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How We Work

AI readiness assessment and data maturity assessment

An AI readiness assessment and data maturity assessment that shows leadership what to fix, in what order, before investing in AI tools or projects.

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Process readiness is strong; customer data is the blockerRadar chart of five readiness dimensions on a 1 to 5 scale: process 4, people 3, analytics 3, governance 2 and data 1, the blocker (demo data).Process readiness is strong; customer data is the blockerReadiness score, 1 to 5, by dimensionDemo dataData 1Analytics 3Governance 2Process 4People 3Blocker: customer dataProcess readiness is strong; customer data is the blockerMobile view: readiness scores, process 4, analytics 3, people 3, governance 2 and data 1, the blocker (demo data).Process readiness is strong;customer data is the blockerReadiness score, 1 to 5, by dimensionDemo dataProcess4Analytics3People3Governance2Data112345Blocker: customer data

Our AI readiness assessment is a fixed-scope engagement. It scores your data, processes, technology and people against the AI uses you are actually considering. It replaces general enthusiasm, or general doubt, with evidence. You see what is ready now, what is close and what would fail without groundwork first.

You receive a readiness scorecard, a ranked list of blockers and a practical roadmap with named owners. Leadership gets a clear go or no-go for each priority use case. The CFO gets the business case inputs needed to judge expected return before any budget is committed.

The assessment is one of two entry points to working with us, alongside strategic advisory. It is vendor-neutral, and we do not resell AI software, so the findings reflect your data and your goals. If they show that implementation is premature, we say so.

Challenges we solve

AI projects fail on missing groundwork, not on weak technology

When AI projects fail, the cause is rarely weak technology. They fail on basics that were never checked.

Six layers of groundwork sit under every AI pilotA pilot sits on six stacked layers: business case, trained staff, access rules, documented processes, consistent records and connected data.Six layers of groundwork sit under every AI pilotWhat a pilot needs underneath before it can scaleAI pilotBusiness caseWhich decision, how success is measuredTrained staffSafe, confident use of AI toolsAccess and privacy rulesAgreed before security reviewDocumented processesA stable baseline to automateConsistent recordsOne definition, one ownerConnected dataCustomer, product and finance systems linkedThe assessmentchecks everylayerSix layers of groundwork sit under every AI pilotMobile view: A pilot sits on six stacked layers: business case, trained staff, access rules, documented processes, consistent records and connected data.Six layers of groundwork sit underevery AI pilotWhat a pilot needs underneath before it can scaleAI pilotBusiness caseWhich decision, how success is measuredTrained staffSafe, confident use of AI toolsAccess and privacy rulesAgreed before security reviewDocumented processesA stable baseline to automateConsistent recordsOne definition, one ownerConnected dataCustomer, product and finance systems linkedThe assessment checks every layer

Customer, product and financial data sits in systems that do not connect. So an AI assistant cannot see the full picture. Records are duplicated, incomplete or defined differently by each department, and nobody owns the fix.

The processes AI is meant to improve are undocumented or vary by team. There is no stable baseline to automate or measure against. Rules on data access and privacy are unclear, which stalls security and legal reviews late in the project. Staff have not been trained to use AI tools safely, so adoption stays low even when a pilot works.

And often the business case is missing. Leadership approves a pilot because competitors are moving, without agreeing which decision it should improve or how success will be measured. An assessment finds these blockers while they are still cheap to fix.

What you get

Five dimensions show exactly where your organization is ready for AI

The assessment covers five dimensions: data readiness, analytics maturity, governance, process and people. Each is scored on evidence from your systems, documents and interviews. We do not rely on self-reported surveys alone. That way the results can stand up to challenge from leadership.

  • Know if your data can support AI

    Data readiness tests quality, completeness and integration against real records for each use case.

  • See if decisions already run on data

    Analytics maturity reviews how reports, dashboards and KPIs are built and used today.

  • Approve AI use safely

    Governance and security covers owners, definitions, tool access, privacy and usage policies.

  • Pick processes AI can actually improve

    Process readiness checks that target workflows are documented, stable and measurable.

  • Prepare people to adopt it

    People and talent reviews leadership alignment, team capacity and training needs.

Data maturity self-check

Quick check: how ready is your organization for AI?

7 questions on common data practices. The result is an illustrative level, not a benchmark score.

Illustrative
1. Have you named the AI use cases you want to fund?
2. Do customer, product and financial records share common IDs across systems?
3. How complete and accurate is the data behind your priority use case?
4. Are the target processes documented and stable?
5. Do you have rules for data access, privacy and AI use?
6. How do decisions use reports and dashboards today?
7. Have staff been trained to use AI tools safely?
0 of 7 answered
What it is

What is an AI readiness assessment? A test of whether AI will work for you now

An AI readiness assessment is a structured review of whether an organization has the data, technology infrastructure, processes, rules and skills to use AI for specific business goals. It answers one leadership question. If we invest in this use case now, will it work? And if not, what has to change first?

Each named use case lands in one of three groupsDecision tree: three tests per use case with side exits for wait and needs groundwork, ending at start now.Each named use case lands in one of three groupsThe tests applied to every use case, such as invoice captureQ1Is there a clear decision and businesscase?NoWait: agree the goal and measurefirstYesQ2Is the data it needs connected andclean?NoNeeds groundwork: integrate and fixthe dataYesQ3Are the process, owners, rules andskills in place?NoNeeds groundwork: document, assignowners, trainYesStart nowReady for a pilot with a named owner and measureEach named use case lands in one of three groupsMobile view: Decision tree: three tests per use case with side exits for wait and needs groundwork, ending at start now.Each named use case lands in one ofthree groupsThe tests applied to every use case, such as invoicecaptureQ1Is there a clear decision andbusiness case?No: Wait: agree the goal and measure firstYesQ2Is the data it needs connected andclean?No: Needs groundwork: integrate and fix thedataYesQ3Are the process, owners, rules andskills in place?No: Needs groundwork: document, assignowners, trainYesStart nowReady for a pilot with a named owner and measure

It differs from a vendor demo or a general survey in three ways. First, it starts from named use cases, such as automating invoice processing or summarizing customer calls, not AI in the abstract. Second, it tests readiness against real data in your own systems, not only against interview answers. Third, it ends with a ranked plan and an expected value view, so the result can be funded and delivered.

Data is often the blocker. So a good assessment is also a data readiness assessment and an analytics maturity assessment. It often works as a data health check for the whole organization, even when AI plans change later. It also gives the leadership team a shared, plain-English language for AI decisions. The result is practical. It tells you where to spend first, and where not to spend yet, in plain terms. Typical questions it answers include the following.

Start the right use cases first

See which can start now, which need groundwork and which should wait for a stronger foundation.

Know what is blocking the rest

The specific data, process, governance or skills gaps, ranked by impact and effort to close.

Assign clear owners

Each action has a named owner, from data stewards and process leads to the executive sponsor.

Judge the expected return

Business case inputs for each use case: effort, cost drivers, risks and the decision it improves.

How we work

The assessment runs in two to three weeks, with no surprises at the end

The assessment runs over 2 to 3 weeks (typical, depends on scope), depending on the number of use cases and systems in scope. Most work is remote, with interviews scheduled across time zones for teams in the US, the UAE and elsewhere. Early findings are shared as we go, so the final session confirms what you already expect.

Two to three weeks, with findings shared as we go

Select a stage, use Next or the arrow keys

Week 1: Agree scope. Week 1: Agree scope and success. We confirm use cases, interview leaders and process owners, and get read-only access for the data review.

Step 1 of 3

Your team's time is kept light. Most people spend one interview with us. Process owners may join a short follow-up to walk through a workflow. The executive sponsor attends the kickoff and the final session.

KPIs we track

A sample readiness scorecard shows each score, its evidence and the next action

Each assessment ends with a scorecard like this sample, shown on demo data for a fictional company. Each dimension gets a readiness score from 1 (not ready) to 5 (ready), on a simple maturity model. Each score comes with the evidence behind it and one next action. That makes the scorecard a to-do list, not just a grade. We walk through a full sample report in the first consultation.

Data and governance hold this company back, not process

Readiness score by dimension, 1 (not ready) to 5 (ready), fictional company

Demo data
Process readiness4Analytics maturity3People and skills3Governance2Data readiness14 in Process readiness: Invoice capture is ready to pilotProcess readiness4Analytics maturity3People and skills3Governance2Data readiness14 in Process readiness: Invoice capture isready to pilot

So what: Pilot invoice capture now while integration and data owners are fixed in parallel.

Data readiness: 1 of 5

Demo

Customer data is split across CRM and billing with no shared ID. Next: integrate and deduplicate first.

Analytics maturity: 3 of 5

Demo

Finance dashboards are trusted; operations KPIs are manual. Next: automate the weekly operations report.

Governance and security: 2 of 5

Demo

No named data owners and no AI usage policy. Next: assign owners and approve a policy.

Process readiness: 4 of 5

Demo

Invoice processing is documented and stable. Next: pilot AI-assisted invoice capture.

People and skills: 3 of 5

Demo

Leadership is aligned; staff need practical training. Next: run role-based AI and data training.

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Comparison

AI readiness vs analytics maturity vs data audit: pick the review that fits the decision

These three reviews are often confused. Each one supports a different decision. Choose the AI readiness assessment when you are deciding on AI investment. Choose an analytics maturity assessment when planning reporting work. Choose data audit services before a migration or integration. The table compares them.

AI readinessAnalytics maturityData audit
Main questionCan we use AI for these goals now?How well do we use data for decisions?Is our data accurate and well managed?
ScopeData, processes, rules, technology and peopleReporting, KPIs, tools and skillsQuality, structure and controls in specific systems
OutputReadiness scorecard, blockers, AI roadmapMaturity score and analytics planData quality findings and fix list
Typical length2 to 3 weeks (typical, depends on scope)2 to 3 weeks (typical, depends on scope)Depends on systems and records reviewed
Best forLeaders deciding on AI investmentTeams planning BI or data strategyTeams preparing a migration or audit
Use cases

Every finding leads to a clear next step: advisory, training, then projects when you are ready

The assessment is the starting point for what follows. Each finding maps to a next step, in a deliberate order: advisory to set direction, training where your team needs it, and implementation only for use cases that are ready. You choose whether to act internally, with us or with another provider. The roadmap is written so any capable team can deliver it, inside or outside your company. A common next step is one or two of the paths below.

Every readiness finding maps to one next stepReadiness scorecard in the center with arrows to five next steps, data modernization, strategy and governance, automation, training and advisory, each labeled with the gap it closes.Readiness scorecardDataAnalyticsGovernanceProcessPeopleData modernizationGap: integration, qualityStrategy and governanceGap: owners, policiesAI and process automationReady: start nowAI and data trainingGap: adoption skillsOngoing advisoryGap: delivery capacityEvery readiness finding maps to one next stepMobile view: the readiness scorecard leads to five next steps, each labeled with the gap it closes.Readiness scorecardDataAnalyticsGovernanceProcessPeopleAI and process automationReady: start nowData modernizationGap: integration, qualityStrategy and governanceGap: owners, policiesAI and data trainingGap: adoption skillsOngoing advisoryGap: delivery capacity
Use case 1

Set rules and direction

Data strategy and data governance work assigns owners, definitions and usage policies tied to leadership priorities.

Use case 2

Build skills across the team

AI and data training for employees builds the skills to adopt AI tools safely.

Use case 3

Fix the data foundation

Once priorities are agreed, data modernization projects close integration and quality gaps, so priority use cases have reliable data.

Use case 4

Deliver the ready use cases

AI automation and process automation start only with use cases scored ready: the highest-value, lowest-risk ones.

Use case 5

Keep momentum month by month

Strategic advisory or analytics as a service delivers the roadmap, tracked against the original scorecard.

data modernization projectsdata strategydata governanceAI automationAI and data trainingstrategic planning advisory
Frequently asked questions

AI readiness assessment: questions leaders ask before they invest in AI

An executive sponsor, usually the CEO, COO or CFO, plus the owners of the processes and systems in scope and the data or IT lead. Most people need one interview of about an hour; process owners may add a short follow-up. The sponsor joins the kickoff and the final working session, where priorities are agreed.

No. If you have candidates, we test them. If not, we run a short workshop at the start to find and rank use cases by value and feasibility. Either way, the assessment ends with specific use cases, each scored for readiness and linked to a next step.

A data audit checks the quality and control of data in specific systems. An AI readiness assessment includes that data health check. It also covers processes, governance, skills and the business case, and ties every finding to the AI use cases leadership is actually considering.

The CFO gets business case inputs for each use case: the decision or process it improves, the effort and cost drivers, the prerequisites and the risks. This lets AI investments be compared with other priorities on the same basis, before any budget is committed.

The assessment has a fixed scope and fee agreed in advance, based on the number of use cases, systems and business units involved. There are no open-ended hours. Follow-on work is scoped separately, and you are under no obligation to continue with us.

Book your assessment and decide on AI with evidence

Tell us which AI opportunities your leadership team is weighing. In a first consultation we confirm the scope, the people involved and the timeline for your assessment. There is no obligation and no preparation needed. You leave with a clear view of what the assessment would cover and who should take part. Book a consultation to get started.

See how we work
Ten scattered AI ideas become three ready use cases and two owned blockersBefore: ten unranked AI ideas. After the assessment: three use cases ready to start and two blockers, each with an owner (demo data).BeforeTen AI ideas, no prioritiesChatbotForecastsLead scoringInvoice captureChurn alertsEmail draftsPricingSupport triageSearchReportsAfterThree ready, two blockers with owners3 use cases readyLead scoringInvoice captureSupport triage2 blockersChurn alertsOwner: CRM leadPricingOwner: CFODemo dataTen unranked AI ideas become three ready use cases and two owned blockersMobile view: ten AI ideas with no priorities become three ready use cases and two blockers with owners (demo data).BeforeTen AI ideas, no prioritiesChatbotForecastsLead scoringInvoice captureChurn alertsEmail draftsPricingSupport triageSearchReportsAfterThree ready, two blockers with owners3 use cases readyLead scoringInvoice captureSupport triage2 blockersChurn alertsOwner: CRM leadPricingOwner: CFODemo data
Related resources

Explore the services and ways of working connected to the assessment. Each builds on the same evidence, scorecard and roadmap. So nothing has to be rediscovered, and work can start as soon as the findings are signed off. Your team keeps every document, whichever path you choose next.