AI maturity scoring
We score you across strategy, data, technology, governance and people on a five-level scale. Each score links to evidence from interviews and system reviews, so you see why you landed there and what moves you up a level.
AI readiness assessment
Know where AI will pay off
Know where AI will pay off
before you fund it
An AI readiness assessment is a 2–4 week fixed-price review of your data, systems, processes and people. You get a maturity score, a data readiness audit, a ranked portfolio of AI use cases with business cases, and a phased roadmap.
Book discovery call
Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
Why do AI plans stall
before the first release?
You have ideas but no ranking
Every team has a favorite AI project, and nobody has compared them on value, feasibility and risk. Budget goes to whoever argues loudest.
Your data isn’t ready for models
Records sit in silos with gaps, duplicates and unclear owners. A pilot works on a clean sample, then breaks on production data.
Leadership wants a business case
Your board asks what AI will return and when. Vendor demos don’t answer that, and an internal estimate lacks a method.
Governance questions block approval
Security, legal and compliance ask who owns model risk, where data flows and how outputs are audited. Without answers, projects wait.
What the assessment covers
and what you take away
Process mapping
We map the workflows where AI could change cost, speed or quality, from intake to decision to hand-off. The maps show volumes, wait times and manual steps, which become the baseline for every business case we write.
Data readiness audit
We profile the sources each use case depends on for completeness, accuracy, access rights and lineage. You get a gap list per use case, with the cleanup, labeling or integration work needed before a model can run on it.
Ranked use-case portfolio
We score candidate use cases on value, feasibility, data readiness and risk, then rank them in one portfolio. Each entry names the owner, the systems involved and the first measurable outcome, so the ranking is easy to defend.
Business case per use case
For the top use cases we build a business case with cost to build, cost to run, expected savings or revenue, and payback period. Assumptions are written down so your finance team can test and adjust them.
Technology and architecture review
We review your cloud setup, integration layer, identity model and existing tools against what the top use cases need. You get a target architecture sketch and a buy, build or extend recommendation for each component.
Governance and risk check
We check how you would govern models in production: data privacy, access control, audit trails, human review points and regulatory exposure such as the EU AI Act. Gaps are listed with a named owner and a fix.
Phased AI roadmap
We turn the ranked portfolio into a roadmap across quick wins, 6–12 month builds and longer bets. Each phase lists dependencies, team needs and a priced first step, so you can start the next quarter without a second study.
Not sure which AI use case to fund first?
Book a 30-minute call and we’ll outline what a 2–4 week assessment would cover.
Scope your assessmentHow a 2–4 week assessment
runs from kickoff to roadmap
-
We meet your sponsors to agree on business goals, scope and the teams we will interview. We collect existing strategy documents, system inventories and data catalogs before fieldwork starts.
Deliverables: Kickoff brief, interview plan, document request list
-
We interview process owners and engineers, map priority workflows and review the systems behind them. We profile the data sources each candidate use case depends on.
Deliverables: Process maps, system inventory, data profiling results
-
We score maturity across five dimensions and rank use cases on value, feasibility, data readiness and risk. We build business cases for the top candidates with your finance contacts.
Deliverables: Maturity scorecard, ranked use-case portfolio, business cases
-
We turn the ranking into a phased roadmap with dependencies, team needs and costs. We present findings to your leadership and hand over every working file.
Deliverables: AI roadmap, executive readout, priced first-phase plan
What changes after the assessment
for your AI budget
Fund the use cases with evidence
Every recommendation traces back to a scored criterion and a written business case, so budget decisions rest on the same method.
Fix data gaps before they sink a pilot
The data audit lists the cleanup and integration work per use case, so you schedule it before a model meets production data.
Give approvers the answers they ask for
The governance check names owners, data flows and review points, which security and legal teams need before they sign off.
Start building next quarter
The roadmap ends in a priced first phase, so the team that scoped the work can move straight into delivery.
Know the cost of the work upfront
A fixed price and a 2–4 week window mean the scope is set before you commit budget.
Ready to see this in your own workflows?
Book discovery callThree ways to assess AI readiness
compared side by side
Here is how a self-assessment, a large strategy firm and a tkxel assessment differ on the points buyers usually weigh.
| Criterion | Online self-assessment | Large strategy consultancy | tkxel assessment |
|---|---|---|---|
| Duration | Under an hour | 8–16 weeks | 2–4 weeks |
| Pricing model | Free | Time and materials | Fixed price |
| Data audit depth | Survey answers only | Sampled, often high level | Source-level profiling per use case |
| Output | Generic maturity score | Strategy deck | Ranked portfolio, business cases, roadmap |
| Path to delivery | None | Separate build vendor | Same engineers can build |
| Fails when | Answers are optimistic guesses | Deck outlives the budget | Stakeholders skip interviews |
Featured success stories
[Placeholder] First case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
[Placeholder] Second case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
[Placeholder] Third case study headline that leads with the metric, then the mechanism.
“Placeholder quote — replace with a real client quote.”
Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Find out where AI fits in your business
Clutch[Placeholder] Clutch badge and rating — add the official widget
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
Frequently asked questions
What is an AI readiness assessment?
An AI readiness assessment is a structured review of whether your data, systems, processes, people and governance can support AI in production. At tkxel it runs 2–4 weeks at a fixed price and ends with a maturity score, a data readiness audit, a ranked list of use cases with business cases, and a phased roadmap. The goal is to tell you which AI projects to fund first and what has to be fixed before they can work.
How long does an AI readiness assessment take?
Our AI readiness assessment takes 2–4 weeks, depending on how many business units and data sources are in scope. A single department with a few core systems usually fits in two weeks. A multi-region company with several ERPs, CRMs and data platforms needs closer to four. The timeline holds only if your process owners and engineers are available for interviews in the first two weeks, so we schedule those during kickoff.
What does an AI readiness assessment cost?
We price each AI readiness assessment as a fixed fee agreed before work starts, based on the number of teams, workflows and data sources in scope. You know the full cost upfront and there are no hourly overruns. Market prices vary widely, and published vendor offers range from a few thousand dollars for a two-week review to tens of thousands for multi-week studies. Ask us for a quote once we have discussed your scope.
What do we get at the end of the assessment?
You get five deliverables: a maturity scorecard across strategy, data, technology, governance and people; process maps of your priority workflows; a data readiness audit per use case; a ranked use-case portfolio with business cases for the top candidates; and a phased roadmap with a priced first step. We also hand over the working files, scoring model and interview notes so your team can update the ranking later.
How do you prioritize AI use cases?
We prioritize AI use cases by scoring each one on business value, technical feasibility, data readiness and risk, then ranking them in one portfolio. Value comes from the process maps and your finance inputs, such as hours saved or revenue affected. Feasibility and data readiness come from our system review and data profiling. Risk covers privacy, regulatory exposure and the cost of a wrong output. The weights are agreed with your sponsors before scoring starts.
Is an assessment worth it if we already know our first use case?
An assessment is still useful if you have a first use case, but you may need a shorter version. If the use case, data and owner are already clear, we can skip portfolio ranking and focus on the data audit, architecture and business case for that one project. The trade-off is that you lose the comparison against other options, so a stronger use case elsewhere in the business can go unnoticed.
Can tkxel build the use cases after the assessment?
Yes, tkxel can build the use cases after the assessment through a forward deployed engineering pod of 3–5 engineers. The pod works inside your environment, owns one outcome from the roadmap, ships it to production and hands it over to your team. You are not obliged to use us for delivery, and the roadmap is written so any capable team can pick it up.
Related insights
All articles
Strategy, Agents
Build vs buy AI agents: platforms, custom builds or a partner
8 October 2026 · 8 min read
Read article Strategy, DataAI readiness checklist: strategy, data, people, governance and infrastructure
8 October 2026 · 8 min read
Read article Agents, StrategyWhy AI pilots fail to reach production, and how to get yours over the line
17 September 2026 · 8 min read
Read article