate list and route on recognition. Evidence: every coverage-intent contact logs the recognition event and the route taken. Owner: Head of Product Compliance.
Go deeper
Three modes, defined precisely enough that nobody can quietly reinterpret them under load.
Mode
What it means
Where it belongs
Monitor
Acts alone; humans review samples
Low impact, reversible
Review before effect
Customer sees nothing until approved
Financial or contractual
Approve each time
A person decides, every instance
Adverse or regulated decisions
The failure that looks like success
A review queue that auto-approves when it exceeds its SLA is not a checkpoint — it is a checkpoint on a diagram. Design the degradation: past SLA, the path drops to human-only rather than waving things through.
Go deeper
Yes — and increasingly you will be required to. Beyond the compliance argument, disclosure is simply better design: customers calibrate their expectations, and the interaction degrades more gracefully when the assistant reaches its limit.
Disclose in the opening turn, in plain language, without an apology
Make the route to a human obvious and always available — hiding it costs more trust than the automation earns
Never simulate a named human persona; it converts a small failure into a betrayal
Log the disclosure so it can be evidenced later
Refusing well is a design skill
“I can't advise on what your policy covers — that has to come from a licensed specialist. I've got your claim number and what you've told me, and I'm passing both across now.” That is a better experience than a confident wrong answer, and customers know it.
Go deeper
A customer experience design agency for the AI era, engaged fractionally. We design the experience end to end — the journeys, the service model, and the human and AI agents that deliver it — and stay with the work until it runs on the floor.
Fractional is the engagement model rather than the identity: you get a senior CX and AI design team for the work in front of you, without carrying the role through the quarters when the decisions are not being made.
Nine offerings arranged as one loop. Most enterprises enter where their constraint sits rather than at the beginning.
What we deliberately do not do
We do not resell platforms, take vendor referral fees, or staff long implementation benches. The advice stays independent and the build work stays sized to what your team can own after we leave.
Go deeper
There is no standard engagement, because each of the twenty-two solutions is engaged differently — a different room, different work, different artifacts, and a different test it accepts being measured against. Most clients start with one, and add the next when the first one earns it.
We do not put a duration against any of it before it is scoped. How long the work runs is set by scope and complexity, and we would rather agree that with you at scoping than guess it in a brochure.
How it is engaged
What that means
Commercial
Fixed scope
A defined piece of work with named artifacts, sized at scoping
Fixed fee
Repeated cycles
The same loop run again, with a decision at the end of each one
Fixed fee per cycle
Embedded alongside your teams
Senior capacity inside your squads, with a handover written in from the start
Fixed fee or embedded
Rolling or retained
A standing seat, or a project that becomes the thing that keeps it running
Monthly retainer
The test we hold ourselves to
The sponsor forwards our summary rather than rewriting it. If they have to rewrite it, the session did not land.
Go deeper
Because the scarce thing is senior judgment at the moment a decision is being made — and that is not a full-time requirement in most organizations, nor is it what a large delivery bench is optimized to provide.
Time to the first decision that changes something. The hire is often the right long-term answer — it is just rarely the right next step.
Seniority in the room while the decisions are still open
No standing bench to keep busy, so scope stays honest
Independent of platform vendors, with no referral economics
Capability transfer written into the engagement, with a handover review
When fractional is the wrong answer
If you need forty consultants to run a migration, we are not that and will say so. Our work is design, sequencing, and governance — not delivery capacity.
Go deeper
Less than most engagements, and all of it is about access rather than effort. The preparation load sits with us by design — an engagement that consumes your leadership team before it produces anything rarely reaches its own readout.
An executive sponsor who can open doors across service, data, and risk
Read access to contact reporting and a quality sample
Whatever CX and journey documentation exists, however dated or unflattering
Calendar time for four to six short conversations
Permission to play the current state back honestly
Executive time for a typical Executive Briefing, role by role. Most of it is the session itself.
The last one matters most
Permission to be honest about the current state is the input that decides whether any of this is worth doing. A softened read is worth nothing to the person who has to act on it.
Go deeper
A strategy names what you are not going to fund. That is the whole test. If every journey on the map is in scope and every function got something, what you have is a budget allocation with an ambition written on the front of it.
The reason this matters is not purity. It is that an experience plan without a not-this list gets re-litigated every quarter, because nothing in it establishes why one journey outranks another. Every new idea arrives with equal standing, and the loudest sponsor wins by default.
Journeys ranked by influence on whether a customer stays. The uncomfortable one is everyday transactions: the most customers, the least influence. Holding it at competitive parity is usually what funds the two above it.
The one-line test
Ask three executives to name a journey the company has deliberately chosen not to improve this year. If you get three different answers, or none, there is no strategy yet — there is a wish list with a governance layer.
A workable CX strategy is short: the ambition in a sentence, two or three journeys chosen on evidence, an explicit parity list, the measures agreed before the work starts, and a named owner with budget authority for each invested journey. Everything else is appendix.
Go deeper
A roadmap answers when. A strategy answers why these and not those. Most organizations have the first and assume it implies the second, which is why roadmaps survive planning and then quietly reorder themselves under pressure.
A roadmap without a strategy
Every item is defensible on its own merits
Reordering costs nothing, so it happens constantly
New ideas arrive with equal standing and get inserted
The sequence reflects who asked, not what compounds
A roadmap under a strategy
Items are defensible against a stated choice
Reordering has to argue with the direction on the record
New ideas are assessed, and declining them is normal
The sequence follows dependencies and value
The practical tell is what happens mid-year when something urgent arrives. Under a strategy, the conversation is whether the new thing beats what it would displace. Without one, it simply gets added, and the roadmap silently becomes a queue.
The expensive version of this mistake
Reorganizing before deciding. Redrawing boxes without redrawing decision rights moves the same disagreement into a new structure, and costs a year doing it.
Go deeper
Because organizations are funded by function and experienced by journey, and almost nobody has reconciled the two. Service owns the contact, digital owns the app, marketing owns the promise, and the customer owns the only view where those are the same thing.
The result is four teams optimizing four handoffs while the customer experiences one broken path. Each team hits its numbers. The journey still fails, and no dashboard shows it.
The fix is a journey owner with budget authority, not another coordination forum. Coordination is what you do when nobody can decide.
What a real journey owner has
Accountability for a journey-level outcome measure, not a channel metric
Budget authority sufficient to move work between functions
The right to say no to work that does not serve the journey
A seat in the forum where funding is actually decided
Without all four, you have created a coordinator with a job title, and the first budget conflict will demonstrate it.
Go deeper
Mostly the second, and treating it as the first is a common and expensive error. The journeys that decide whether customers stay do not change because a model got better. What changes is the cost and the speed of serving them, which changes what is affordable — and therefore what is worth choosing.
So AI belongs in the strategy conversation as an input to feasibility, not as the ambition. “We will use agentic AI” is not a customer experience strategy. It is a delivery assumption that has not yet been tested against a customer decision.
The tell that AI has captured the strategy
The plan is organized by capability — a chatbot workstream, a knowledge workstream, an analytics workstream — rather than by journey. Capability-shaped plans deliver capability and leave the customer experience roughly where it was.
There is one genuine exception. When a capability makes something possible that simply was not before — proactive contact at a scale no contact center could staff, for instance — it can open a strategic choice that did not exist last year. That is worth reopening the strategy for. Incremental efficiency is not.
Go deeper
A cadence for being wrong cheaply. Most enterprises are not short of experience ideas; they are short of a mechanism for finding out which ones are any good before committing a delivery year to them.
Practically, that means framing an idea as a testable bet with a stated stop condition, building only the risky part of it, putting it in front of real customers against criteria agreed before the build, and taking a scale, iterate, or stop decision at the end. Two to six weeks per answer.
The criteria are written before anything is built. That is what makes the result impossible to reinterpret afterward.
Why the stop condition comes first
A hypothesis without a stop condition is a wish. Writing down in advance what result would make you abandon the idea is the single cheapest discipline in the whole method, and the one most often skipped.
What it is not: a lab that produces demos, an accelerator that produces relationships, or an annual event that produces slides. Those can be worthwhile; none of them answers whether the concept works.
Go deeper
A pilot is a small version of the thing you have already decided to build. A prototype is how you decide whether to build it. Running a pilot when you needed a prototype is how organizations spend a year finding out something six weeks would have told them.
A pilot
Comes after the build decision
Built on production-grade foundations
Measured on rollout readiness
Politically difficult to stop, because it is already funded
A prototype
Comes before the build decision
Real only where the risk sits; everything else faked
Measured against criteria set in advance
Designed to be thrown away without regret
The distinction shows up in what gets built. A prototype makes the risky part real — the live model behavior, the moment of trust, the thing nobody can predict — and fakes the authentication, the data, the integrations. A pilot has to build all of it, which is why it costs what it costs.
Read a sprint duration as an estimate at your peril
The parts a prototype fakes are usually the parts that take the engineering time. A concept proven in two weeks is not a concept that ships in two weeks, and saying so in the room is part of the method.
Go deeper
Enough that the process is visibly doing something. A portfolio where nothing is ever stopped is not testing ideas; it is decorating decisions that were already made. We report the stop rate as a measure of health rather than as an embarrassment.
There is no correct number, and anyone quoting one is selling something. But the direction is clear: if your last eight concepts all proceeded, either you are unusually good at picking them or your criteria are not load-bearing. In our experience it is the second one.
One cycle, four concepts, four different answers. A cycle where every concept scales is a cycle that learned nothing it did not already believe.
What makes a stop cheap rather than painful
The stop condition was agreed before the build, so nobody is being overruled
The sponsor committed in advance to act on a stop recommendation
The concept was built to be discarded, so there is no sunk asset to defend
The stop is written up with its evidence, so the idea does not quietly return next year
That last one matters more than it sounds. An undocumented stop is a deferral, and deferred ideas have a way of reappearing in the next planning round with the objection forgotten.
Go deeper
By ending every cycle in a funding decision rather than in a showcase. A concept that scales leaves the cycle with a cost to proceed attached and enters the same portfolio conversation as everything else. If it cannot win there, it should not have scaled.
The failure mode is a lab that reports to itself. It produces increasingly impressive prototypes, none of which enter delivery, and after a year it is quietly defunded — usually with the conclusion that innovation does not work here, which is the wrong lesson.
The structural fix
The person who chairs the funding forum sits in the decision session at the end of each cycle. Not a readout afterward — the actual session. Concepts then compete for money in the room where money is decided.
Three connections that keep it honest
Framing draws from the journey work, so concepts start where the evidence says value leaks
Deciding feeds the roadmap, with a cost to proceed sized well enough to fund or decline
Frontline teams are in the sprints, so what scales has already survived the people who will run it
Run that way, the cadence is not parallel to delivery. It is the front of it — the part that decides what delivery should spend the year on.
Go deeper
Nothing matches that yet
Try a broader term — or ask us directly and we will answer it properly.
Service offering 01
CX AI Readiness & Opportunity Assessment
A fixed-scope diagnostic that answers one question honestly: what should you fund first, and what will it return?
EngagementSet by scope and complexity
CommercialFixed fee
CadenceWeekly working session
RoomSponsor plus 5–9 owners
Scope of work
Contact driver analysis across voice, chat, email, and self-service for a representative 90-day window
Journey mapping for the three to five journeys carrying the most volume and the most value
Data, knowledge, and platform audit — CCaaS, CRM, and the content retrieval would have to rely on
Six to ten stakeholder interviews across service, digital, data, risk, and brand
Use-case scoring against value, feasibility, and risk, with a modeled return on each line
Readout and one revision pass with the executive sponsor
What it changes for the business
One ranked list the leadership team can argue with, instead of five competing wish lists
An investment narrative finance and the board can actually defend
Blockers surfaced early — knowledge quality, data access, risk posture — before money is committed
A sequence where each phase pays for the next one
Deliverables click any one to open a sample
What this has looked like in practice
Multi-line insurer
The ranked backlog put proactive claim-status notification ahead of the voice assistant the board had already been shown. It removed more contact in two quarters than the assistant was ever modeled to contain.
Global manufacturer
The readiness scorecard stopped a launch that was four weeks out. Retrieval accuracy sat well below the gate on half the test journeys, and the knowledge work was done first instead.
Retail bank
Three competing wish lists became one sequence the executive team argued with and then signed. The phasing held through two budget cycles without being rewritten.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
An executive sponsor who can open doors across service, data, and risk
Read access to contact volumes, dispositions, and a QA sample
Whatever journey and CX documentation already exists, however dated
How we measure it
The ranked backlog is accepted by the executive team, not just received
Every phase in the roadmap leaves with a named owner
The investment narrative survives its first finance review
Service offering 02
Fractional CX Leadership
A senior CX and AI executive embedded with your leadership team, carrying the roadmap and the decisions between board meetings.
EngagementSet by scope and complexity
CommercialMonthly retainer
CadenceStanding monthly commitment
RoomExecutive team and their direct reports
Scope of work
Standing counsel to the CEO, COO, CCO, CIO, or CX lead
Ownership of the CX and AI roadmap, and the operating cadence that keeps it moving
Portfolio and vendor decision support ahead of commitments rather than after them
Governance forum design — who decides what, on what evidence, how often
Board and executive communication, written in the language the business already uses
Coaching for the internal leaders who will own the capability when we leave
What it changes for the business
Seniority in the room at the moment decisions are made, not in a readout three weeks later
A single accountable CX voice without a full-time executive hire
Vendor and platform commitments pressure-tested before signature
Internal capability that compounds instead of dependency that deepens
Deliverables click any one to open a sample
What this has looked like in practice
Healthcare provider network
Decisions that had been deferring a quarter at a time started closing inside the monthly cadence, because the forum finally had named decision rights and evidence in front of it.
Logistics operator
A CCaaS commitment was pressure-tested against the actual journeys two weeks before signature. The scope changed materially, and so did the price.
Subscription business
An internal CX lead was running the cadence unaided by month six, which was the point of the engagement rather than a happy side effect.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
A seat in the forums where CX and AI decisions actually get made
Visibility of the portfolio, the budget, and the vendor commitments in flight
Agreement that we can say the unpopular thing in the room
How we measure it
Decisions get made on schedule instead of deferring another quarter
The roadmap survives budget cycles without being rewritten
Named internal owners can run the cadence without us by month six
Service offering 03
Agentic Service Design & Deployment Plan
The design behind AI agents that face customers: what they resolve, when they hand off, how they sound, and how you know they are safe.
EngagementSet by scope and complexity
CommercialOutcome-priced project
CadenceTwice-weekly build reviews
RoomService, digital, data, risk
Scope of work
Intent and containment design grounded in your actual contact mix, not a generic taxonomy
Orchestration blueprint across CCaaS, CRM, knowledge, and systems of record
Escalation and handoff design that carries full context to the human
Conversation and tone design so the agent sounds like the brand under pressure
Evaluation sets, adversarial testing, and launch gating criteria
Pilot-to-floor transition plan with owners, metrics, and rollback
What it changes for the business
Containment that comes from resolution rather than from customers giving up
A launch decision made on evidence, with the gate criteria agreed in advance
Customers who never repeat themselves at the handoff
A plan your delivery teams can build from without re-interpreting it
Deliverables click any one to open a sample
What this has looked like in practice
Telecommunications
Containment was redefined as resolution, with repeat contact tracked beside it. The reported number fell, and the real one improved — which was the harder conversation to have.
Travel platform
Launch was gated on an evaluation set agreed before the build. Two adversarial cases failed, the gate held, and the fix shipped before customers ever saw it.
Utility
Escalations began arriving with full context in the advisor's hands. Customers stopped repeating themselves at the handoff, which showed up in satisfaction before it showed up in handle time.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
A representative sample of real transcripts across channels
Access to the systems the agent would need to read from and write to
A named launch decision-maker who can hold the gate
How we measure it
Containment measured as resolution, with repeat contact tracked alongside it
Escalations arrive with context in the agent's hands, verified in QA
No launch without the agreed gate criteria met on the evaluation set
Service offering 04
Contact Center Modernization & Agent Assist
What makes the service floor measurably better before AI faces a customer — plus the knowledge foundation everything else depends on.
EngagementSet by scope and complexity
CommercialOutcome-priced project
CadenceWeekly floor review
RoomService ops, QA, knowledge, IT
Scope of work
Real-time assist and next-best-action design inside the flow of the conversation
Automated summarization and disposition standards agents will actually trust
Automated quality scoring across all interactions, with coaching routed to supervisors
Knowledge base restructure and content governance to the standard retrieval requires
Supervisor and coaching workflow redesign around the new signal
Change plan for the floor: pilot team, feedback loop, and rollout
What it changes for the business
Handle time and after-call work reduced without pushing agents to rush customers
Quality coverage across every interaction instead of a two percent sample
Knowledge that answers the question rather than returning nine articles
The fastest measurable win when AI is not yet ready to face customers
Deliverables click any one to open a sample
What this has looked like in practice
Financial services
After-call work fell while quality scores held, because summarization was designed with the agents who would have to trust it rather than for them.
Public sector service
Quality coverage moved from a two percent sample to every interaction, and coaching started reaching the supervisors who could act on it the same week.
Insurance
The knowledge restructure lifted retrieval accuracy above the threshold that had been blocking three separate AI use cases. All three became viable without any further model work.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
A pilot team of agents and their supervisor, freed to give real feedback
The current knowledge base in whatever state it is actually in
Existing QA rubric and a sample of scored interactions
How we measure it
After-call work down with quality scores holding or improving
Quality coverage moved from sampled to complete
Knowledge retrieval accuracy measured before and after restructure
Service offering 05
Conversation Intelligence & Voice of Customer
From a two percent QA sample and a score nobody can act on, to driver-level insight the operating team schedules work against.
EngagementSet by scope and complexity
CommercialProject into managed service
CadenceWeekly insight review
RoomCX, service ops, analytics, finance
Scope of work
Analysis across every interaction — voice, chat, email, and survey text
Driver taxonomy built from your contacts, tied to journeys and root cause
Predictive signals for churn, escalation, and repeat contact
Executive dashboard specification connecting experience to revenue and cost to serve
Insight-to-action operating rhythm, so findings reach an owner with a deadline
Baseline measurement and the method for proving movement
What it changes for the business
Root cause instead of sentiment averages nobody can act on
Early warning on churn and escalation while there is still time to intervene
A defensible link between experience movement and the P&L
The evidence base that makes every other CX investment arguable
Deliverables click any one to open a sample
What this has looked like in practice
E-commerce
The driver taxonomy was rebuilt until two analysts classified the same contact the same way. Only then did the dashboard stop being argued with in the meeting.
Business services
Churn signals surfaced far enough ahead of renewal for the account team to intervene, which is the only version of a churn model that is worth running.
Media subscription
Experience movement was tied to revenue and cost to serve on assumptions finance had already agreed. The CX number entered the operating review and stayed there.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
Interaction data across channels, with the retention window you actually keep
Survey history and whatever VoC program already exists
An analytics counterpart who can validate the taxonomy against the business
How we measure it
Two analysts classify the same contact the same way
Every top driver has a named owner and a dated action
Predicted churn and escalation validated against what actually happened
Service offering 06
CX Governance, Trust & Enablement
The guardrails, evidence, and change work that decide whether an AI-led service model survives risk, legal, and the people running it.
EngagementSet by scope and complexity
CommercialAttaches to any engagement
CadenceRisk forum every 2 weeks
RoomRisk, legal, compliance, service, HR
Scope of work
Responsible-use policy and model oversight written for your regulators, not a template
Guardrails, evaluation sets, and red-team cases for customer-facing AI
Human-in-the-loop design on the paths that carry real consequence
Evidence trail and audit artifacts the board and regulator can both follow
Role redesign and reskilling for agents, supervisors, and quality teams
Adoption, communication, and the measurement that proves it stuck
What it changes for the business
Approval becomes a step in the plan rather than a wall at the end
Risk and legal on your side of the table from design, not review
Agents who understand what changed and what their role becomes
An audit trail that holds up the day someone asks how a decision was made
Deliverables click any one to open a sample
What this has looked like in practice
Regulated lender
Risk and legal joined at design rather than at review. Approval became a scheduled step in the plan instead of the wall the previous two programs had hit.
Health insurer
The red-team pass found failure modes on eligibility questions that the accuracy testing had cleared. The guardrail was redrawn before launch, not after an incident.
Retail group
Adoption was measured on the floor rather than assumed from training completion, which surfaced a supervisor workflow problem nobody had reported upward.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
Your existing model risk and third-party frameworks, however heavy
Access to risk and legal early enough to shape the design
An HR or enablement partner for the role and training work
How we measure it
Risk and legal sign the design without a late-stage rewrite
Every customer-facing path has a documented guardrail and owner
Adoption measured on the floor, not assumed from a training completion rate
Service offering 07
CX Strategy & Operating Model Design
In the experience economy the experience is the product. This decides the one you intend to sell, who owns delivering it, and the decision rights that keep it intact.
EngagementSet by scope and complexity
CommercialFixed fee
CadenceWeekly executive session
RoomCEO, COO, CCO plus function heads
Scope of work
Experience ambition set against what customers actually value, and what competitors already do
Segment and journey prioritization — where experience earns a return and where it does not
Target operating model across service, digital, data, and brand, including where AI sits in it
Decision rights, funding routes, and the forums that turn strategy into scheduled work
Capability gap assessment against the model, with build, buy, and partner calls made explicitly
The value case: what the strategy is worth, on assumptions finance will accept
What it changes for the business
One experience direction the executive team has actually signed, not five functional versions of it
An operating model that names who owns the customer outcome, not just the channel
Investment decisions made against the strategy instead of against whoever asked most recently
A strategy that survives the reorganization, because the decision rights outlive the org chart
Deliverables click any one to open a sample
What this has looked like in practice
Regional bank
Three functions had been running separate experience programs against overlapping journeys. Consolidating to one owned direction released roughly a fifth of committed CX spend, which was redirected into the two journeys carrying actual attrition risk.
B2B software
Support and product had opposing views of who owned onboarding. Naming the outcome owner and the forum that decides moved a stalled roadmap into delivery inside one quarter.
Utility
The value case was built with finance in the room rather than presented to them afterward. The program cleared its investment committee on first submission, having previously failed twice.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
The strategy the business is already committed to — we align to it rather than around it
Access to the executives who own service, digital, data, and brand
Whatever customer research, segmentation, and financials already exist
How we measure it
The executive team signs one direction rather than tolerating several
Every priority journey has a named owner with budget authority
The next funding round is argued against the strategy, on the record
Service offering 08
CX Innovation Lab & Experience Prototyping
A cadence for testing experience ideas cheaply — concept to prototype to real customer feedback in weeks, so the big commitments rest on evidence.
EngagementSet by scope and complexity
CommercialFixed fee per cycle
CadenceTwo-week sprints
RoomProduct, design, service, engineering
Scope of work
Opportunity framing from journey pain, emerging capability, and competitive white space
Two-week prototyping sprints producing something a customer can actually use, not a slide
Live AI prototypes where the idea depends on model behavior rather than on interface alone
Customer and frontline testing with pre-agreed success criteria set before anything is built
Scale, iterate, or stop decisions taken on the evidence, with the stop cases recorded and closed
The internal cadence, roles, and funding route so the lab runs after we leave
What it changes for the business
Ideas killed in weeks for the cost of a sprint instead of dying in year two of a program
Executive debate settled by a working prototype rather than by seniority
A pipeline of tested concepts feeding the roadmap, so innovation stops being an annual offsite
Frontline teams shaping the experience before it is built, which is also how adoption starts
Deliverables click any one to open a sample
What this has looked like in practice
Health insurer
A conversational claims assistant that had strong executive backing failed its second customer test on trust rather than accuracy. Stopping it at prototype cost one sprint instead of the delivery year already provisionally scheduled.
Specialty retailer
Four proactive-service concepts were prototyped in eight weeks. One reached the floor, two were merged, and the fourth was stopped — the merged concept became the following year's largest experience investment.
Telecommunications
Frontline advisors joined the sprints from day one. Adoption of the resulting tool ran materially ahead of previous rollouts, largely because the people using it had already argued with it.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
Access to real customers or frontline staff for testing, booked before the sprint starts
A sandbox environment and, where the concept needs it, model access
An executive who agrees in advance to act on a stop recommendation
How we measure it
Every sprint ends with a decision, not with a request for another sprint
Concepts are tested with real users before any scale commitment
The stop rate is healthy — a lab that never kills anything is not testing anything
Service offering 09
Customer Journey Reinvention
Not a journey map on a wall — the end-to-end redesign of the journeys that carry your value, around the moments that decide whether customers stay.
EngagementSet by scope and complexity
CommercialOutcome-priced project
CadenceWeekly design review
RoomCX, service, digital, operations, brand
Scope of work
Evidence-based journey diagnostic from interaction data, research, and operational reality
Identification of the signature moments — the few interactions that actually move loyalty
Redesign of those moments across channels, with human and AI roles decided deliberately
Service blueprint behind each moment: the systems, data, teams, and policies that must change
Upstream root-cause work, because most contact is caused before the customer ever gets in touch
Sequenced reinvention roadmap with the measurement that proves each release moved something
What it changes for the business
Effort concentrated on the moments that change behavior instead of spread evenly across the map
Demand removed upstream rather than absorbed downstream by more capacity or more automation
A blueprint delivery teams can build from, including the policy changes nobody wants to own
Movement you can prove, because the baseline was taken before anything shipped
Deliverables click any one to open a sample
What this has looked like in practice
Property insurer
The diagnostic put the loyalty risk in the first notification after a claim was filed, not in the settlement everyone had been optimizing. Redesigning that one moment reduced repeat contact on claims by roughly a third.
Consumer lender
Two thirds of collections contact traced to a statement customers could not interpret. Rewriting the statement removed more demand than the automation program it replaced.
Airline
Disruption re-accommodation was rebuilt with the agent and the app sharing one state. Customers stopped calling to confirm what the app had already told them, and the contact peak during disruption flattened noticeably.
Composites of recurring engagement patterns, not the reported results of any named client.
What we need from you
Interaction and journey data for the period you want to be measured against
Access to frontline teams and to real customers, not only to their proxies
Willingness to change policy, because some moments cannot be designed around one
How we measure it
Each signature moment has a baseline taken before the redesign ships
Root causes are fixed upstream where the evidence says the demand originates
Journey-level movement is reported alongside the operational numbers, not instead of them
Sample deliverable
Contact driver analysis
Sample — multi-line insurer, 90-day window across voice, chat, and email
28 pagesXLSX model + slidesAudience: CX, service ops, finance
What is in it
Every contact reason sized by volume, handle time, and fully loaded cost to serve
Repeat-contact rate per driver — the number that separates deflection from resolution
Channel mix per driver, so the automation question is asked per journey rather than per platform
The 20 percent of drivers that generate the majority of avoidable contact
Excerpt — finding 03
Claim status accounts for 31% of contacts and 38% of repeat contacts.
Customers contact a median of 2.4 times per claim, and 61% of those
contacts arrive within 48 hours of a status change the customer was
never told about. This is not a containment problem. It is a proactive
notification problem wearing a containment costume.
Sample detail
Driver
Volume
Avg handle
Repeat rate
Cost to serve
Claim status & updates
434k
6m 10s
38%
$3.9M
Billing & payments
336k
4m 45s
21%
$2.3M
Policy changes
252k
8m 20s
14%
$3.0M
Coverage questions
210k
9m 05s
26%
$2.7M
What it changes
Two of the top four drivers are caused upstream, not in the contact center
Proactive notification would remove more contact than any assistant would contain
Cost to serve per driver, not per channel, is what makes the ranking arguable
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Ranked use-case backlog
Sample — 23 candidates scored, top 8 shown
Scored on value, feasibility, riskModeled return per caseLive in the roadmap model
What is in it
Every candidate scored on the same three axes, with the rationale attached to each line
Modeled annual return and the assumptions behind it, editable in the spreadsheet
Dependencies made explicit — what has to be true before a case can start
Explicit not-now list, with the reason recorded so it stops being re-litigated
Excerpt — scoring note, case 09
Full voice containment scores 34. Value is real but feasibility is low:
the knowledge base fails retrieval accuracy on 4 of 6 test journeys, and
authentication cannot yet be completed in-agent. Revisit after the
knowledge restructure lands. Recorded as not-now, not as rejected.
Sample detail
#
Use case
Value
Feasibility
Risk
Modeled return
1
Proactive claim status
High
High
Low
$4.1M / yr
2
Billing self-serve
High
High
Low
$2.6M / yr
3
Agent assist — policy
Med
High
Low
$1.8M / yr
4
Auto-summarization
Med
High
Low
$1.2M / yr
What it changes
The ranking is defensible line by line because the scoring rationale travels with it
Sequencing follows dependencies, so phase one unblocks phase two
The not-now list is as valuable as the yes list in a budget conversation
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Readiness scorecard
Sample — readiness across six dimensions, scored 0–100
6 dimensionsEvidence attached per scoreRe-scored at each phase gate
Composite readiness — adequate to start phase one, not to start phase three. Knowledge and data access are the binding constraints, and both are fixable inside the first quarter.
What is in it
A score per dimension with the evidence behind it, not a colored traffic light
The binding constraint named explicitly, so effort goes where it changes the answer
What each dimension needs to reach the next threshold, with an owner
Re-scored at every phase gate, so readiness is tracked rather than assumed
Excerpt — dimension 04, knowledge
Knowledge scores 41. Of 2,140 live articles, 38% have not been reviewed in
24 months and 14% contradict another live article. Retrieval accuracy on
the six test journeys averages 62%. No AI-facing use case that depends on
retrieval should be gated to launch before this reaches 85%.
Sample detail
Dimension
Score
Binding constraint
Owner
Data access
52
No unified contact ID across channels
Data platform
Knowledge
41
Stale and contradictory content
Service enablement
Platform
74
CCaaS is capable; integration debt
IT
Operating model
63
No owner for AI in the customer path
CX leadership
What it changes
A composite score is only useful when the binding constraint is named underneath it
Readiness is a gate on sequence, not a reason to stop
Two of six dimensions are fixable inside a quarter and unlock the rest
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Board-ready roadmap
Sample — four phases across five quarters, with investment narrative
Slides + editable sourceOwner per phaseFinance-reviewed
What is in it
Phases sequenced so each one funds and unblocks the next
Investment, expected return, and confidence level stated per phase
A named owner and a decision gate at every boundary
The narrative written in the language the board already uses for other capital
Excerpt — phase gate 2 → 3
Phase 3 does not start on a date. It starts when three things are true:
knowledge retrieval accuracy above 85% on test journeys, unified contact
identity in production, and the responsible-use policy signed by risk and
legal. If those slip, phase 3 slips. The board is asked to approve the
gate, not the calendar.
Sample detail
Phase
Window
Investment
Expected return
Gate
1 Foundation
Q4 26 – Q1 27
$0.9M
Enabling
Knowledge ≥ 85%
2 Assist
Q1 – Q2 27
$1.4M
$2.1M / yr
AHT and QA held
3 Customer-facing
Q2 – Q4 27
$2.2M
$4.6M / yr
Eval set passed
4 Scale
Q4 27 – Q1 28
$1.1M
$2.4M / yr
Owner in place
What it changes
Gates on evidence rather than dates are what keep a roadmap credible after month three
Every phase has an owner before the board is asked to approve it
Enabling investment is named as enabling, not dressed up with a return
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Operating cadence & governance pack
Sample — the forums, owners, and decision rights for a CX and AI portfolio
Cadence mapRACI per decision typeEscalation path
Each forum has a fixed input, a decision right, and a named chair — so nothing waits for the next meeting that did not need to.
What is in it
Every forum defined by what it decides, not by who attends
The evidence each forum needs, agreed in advance so meetings are not spent building it
Decision rights written down — who decides, who is consulted, who is merely informed
An escalation path with a clock on it, so stalled decisions surface rather than sink
Excerpt — decision right, vendor commitment
Any customer-facing AI vendor commitment above $250k is decided at the
monthly portfolio review, not in procurement. Required evidence: journey
fit against the top five drivers, a reference in the same regulatory
regime, and an exit plan. Absent any of the three, the decision defers
one cycle. It does not default to yes because the quarter is ending.
Sample detail
Forum
Frequency
Decides
Chair
Delivery stand-up
Weekly
Sequencing inside a phase
Delivery lead
Risk forum
Every 2 weeks
Guardrails, launch gates
Risk + CX
Portfolio review
Monthly
Funding, vendors, phase gates
CX exec
Board narrative
Quarterly
Story and ask
Sponsor
What it changes
Most CX programs stall in the gap between forums, not inside them
Writing the decision right down removes the most common source of delay
A clock on escalation makes stalled work visible without anyone having to complain
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Portfolio review & decision log
Sample — a quarter of decisions, with basis and outcome tracked
Living documentBasis recorded per decisionReviewed at each gate
What is in it
Every material decision recorded with the evidence it was made on
Deferred decisions tracked with a reason and a review date, so deferral is visible
Reversals recorded honestly — what changed, and what we learned
A running view of decision throughput, which is the real health metric of a program
Excerpt — entry 41, reversed
Decided M2: build coverage Q&A on the existing knowledge base.
Reversed M4: retrieval accuracy testing returned 62% on coverage
journeys against an 85% gate. Sequence changed — knowledge restructure
moved ahead of the agent. Cost of the reversal: six weeks. Cost of not
reversing: a launched agent that answers coverage questions wrongly.
Sample detail
#
Decision
Basis
Status
38
Fund proactive status first
Driver analysis, repeat rate
Held
41
Knowledge before coverage agent
Retrieval test at 62%
Reversed & resequenced
47
Single CCaaS, no second vendor
Integration debt, exit terms
Held
52
Defer voice containment
Auth cannot complete in-agent
Deferred to Q3
What it changes
A decision log turns a program's memory into an asset rather than folklore
Recording the basis makes reversal cheap and unembarrassing when evidence changes
Throughput of closed decisions predicts delivery better than a status color
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Vendor evaluation scorecard
Sample — three platforms weighted against the top five journeys
Weighted modelJourney fit, not feature countExit terms scored
What is in it
Weighted against your journeys and your regulatory regime, not a generic feature grid
Integration reality scored from a technical session, not from the sales deck
Total cost including the internal effort each option actually consumes
Exit terms and data portability scored explicitly, because renewal leverage is a real asset
Excerpt — scoring note, exit terms
Vendor B scores highest on capability and lowest on exit. Conversation
data is exportable only in a proprietary format, and the model tuned on
your interactions does not travel. On a five-year view that is a larger
cost than the license gap, and it is the reason B does not lead the
recommendation despite the better demo.
Sample detail
Criterion
Weight
Vendor A
Vendor B
Vendor C
Journey fit (top 5)
30%
8.1
7.4
5.9
Integration reality
25%
7.6
7.9
5.2
Governance & audit
20%
8.4
6.1
6.0
Exit & portability
15%
7.2
4.3
6.8
What it changes
Weighting is agreed before the scores are seen, which is what stops the exercise being theater
The best demo and the best five-year decision are frequently not the same platform
Exit terms belong in the scorecard, not in the contract review at the end
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Board narrative pack
Sample — quarterly CX story told in revenue, cost, and risk
12 slidesOne-page summaryFinance-agreed figures
What is in it
The quarter's movement stated in the three terms a board actually weighs
Every figure reconciled with finance before the pack is circulated
What did not work, stated plainly, with what changed as a result
The next ask framed as a gate to pass rather than a date to fund
Excerpt — summary page
Cost to serve is down 14% year on year across the two automated journeys.
Repeat contact on those journeys is down 9%, which is the number that
tells us the reduction came from resolution and not from customers
giving up. Experience index up 6 points. One initiative was stopped this
quarter; the reason and the recovered spend are on page 9.
Sample detail
Measure
Q1
Q2
Movement
Source
Cost to serve
$14.2M
$12.9M
−9.2%
Finance
Repeat contact
24%
21%
−3 pts
Conversation intelligence
Experience index
61
68
+7
VoC program
Open risk items
6
3
−3
Risk forum
What it changes
Boards trust a CX story that includes what was stopped and why
Reconciling with finance before circulation is what makes the numbers survive the room
Repeat contact alongside cost is what proves savings came from resolution
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Intent & containment design
Sample — 46 intents mapped, containment target set per intent
Intent mapContainment target per intentNever-automate list
What is in it
Every intent placed on volume against automation safety, so the argument happens on one picture
A containment target per intent rather than one number for the whole assistant
An explicit never-automate list, agreed with risk before design starts
The utterance variety behind each intent, drawn from real transcripts
Excerpt — never-automate entry
Coverage advice is high volume and will be requested constantly. It is
on the never-automate list because an incorrect answer creates a
regulated advice event, not a bad experience. The agent recognizes the
intent, states plainly that a specialist will answer, and routes with
full context. Recognition without resolution is a valid design.
Sample detail
Intent
Volume share
Target containment
Escalation trigger
Claim status
18%
80%
Dispute language detected
Billing balance
12%
75%
Hardship or dispute
Address change
7%
65%
Failed identity check
Coverage advice
9%
0% — recognize & route
Always
What it changes
A single containment number across an assistant hides the intents where it is dangerous
Recognizing an intent and refusing to resolve it is a designed outcome, not a failure
The never-automate list is agreed with risk before a line is built
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Orchestration blueprint
Sample — agent to systems of record, with degradation paths
Sequence diagramsFailure modes per hopLatency budget
Every hop carries a timeout, a fallback, and a rule for what the customer is told when the system behind it is unavailable.
What is in it
The full path from utterance to system of record and back, hop by hop
What the agent says when a downstream system is slow or down — written, not improvised
A latency budget per hop, so the experience target is a design constraint
Write-path controls: what the agent may change, and what it may only read
Excerpt — degradation rule 3
If the claims system exceeds a 4-second response, the agent does not wait
and does not guess. It tells the customer the claim system is slow right
now, offers a callback or a notification when status changes, and logs a
degraded-path event. Silence and invention are both failures; the
designed behavior is honesty plus an option.
Sample detail
Hop
Purpose
Budget
On failure
Channel → orchestrator
Intent + identity
400ms
Retry once, then human
Orchestrator → CRM
Customer context
600ms
Proceed without context flag
Orchestrator → claims
Status read
4s
Offer notification instead
Orchestrator → KB
Retrieval
800ms
Route to human with transcript
What it changes
Most bad AI service experiences are degradation paths nobody designed
A latency budget turns experience quality into an engineering requirement
Separating read and write paths is the cheapest risk control available
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Escalation & tone playbook
Sample — handoff rules, context payload, and voice under pressure
Handoff specContext payload schemaTone examples
The measure that matters: the customer never states their problem twice.
What is in it
The triggers that force escalation, including emotional and vulnerability signals
The exact context payload the human receives, field by field
How the agent opens after a handoff, so the customer is not restarted
Tone written as examples in the brand's voice, including apology and refusal
Excerpt — tone, refusing without a wall
Wrong: "I am unable to assist with that request."
Right: "I can't advise on what your policy covers — that has to come
from a licensed specialist. I've got your claim number and what you've
told me, and I'm passing both across now so you won't need to repeat
any of it. You're next in line."
Sample detail
Trigger
Detection
Route
Payload
Dispute language
Phrase + sentiment
Specialist queue
Full transcript, claim ID
Vulnerability signal
Phrase + context
Trained agent
Flagged, summary only
Third failed attempt
Turn counter
Any agent
Attempts, what failed
Explicit request
Direct ask
Any agent
Full transcript
What it changes
The handoff is where AI service is won or lost, and it is usually designed last
A context payload schema turns a promise into something QA can verify
Refusing well is a tone problem, not a capability problem
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Evaluation & launch plan
Sample — 340-case evaluation set and the gate criteria for launch
An evaluation set built from real contacts, including the awkward ones
An adversarial subset: manipulation, ambiguity, distress, and out-of-scope pressure
Gate criteria agreed before results exist, so nobody negotiates the bar afterward
The rollout sequence, the rollback trigger, and who is allowed to pull it
Excerpt — gate criteria
Launch requires: resolution accuracy ≥ 90%, correct escalation ≥ 85%,
and 100% refusal on the never-automate set with correct routing. Tone
fit ≥ 80% on human review. Adversarial hold ≥ 75%. Any single gate
missed means no launch — the set is not scored on average, because an
average lets one dangerous category hide behind four safe ones.
Sample detail
Gate
Threshold
Result
Status
Resolution accuracy
≥ 90%
91%
Pass
Correct escalation
≥ 85%
88%
Pass
Never-automate refusal
100%
100%
Pass
Adversarial hold
≥ 75%
79%
Pass
What it changes
Gates agreed before results exist are the only ones that hold under launch pressure
Scoring categories separately stops a dangerous failure hiding inside a good average
A rollback trigger with a named owner is part of the launch, not an afterthought
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Agent assist design spec
Sample — what surfaces to the agent, when, and how it stays out of the way
Assist proposes. The agent disposes. Nothing auto-sends to a customer.
What is in it
Surfacing rules: what appears, at what confidence, and what stays silent
Citation on every suggestion, so agents can verify rather than trust
Dismissal tracked as signal — repeated dismissals are a content defect, not agent resistance
Screen real estate and timing designed with agents, in their actual workflow
Excerpt — surfacing rule 2
Below 0.72 retrieval confidence, assist shows nothing. An empty panel is
better than a plausible wrong answer, because the second one costs the
agent more time to disprove than to look up. Silence is also logged —
the intents where assist stays quiet most often become the knowledge
backlog for the next sprint.
Sample detail
Signal
Threshold
Behavior
Logged as
High confidence
≥ 0.85
Card with citation
Offered
Medium
0.72 – 0.85
Collapsed hint
Offered, low
Low
< 0.72
Nothing surfaces
Knowledge gap
Dismissed 3×
—
Suppress for intent
Content defect
What it changes
The fastest way to lose the floor is a confident wrong suggestion in the first week
Dismissals are the highest-quality knowledge backlog you will ever get, free
Assist that never auto-sends is far easier to get past risk and past agents
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Summarization & disposition standard
Sample — the wrap-up format and the quality bar it has to clear
Format specQuality rubricDownstream contract
What is in it
The summary format, field by field, and what each downstream system needs from it
A quality rubric so "good summary" is a measurable thing rather than a feeling
Edit-rate tracking, because an edited summary is a defect signal
The disposition taxonomy tightened so reporting stops lying quietly
Excerpt — format contract
Every summary carries: what the customer wanted, what was done, what was
promised, and what remains open with an owner. The fourth field is the
one everyone drops and the one every downstream team needs. If nothing
remains open, the field says so explicitly. Blank is not an allowed
value, because blank cannot be distinguished from forgotten.
Sample detail
Field
Required
Consumed by
Quality bar
Customer intent
Yes
Analytics, QA
Matches driver taxonomy
Action taken
Yes
CRM, next agent
Specific, no boilerplate
Commitment made
Yes
Fulfillment
Dated, owned
Open item
Yes
Follow-up queue
Explicit "none" allowed
What it changes
Edit rate is the honest measure of summarization quality; accuracy claims are not
The open-item field is the one that prevents the next avoidable contact
Disposition accuracy is the difference between reporting and guessing
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Automated QA scorecard model
Sample — scored dimensions, calibration, and coaching routing
Quality coverage moves from a 2% sample to 96% of interactions. The remaining 4% are excluded by consent or data-retention rules, and that exclusion is documented rather than quietly dropped.
What is in it
The scored dimensions, each with the evidence the model uses to score it
A calibration protocol against human scorers, run monthly and published
Coaching routed to the supervisor with the moment attached, not a monthly average
An appeal path for agents, because a score without an appeal will not be trusted
Excerpt — calibration note
Monthly calibration scores 60 interactions both ways. Current agreement
with human QA is 0.81 on resolution and 0.62 on empathy. Empathy stays
advisory-only until agreement passes 0.75 — it is reported to
supervisors as a prompt for listening, never as a number attached to an
agent's record.
Sample detail
Dimension
Weight
Human agreement
Use
Resolution achieved
35%
0.81
Scored
Compliance statements
25%
0.94
Scored
Process accuracy
25%
0.79
Scored
Empathy
15%
0.62
Advisory only
What it changes
Publishing the calibration number is what earns the floor's trust in automated QA
Dimensions below agreement threshold stay advisory rather than being quietly scored anyway
Coaching attached to the moment beats a monthly average by a wide margin
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Knowledge restructure plan
Sample — 2,140 articles audited, chunked, owned, and retired
Content auditChunking standardOwnership model
What is in it
Every article audited for accuracy, age, duplication, and contradiction
A chunking and metadata standard written for retrieval rather than for reading
A named owner per content domain, with a review clock that actually fires
A retirement list, because deleting content is the highest-leverage work available
Excerpt — retirement rationale
412 articles are recommended for retirement, of which 96 contradict a
live article. Contradiction is worse than absence: retrieval will return
one of the two at random and the agent has no way to tell which is
current. Retiring these raises measured retrieval accuracy more than
any model change available to us this year.
Sample detail
Domain
Articles
Retire
Rewrite
Owner
Claims
612
148
203
Claims ops
Billing
388
61
84
Finance ops
Policy admin
504
112
167
Policy services
Coverage
296
74
121
Product
What it changes
Contradictory content damages retrieval more than missing content does
Deleting is the highest-return knowledge work and the least likely to be funded
Content without an owner and a review clock returns to the same state within a year
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Driver taxonomy
Sample — three-level taxonomy with definitions two analysts agree on
3 levels, 118 nodesInter-rater testedMapped to journeys
What is in it
A three-level structure: theme, driver, and root cause, each defined in one sentence
Inter-rater testing, because a taxonomy two analysts read differently is not a taxonomy
Every node mapped to a journey stage and to the team that can actually fix it
A change process, so the taxonomy evolves without breaking last quarter's trend
Excerpt — definition, driver 2.4
2.4 Payment failed — customer-side. Contact where a payment did not
complete and the cause sits with the customer's instrument or bank.
Distinguished from 2.5, payment failed — our side, by whether our
system returned an error. If the transcript does not make the cause
clear, classify as 2.6 unresolved, never as a guess between the two.
Sample detail
Level 1
Level 2
Level 3 root cause
Fix owner
Service & status
Claim status unknown
No proactive notification
Claims ops
Money & billing
Payment failed
Customer instrument declined
Billing
Change & admin
Address change
Self-serve path abandoned
Digital
Advice & product
Coverage unclear
Policy wording
Product
What it changes
A taxonomy is only real once two analysts classify the same contact the same way
The unresolved category prevents forced guesses polluting the trend
Mapping each node to a fix owner is what turns insight into scheduled work
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Executive CX dashboard spec
Sample — the Monday view, and what every number on it means
Spec + wireframesMetric definitionsData lineage per tile
What is in it
Every tile defined: the metric, the source, the refresh, and who owns the number
Data lineage documented per tile, so a disputed figure can be traced in minutes
Movement thresholds that trigger a conversation, rather than colors that trigger nothing
A deliberately short dashboard — the tiles that were cut are listed with the reason
Excerpt — tile definition, repeat contact
Repeat contact: the share of resolved contacts followed by another contact
on the same driver within 7 days, same customer. Source: conversation
intelligence, refreshed daily, owned by service ops. Threshold: a 2-point
week-on-week rise opens an item in the Monday review with a named owner.
This is the tile that tells us whether containment is real.
Sample detail
Tile
Definition owner
Refresh
Threshold
Resolution rate
Service ops
Daily
−2 pts w/w
Repeat contact
Service ops
Daily
+2 pts w/w
Cost to serve
Finance
Monthly
Variance > 5%
Experience index
CX
Weekly
−3 pts w/w
What it changes
A dashboard nobody can trace is a dashboard nobody defends in a hard meeting
Thresholds that open an owned item beat colors that open nothing
The tiles you cut matter as much as the ones you keep
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Predictive signal brief
Sample — churn, escalation, and repeat-contact signals with intervention points
3 signalsValidated on holdoutIntervention designed per signal
Escalation signal precision on the holdout period. Deliberately tuned for precision over recall, because an intervention offered wrongly costs more trust than a missed one costs money.
What is in it
Each signal stated with its precision, recall, and the trade-off chosen deliberately
Validation on a holdout period the model never saw, reported honestly
An intervention designed per signal — a score with no intervention is a number, not a capability
Fairness review: which customer groups the signal fires on, and whether that is defensible
Excerpt — intervention design
When the escalation signal fires mid-conversation, the agent sees a
single line: this contact resembles ones that escalated, and the two
factors driving it. No score is shown. Agents given a number manage the
number; agents given the factors manage the customer. Supervisor alerting
fires only above 0.85, to protect the floor from alert fatigue.
Sample detail
Signal
Precision
Recall
Intervention
Escalation risk
0.73
0.51
Agent prompt, factors only
Repeat contact
0.68
0.62
Proactive notification
Churn risk (90d)
0.61
0.44
Retention queue, human
Vulnerability flag
0.79
0.38
Trained agent routing
What it changes
Precision over recall is usually the right trade in customer-facing intervention
Showing agents factors rather than scores changes behavior in the right direction
A signal without a designed intervention never survives its second quarter
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Insight-to-action rhythm
Sample — the weekly loop that turns a finding into owned, dated work
Weekly loopOwner per findingAging tracked
What is in it
A fixed weekly loop with a named chair, so insight does not accumulate unread
Every finding leaves the room with an owner and a date, or it is closed explicitly
Aging tracked, because the honest failure mode is findings that quietly get old
Movement checked against the same measure that raised the finding
Excerpt — closing rule
A finding may leave the review in one of two states: owned with a date,
or closed with a reason. There is no third state. "Noted" is how insight
programs die — the analysis keeps being produced, nothing changes, and
within two quarters nobody attends. The chair is accountable for the
absence of a third state, not for the fix.
Sample detail
Week
Findings raised
Owned
Closed
Aged > 30d
W7
9
6
3
1
W8
11
8
3
1
W9
7
5
2
0
W10
8
6
2
0
What it changes
Insight programs die of "noted," not of bad analysis
Two exit states only is the simplest discipline that keeps the loop alive
Aging is the leading indicator of an insight program losing its audience
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Responsible-use policy
Sample — boundaries, owners, and evidence for customer-facing AI
18 pagesMapped to existing frameworksSigned by risk and legal
Every boundary in the policy carries all four. A boundary without an owner is a sentence, not a control.
What is in it
Boundaries written for your regulatory regime rather than lifted from a template
Each boundary mapped to an enforcement mechanism and the log that evidences it
Named owners, so the policy answers "who" and not only "what"
Mapped against your existing model risk and third-party frameworks to avoid a parallel regime
Excerpt — boundary 4.2
The assistant must not state or imply what a policy covers. Enforcement:
coverage intents are on the never-automate list and route on recognition.
Evidence: every coverage-intent contact logs the recognition event and
the route taken, retained 7 years. Owner: Head of Product Compliance.
Breach of 4.2 is a launch-blocking defect, not a backlog item.
Sample detail
Boundary
Enforcement
Evidence
Owner
No coverage advice
Never-automate routing
Intent + route log
Product compliance
No adverse decisions
Read-only claim path
Write-path audit
Claims risk
Identity before change
Auth gate in orchestrator
Auth event log
Security
Disclosure of AI
Opening turn script
Transcript sample QA
CX
What it changes
A boundary without an owner and a log is a sentence, not a control
Mapping to existing frameworks avoids creating a parallel regime nobody maintains
Naming which breaches block launch is what makes the policy operational
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Evaluation set & red-team report
Sample — what it failed, what changed, and what remains open
Adversarial cases written against your specific boundaries, not generic jailbreaks
Failures reported in full, including the ones that are uncomfortable to circulate
Each failure traced to a cause: model, knowledge, orchestration, or design
Retest evidence after remediation, with the residual risk stated plainly
Excerpt — finding R-14, open
Under contradictory knowledge the assistant selected the stale article in
36% of trials and answered confidently. Cause: knowledge, not model. No
prompt change fixes this. Remediation is the retirement of 96
contradictory articles, tracked in the knowledge plan. Until that lands,
the affected intents stay off the launch scope. Residual risk: accepted.
Sample detail
Category
Cases
Pass
Cause of failures
Status
Prompt manipulation
48
88%
Design — refusal wording
Remediated
Ambiguous identity
34
71%
Orchestration — auth gate
Remediated
Distress handling
40
83%
Design — routing trigger
Remediated
Contradictory knowledge
36
64%
Knowledge — stale content
Open
What it changes
Tracing each failure to a cause tells you whether a prompt change can ever fix it
Publishing failures is what makes the passes believable
An open finding with scope removed is a defensible position; a hidden one is not
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Human-in-the-loop design
Sample — where a person reviews, approves, or overrides, and how fast
Decision mapReview SLAsOverride logging
What is in it
Every automated path placed on impact against required human involvement
Three modes defined precisely: monitor, review before effect, and approve each time
Review SLAs, because a checkpoint without a clock becomes a queue and then a rubber stamp
Override logging, so disagreement between human and model becomes training signal
Excerpt — mode definition
Review-before-effect means the customer sees nothing until a person
approves. It is not "a human can check later." If the review queue
exceeds its 15-minute SLA for more than an hour, the path degrades to
human-only rather than auto-approving. A checkpoint that auto-approves
under load is a checkpoint that exists only on the diagram.
Sample detail
Path
Mode
SLA
Override rate
Status read
Monitor
—
0.4%
Address change
Monitor + sample
Daily
1.1%
Payment plan offer
Review before effect
15 min
6.8%
Claim decision
Approve each time
4 hrs
11.2%
What it changes
A checkpoint that auto-approves under load is not a checkpoint
Override rate is the most useful signal you will get about where the model is weak
Naming the three modes precisely prevents "human oversight" meaning nothing
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Enablement & adoption plan
Sample — role changes, training, and what adoption is measured on
Role mapsTraining planAdoption measures
What is in it
What each role becomes, written honestly, including the parts of the job that go away
Training built around the new judgment calls rather than around the interface
Adoption measured on the floor — usage in real contacts, not training completion
A feedback path from agents that visibly changes the product, or adoption stalls at week three
Excerpt — communication principle
Say what changes about the job before saying what the tool does. Agents
are told directly: wrap-up largely disappears, quality coverage becomes
complete rather than sampled, and the work shifts toward the contacts
that need judgment. Headcount intent is stated by leadership in the same
session. Uncertainty left unaddressed is filled in with the worst case.
Sample detail
Role
What changes
Training
Adoption measure
Agent
Wrap-up automated, harder mix
4 hrs + floor coaching
Assist used in-contact
Supervisor
Coaching from moments
6 hrs
Coaching sessions logged
QA analyst
Sampling → calibration
8 hrs
Calibration cadence held
Knowledge owner
New review clock
3 hrs
Review SLA met
What it changes
Adoption is a communication problem before it is a training problem
Measuring usage in real contacts is the only adoption number worth reporting
Stating headcount intent early costs less than the rumor that fills the silence
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Experience strategy on a page
Sample — regional bank, retail and small business segments
1 page, plus a 20-page evidence appendixSigned by the executive teamReviewed twice a year
What is in it
The experience ambition in one sentence the executive team will repeat unprompted
The two or three journeys that earn investment, with the evidence for choosing them
An explicit not-this list — the journeys deliberately left at parity, and why
The measures that will be reported against the ambition, agreed before any work starts
Excerpt — the choice
Everyday transactions carry the most customers and almost no influence on
whether they stay. We will hold that journey at competitive parity and stop
funding improvements to it. Servicing a problem affects fewer customers and
decides most of the attrition we can address. It gets the investment, and
it gets the senior owner. This is the trade the strategy is making.
Sample detail
Journey
Customers affected
Influence on retention
Position
Owner
Servicing a problem
High
Highest
Invest to lead
Chief Customer Officer
Onboarding
High
High
Invest to lead
Head of Digital
Closing an account
Low
High
Fix the basics
Service Operations
Everyday transactions
Highest
Low
Hold at parity
Head of Digital
What it changes
The not-this list is what makes the strategy a strategy rather than a list of intentions
Positions are stated per journey, so funding arguments have a reference point
Every invested journey leaves with an owner senior enough to hold it
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Target operating model
Sample — where the customer outcome is owned across four functions
Current and target statesRole charters per boxTransition plan attached
The model names an owner for the customer outcome, not only for each channel.
What is in it
The target model drawn against journeys rather than against the current org chart
Role charters — what each function decides, contributes, and is held to
Where AI capability sits, who owns model behavior, and who owns the customer promise
The transition path from where you are, including what has to stop
Excerpt — the diagnosis
Every channel has an owner. No one owns the journey that crosses them.
The result is four teams optimizing four handoffs and a customer
experiencing one broken path. The target model introduces a journey
owner with budget authority and moves channel leads into delivery
against that owner's standard. This is the change; the rest is detail.
Sample detail
Function
Decides
Contributes
Held to
Journey owner
Experience standard, sequence, trade-offs
Budget and priority
Journey outcome measure
Service operations
Staffing, floor process, escalation
Operational feasibility
Resolution and cost to serve
Digital & product
Interface, release sequence
Build capacity
Adoption and task completion
Data & AI
Model behavior, evaluation, guardrails
Evidence and tooling
Accuracy, safety, and drift
What it changes
Owning the journey is a different job from owning a channel, and needs a different charter
Model behavior and the customer promise are owned separately and deliberately
The transition names what stops, which is the part most operating models leave out
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Decision rights & funding map
Sample — six recurring CX decisions, mapped to forum, evidence, and money
6 decision typesForum, cadence, and evidence per rowAdopted into the governance calendar
What is in it
Every recurring CX decision mapped to the forum that owns it and the cadence it runs on
The evidence each decision requires, so meetings stop being opinion contests
The funding route attached to each decision type, including who can commit what
Escalation paths for the decisions that genuinely cannot be made at that level
Excerpt — decision 03, release gate
Owned by the journey owner, not by the delivery lead and not by risk.
Requires: evaluation results against the agreed set, the guardrail sign-off,
and the rollback plan. Risk holds a veto on safety grounds only, exercised
in the forum rather than by email afterward. If evidence is missing the gate
does not convene. Deferral is a decision and gets recorded as one.
Sample detail
Decision
Owner
Forum
Evidence required
Money
What we fund next
Executive committee
Quarterly portfolio
Ranked backlog with modeled return
Portfolio budget
Whether it ships
Journey owner
Release gate
Evaluation set, guardrails, rollback
None
Whether we buy
CIO with journey owner
Quarterly portfolio
Weighted scorecard against journeys
Capital
Whether to stop
Journey owner
Monthly journey review
Baseline movement against target
Releases budget
What it changes
Naming who decides removes most of the delay people attribute to complexity
Requiring evidence before a forum convenes is what stops the meeting being reopened
Recording deferral as a decision makes drift visible in the minutes
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
CX value case
Sample — three-year model, assumptions exposed and editable
XLSX modelAssumptions on their own sheetReviewed with finance before issue
What is in it
Value split into cost to serve, retained revenue, and avoided cost, each modeled separately
Every assumption on its own sheet, sourced and editable, so finance can argue with the inputs
A conservative, expected, and optimistic case, with the conservative case used for approval
The measurement plan that will prove or disprove the model once work is live
Excerpt — assumption note 07
Retention benefit is modeled only on the two journeys the strategy invests in,
and only on the segment where we have attrition evidence. Applying the same
uplift across the base would roughly triple the headline number and would not
survive finance review. The conservative case is the one submitted; the
optimistic case exists to show what upside looks like, not to be approved.
Sample detail
Line
Year 1
Year 2
Year 3
Basis
Cost to serve reduction
$1.4M
$3.2M
$4.1M
Contact removed × loaded cost
Retained revenue
$0.9M
$2.8M
$4.6M
Attrition delta on invested journeys
Avoided build
$0.6M
—
—
Duplicate programs consolidated
Investment
($2.1M)
($1.6M)
($1.1M)
Delivery, platform, and change
What it changes
Exposing assumptions is what lets finance own the number rather than merely receive it
Approving the conservative case is why the program keeps its credibility in year two
The measurement plan is part of the case, so the model can be proved wrong on schedule
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Opportunity portfolio
Sample — 14 framed opportunities, top 6 shown
Framed as testable betsScored before any buildRefreshed each cycle
What is in it
Each opportunity framed as a bet with a stated hypothesis, not as a feature request
Scored on customer value, differentiation, and feasibility, with the reasoning attached
The success criteria written before the sprint, so the result cannot be reinterpreted later
A parked list with the condition that would make each one worth revisiting
Excerpt — opportunity 02, hypothesis
If customers capturing a claim are guided through evidence collection at the
moment of the incident, then first-time-complete submissions rise above 70%
and downstream chase contact falls by at least a quarter. We will test with
20 recent claimants on a working prototype. Below 55% first-time-complete we
stop; the concept depends on customers doing this at the roadside, not later.
Sample detail
#
Opportunity
Customer value
Differentiation
Feasibility
Call
1
Proactive disruption care
High
High
Med
Sprint 1
2
Guided claim capture
High
Med
High
Sprint 1
3
Advisor co-pilot handoff
Med
Med
High
Sprint 2
5
Voice concierge
Med
Low
Low
Parked — revisit after knowledge work
What it changes
A hypothesis with a stop condition is what separates a bet from a wish
Scoring before the build is what stops the loudest idea absorbing the cycle
The parked list keeps good ideas alive without letting them block the sprint
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Working prototypes
Sample — sprint 1 output, two concepts built to test
Clickable and live-model buildsInstrumented for the testThrown away without regret
Prototypes are built to answer one question, then discarded — they are evidence, not assets.
What is in it
A prototype real enough to test the risky part, and deliberately unfinished everywhere else
Live model behavior where the concept depends on it, because a scripted demo proves nothing
Instrumentation for the specific criteria agreed at framing, not general analytics
An honest note on what the prototype fakes, so nobody mistakes it for a build estimate
Excerpt — what this prototype fakes
Policy lookup is stubbed against six fixed records. Authentication is skipped
entirely. The model is live and unscripted, because the question is whether
customers trust its answers — everything else is set dressing. Do not read
sprint duration as a build estimate: the parts we faked are the parts that
take the engineering time.
Sample detail
Concept
Built
What is real
What is faked
Test question
Guided claim capture
Mobile prototype
Capture flow, model prompts
Policy data, auth
First-time-complete rate
Proactive disruption care
Notification + rebook flow
Decision logic, copy
Inventory, payments
Do customers stop calling
What it changes
Building only the risky part is what makes a two-week answer possible
Naming what is faked protects the estimate conversation that always follows a good demo
Prototypes are evidence with a short shelf life, and are not carried into delivery
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Customer test findings
Sample — 20 participants, two concepts, criteria set at framing
Behavioral, not attitudinalCriteria agreed in advanceRecorded sessions available
What is in it
What participants actually did, measured against the criteria set before the build
Where they hesitated, backed out, or went looking for a human, with the session timestamps
Verbatims kept separate from behavior, because stated preference is the weaker evidence
A clear read against each stop condition, stated before the recommendation
Excerpt — finding 02, trust
Task completion met target at 72%. Trust did not: 41% against a 65% criterion.
Participants completed the task and then rang to confirm the outcome, which is
completion without benefit. The failure is not accuracy — the answers were
right. It is that nothing in the experience told them the claim was actually
logged. That is a design problem, and it is a cheap one to test again.
Sample detail
Criterion
Target
Result
Read
Completed unaided
70%
72%
Met
Trusted the answer
65%
41%
Failed — stop condition
Would use again
60%
68%
Met
Called anyway afterward
<25%
22%
Met, but sits alongside the trust result
What it changes
Behavior is scored first; verbatims explain the behavior rather than replace it
A failed criterion is reported as a failure even when the concept is well liked
Naming the failure precisely is what makes the next sprint cheap
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Scale-or-stop pack
Sample — end of cycle, four concepts, one recommendation each
One page per conceptCost to proceed attachedTaken to the funding forum
What is in it
A single recommendation per concept, with the evidence that supports it on the same page
What it costs to take the concept further, sized well enough to fund or decline
The stop cases written up properly, so the idea does not quietly return next year
What the cycle taught that applies beyond the concept tested
Excerpt — concept 04, recommendation
Stop. The concept tested well on usability and failed on the only criterion
that mattered, twice. The underlying need is real and is better served by the
notification work already in the roadmap. Recorded as stopped on evidence,
with the test data attached, so the next person to propose it starts from
here rather than from the beginning.
Sample detail
Concept
Recommendation
Evidence
Cost to proceed
Guided claim capture
Iterate once
Completion met, trust failed
One further sprint
Proactive disruption care
Scale
All criteria met, strong repeat intent
Delivery estimate attached
Advisor co-pilot handoff
Merge
Overlaps agentic design workstream
Absorbed
Voice concierge
Stop
Failed trust criterion twice
—
What it changes
Every concept leaves the cycle with a decision, which is the point of running one
Writing up the stops properly is what stops the portfolio silently re-growing
The cycle's transferable lessons usually outlive the concepts that produced them
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Behavioral and operational evidenceSized in cost and attritionBaseline for everything after
What is in it
Effort and contact volume plotted across the journey rather than averaged over it
The stages where customers leave, sized in retained revenue instead of in satisfaction points
Root cause traced upstream, separating demand the journey creates from demand it merely receives
A baseline recorded per stage, so later movement can be attributed rather than claimed
Excerpt — finding 01
Effort and contact both peak between assessment and decision, and that is
also where attrition concentrates. The organization has been investing in
settlement speed, which sits after the peak. Customers are not leaving
because settlement is slow. They are leaving because for eleven days nobody
tells them anything, and they have to ask.
Sample detail
Stage
Contacts per claim
Effort
Attrition index
Demand origin
Notify
1.2
Moderate
Low
Journey creates it
Assess
2.6
High
Elevated
Silence upstream
Decision
3.1
Highest
Highest
Silence upstream
Repair
1.4
Moderate
Low
Third party
What it changes
Averaging across a journey hides the stage that is actually doing the damage
Separating created from received demand decides whether to automate or to fix upstream
The baseline is taken before design starts, which is the only time it is credible
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Signature moment designs
Sample — three moments designed end to end
Designed across channelsHuman and AI roles decidedWritten to be built from
A moment is designed once and delivered consistently across every channel it touches.
What is in it
The few moments that carry the loyalty weight, designed rather than mapped
The promise each moment makes, written in language a customer would recognize
Human and AI roles assigned deliberately, including where a person is required regardless of cost
The proof point — how the customer knows the promise was kept, which is where most designs fail
Excerpt — moment 02, the wait after assessment
Promise: you will never have to ask us what is happening. Delivery: automated
status at every state change plus a scheduled human call at day five whether
or not there is news. The day-five call is the expensive part and it is not
negotiable — the evidence says the silence, not the wait, is what breaks
trust. Proof: the customer can see what happens next and when, at all times.
Sample detail
Moment
Promise
AI does
A person does
Proof to the customer
First notification
This will be easy to start
Guided capture, evidence checks
Nothing routine
Immediate confirmation with a reference
The wait after assessment
You never have to ask
Status at every change
Day-five call regardless
Visible next step and date
The decision
You will understand why
Draft the explanation
Deliver anything contested
Plain-language reasoning
What it changes
Naming the promise is what makes the moment testable rather than aspirational
Deciding where a human is required regardless of cost is a strategy decision, not an ops one
Most experience failures are proof failures: the thing was done and nobody told the customer
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Service blueprint
Sample — everything behind one signature moment
Frontstage and backstageSystems, data, teams, policyHanded to delivery teams
What is in it
Frontstage and backstage on one page, so the customer view and the operational view stay tied
Every system, data flow, and team the moment depends on, including the ones nobody owns yet
The policy changes required, named explicitly, because those are what stall reinvention
Failure modes and what the customer experiences when each dependency is unavailable
Excerpt — dependency note, policy
The day-five call cannot be delivered inside the current contact model: advisor
capacity is scheduled against inbound volume only. This is a policy and
workforce-planning change, not a technology one, and it is the single
dependency most likely to quietly remove this moment from the design during
delivery. It is listed first for that reason.
Sample detail
Dependency
Type
Owner
Status
If unavailable
Claim state events
Data
Claims platform
Exists, not exposed
No proactive status at all
Notification service
System
Digital
Build
Falls back to email only
Day-five outbound call
Policy & workforce
Service operations
Not agreed
Moment loses its human proof
Plain-language templates
Content
Service enablement
Rewrite
Explanation stays unreadable
What it changes
Listing the policy dependency first is deliberate: it is the one that gets dropped
Describing what the customer experiences on failure turns a risk register into a design input
The blueprint is written to be built from, not to be presented
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
Sample deliverable
Reinvention roadmap
Sample — sequenced releases with baselines attached
Release-level sequenceOne measure per releaseBaseline taken before each
What is in it
Releases sequenced by dependency, so each one makes the next cheaper rather than harder
A single measure per release, with its baseline recorded before the release ships
The upstream fixes sequenced ahead of the downstream automation they would otherwise mask
Explicit re-baselining points, so year-two claims stay honest
Excerpt — sequencing note
Guided capture is the most visible release and it is scheduled fifth. Shipping
it first would improve a stage that is not where value leaks, and would consume
the delivery capacity the notification work needs. The sequence follows the
diagnostic, not the demo appeal. This will be unpopular in month one and
obvious by month six.
Sample detail
Release
Depends on
Measure
Baseline
Status events exposed
—
Events available per claim
0% exposed
Proactive notification
Status events
Contacts per claim, assess stage
2.6
Plain-language decision
Template rewrite
Decision-stage repeat contact
3.1
Day-five call pilot
Policy change
Attrition index, decision stage
Index 100
What it changes
Sequencing by dependency is what stops the roadmap being reordered by whoever presents best
One measure per release keeps attribution possible when several things ship in a quarter
Re-baselining on schedule is how a program keeps its numbers credible into year two
Illustrative sample. Composite of common engagement patterns — not the work of any client, and every figure is representative rather than reported.
