From “we should do AI” to a
roadmap your board can fund.

A short, structured engagement that inventories every AI opportunity in your business, scores each one by ROI against feasibility, and hands you a costed, sequenced 12-month roadmap. Fixed scope and vendor-neutral, including a written list of what not to build.

Scope your discovery ↗See how pilots get built

5 wks

typical discovery, kickoff to funded roadmap

35+

AI roadmaps delivered across seven industries

1 in 3

proposed use cases we advise against building

CRM health audit

20-point review

COMPLETE

Data quality

3,400 duplicate records found

CRITICAL

Automations

12 broken flows firing silently

WARN

✓

Security

Roles follow least-privilege

PASS

VERDICTcritical: 1 · warnings: 1 · passing: 18

checkpoints scored

quick wins found

30 d

fix roadmap

Leadership teams planning AI with Encloud

BLUESUMMIT✚CaremontmeridianStratosNORTHBRIDGE

AI Discovery, Strategy & Roadmap

Challenges Why Encloud Services Process Outcomes Stack

Book a call ↗

Everyone has AI pressure.
Almost nobody has a plan.

Four patterns show up in nearly every leadership team we sit down with. A structured discovery is the cheapest way out of all of them.

01MANDATE

The board wants an AI strategy by next quarter

Someone senior asked what the AI plan is, and the honest answer is a slide of ideas nobody has costed. Under deadline pressure, teams fund whichever demo looked best last week, and call that a strategy.

02SCATTER

Pilots everywhere, production nowhere

Marketing tried a chatbot, ops has a ChatGPT subscription, an intern built a prototype. Each experiment taught someone something, but none were scored against each other, so budget scatters and nothing compounds.

03DATA

The roadmap was written before anyone checked the data

Most AI plans assume the data exists, is clean and can legally be used. Then the build starts and the model meets reality: half-empty CRM fields, six versions of every customer, and consent nobody collected.

04VENDORS

Every vendor's answer is their own product

Ask a platform vendor what to do and the roadmap ends at their pricing page. Without an independent build-vs-buy view, you either overpay for shelfware or hand-build something $500 a month would have solved.

Our fix: score every idea before anyone builds.

We build AI systems for a living, call intelligence, agents, predictive models, so the roadmap is written by people who know what shipping actually costs. That is also why we can tell you, in writing, which ideas are not worth it.

Pressure-test your AI plan →

Start from the P&L, not the demo

Use cases come from where your business bleeds time and margin, quote turnaround, claim triage, churn, manual re-keying, not from whatever a model demo made look easy. Every candidate is sized in dollars before it earns a place on the list.

Use-case inventoryValue sizingProcess walkthroughs

ROI × feasibility, on one scorecard

Each use case gets scored on expected return and on how hard it really is, data availability, integration surface, model maturity, change cost. The result is a ranked matrix and an explicit kill list, not a wishlist.

Scoring matrixKill listEffort estimates

Check the data before promising the model

A data-readiness assessment runs alongside the workshops: source inventory, field-level quality sampling, lineage, access and consent. If a use case needs data you do not have, the roadmap says so, and prices the fix.

Data-readiness scorecardQuality samplingGap remediation plan

Vendor-honest build-vs-buy

We resell no licenses and take no platform commissions. For every shortlisted use case you get a build-vs-buy memo with three-year TCO on each path, and "buy the $6K tool" is a recommendation we make often.

Build-vs-buy memos3-year TCO modelsNo resale agenda

What a discovery covers, end to end

All AI & data services

Use-Case Inventory & Prioritization

Cross-team workshopsScoring matrixRanked shortlistKill list

Data-Readiness Assessment

Source inventoryQuality samplingAccess & consent reviewGap plan

Build-vs-Buy Analysis

Vendor shortlistsTCO comparisonLock-in review

Risk, Compliance & Governance

Risk registerAI usage policy draftHuman-in-the-loop rules

Quick-Win Pilot Definition

Pilot briefsSuccess metricsGo/no-go criteria

Executive Alignment Workshop

Exec briefing deckDecision frameworkBoard-ready summary

Platform & Architecture Direction

Reference architectureModel shortlistIntegration map

12-Month Roadmap & Budget

Sequenced quartersBudget bandsHiring planDependency map

How a discovery runs at Encloud

Five stages inside a fixed window, each with named deliverables. Hover a stage to see what you get.

/ 05

Immerse

01Learn how the business actually runs

Stakeholder interviews across sales, ops, service and finance, plus walkthroughs of the systems where work really happens. Out of it comes a long-list of AI candidates tied to specific processes and their cost.

Opportunity long-listSystems & data mapInterview findings

02Test the data against the ideas

For every serious candidate we sample the underlying data, completeness, accuracy, lineage, consent, and check the integration surface. Ideas that need data you do not have get a remediation price, not silence.

Data-readiness scorecardFeasibility notesRemediation estimates

03Rank ruthlessly, kill openly

A working session where the long-list meets the scorecard: ROI against feasibility, argued in the open with your leaders in the room. You leave with a ranked shortlist and a kill list everyone saw the reasoning behind.

Scored use-case matrixKill list with rationaleShortlist of 3-5

04Settle build-vs-buy and risk

Each shortlisted use case gets a build-vs-buy memo with three-year TCO, a risk and compliance review, and a recommended path. The exec alignment workshop closes this stage with a funded direction.

Build-vs-buy memosRisk register & policy draftExec alignment session

05Sequence the year, brief the pilots

The final artifact: a 12-month roadmap sequenced into quarters with budget bands, hiring implications, a reference architecture, and two or three quick-win pilots scoped to start the week after sign-off.

12-month costed roadmapPilot briefsReference architecture

Discovery outcomes in spotlight

All case studies

←

→

27 AI ideas scored, 3 funded, approved in one board meeting

Financial ServicesUse-Case Prioritization

BLUESUMMIT

3 of 27use cases funded, rest killed with rationale

View case study ↗

“We walked into the board with a scored matrix instead of a vision slide. Approval took one meeting because every number had working behind it.”

James Whitaker

Director of Risk, BlueSummit

Consent gaps caught in discovery, not in production

HealthcareData Readiness & Risk

✚Caremont

14data and consent gaps closed before any build

View case study ↗

“They told us our flagship chatbot idea was a compliance incident waiting to happen, and showed us the two use cases that were actually safe to ship first.”

Hannah Leigh

Patient Services Lead, Caremont Health

First quick-win pilot live nine weeks after roadmap sign-off

ManufacturingQuick-Win Pilots

meridian

9 wksfrom sign-off to first pilot in production

View case study ↗

“The pilot brief was so specific, data source, owner, success number, that the build barely needed a kickoff. It paid for the whole discovery inside the quarter.”

Daniel Okafor

COO, Meridian Manufacturing

Build-vs-buy memo killed a custom build a vendor tool covered

B2B SaaSBuild-vs-Buy

Stratos

$240Kengineering spend avoided in year one

View case study ↗

“An engineering firm told us not to build. That was the moment we trusted the rest of the roadmap.”

Marcus Hale

VP Sales, Stratos

Five departments, one AI budget, zero turf wars

LogisticsExecutive Alignment

NORTHBRIDGE

1shared roadmap replacing five departmental wishlists

View case study ↗

“Every VP arrived with their own AI pitch. The scoring workshop settled it with numbers instead of politics, and everyone saw why their idea ranked where it did.”

Elena Vasquez

RevOps Lead, Northbridge Logistics

Put engineers on the strategy, not a slide factory.

Your discovery is run by a solutions architect, a data engineer and a delivery lead who build AI systems the rest of the year. The roadmap is priced by the people who would have to ship it.

5 wks

Fixed discovery window, kickoff to signed roadmap

Licenses we resell, recommendations stay independent

Quick-win pilots scoped and budgeted in every roadmap

What we assess with, and what we assess

The discovery evaluates your stack as it is, and shortlists the models and platforms your roadmap should actually bet on.

Model candidates

Data foundations

Systems we inventory

Pilot delivery

Evaluation & measurement

The families we benchmark per use case, hosted, open-weight or fine-tuned, chosen by fit and unit cost.

Claude

OpenAI

LLlama

Hugging Face

Book a strategy session, not a sales pitch.

45 minutes with a solutions architect. Bring your idea list, your board deadline, or nothing at all, leave with our scoring template, an honest read on your data readiness, and a fixed quote for the full discovery.

✓No obligation, no prepared pitch

✓NDA on request before you share anything

✓Vendor-neutral, we resell no licenses

✓Fixed scope and fixed price, agreed up front

4.9 / 5average across 35+ strategy engagements

“Five weeks, one document, and suddenly the AI conversation at board level had numbers in it. The kill list was worth the fee by itself.”

James Whitaker

Director of Risk, BlueSummit

Tell us where AI is on your agenda

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We reply within one business day. Your details never leave Encloud.

Frequently asked questions

Deciding between a discovery, a bigger consultancy, or just building something? Bring the question to a strategy session and get an answer backed by your own data.

Talk to a solutions architect →

How long does an AI discovery engagement take?+

Four to six weeks from kickoff to signed roadmap, with five weeks typical. The window is fixed at scoping: we would rather rank your top use cases decisively than inventory every idea forever. Larger organizations with many departments sometimes run a second, deeper pass on one business unit afterward.

What exactly do we get at the end?+

A scored use-case matrix with an explicit kill list, a data-readiness scorecard, build-vs-buy memos with three-year TCO for each shortlisted use case, a risk register with a draft AI usage policy, two or three quick-win pilot briefs, and a 12-month roadmap sequenced into quarters with budget bands. Everything is yours to execute with us, with another vendor, or in-house.

What does "data readiness" actually mean?+

Whether the data a specific use case needs exists, is complete and accurate enough at the field level, can be accessed without heroics, and can legally be used for that purpose. We sample real records rather than trusting system diagrams, a CRM that is 40% empty on the fields a model needs is the most common finding in our AI readiness assessments.

Do we need a data warehouse before we can do anything with AI?+

Usually not. Plenty of high-ROI use cases, document processing, call summarization, drafting assistance, run against operational systems you already have. A warehouse like Snowflake or BigQuery earns its place when use cases need history joined across systems, and if yours do, the roadmap sequences that work with a price rather than assuming it away.

How do you estimate ROI before anything is built?+

From your numbers, not industry benchmarks: hours spent on the process today, loaded labor cost, error and rework rates, revenue at stake. We model conservative, expected and optimistic cases per use case and state every assumption, so finance can challenge the working instead of taking a vendor slide on faith.

What happens after the roadmap is delivered?+

Whatever you choose. The pilot briefs are written so any competent team can execute them. Many clients have our engineering pods build the first quick wins, that is our AI PoC & MVP and custom LLM integration work, but the discovery is priced to stand alone, and the roadmap never assumes you hire us for the build.

How is this different from a big-consultancy AI strategy?+

It is written by engineers who will quote you a price to build each item, which changes what gets recommended: no 200-page decks, no transformation programs designed to sell the next engagement. Every roadmap line has an effort estimate someone here is prepared to stand behind, and a kill list is a standard deliverable, not a diplomatic omission.

What does an AI discovery cost?+

A fixed price agreed before kickoff, scaled to company size and the number of departments in scope, no day rates, no change orders. For most mid-market companies it costs meaningfully less than one quarter of one misdirected AI hire, which is precisely the mistake it exists to prevent.