An AI transformation assessment is a short, structured review of how your business runs today, to find where AI will create the most value first. It looks at your systems, your biggest bottlenecks and your quickest wins, and it ends with a concrete roadmap: what to build first, the timeline, the tasks and how success will be measured.
What is an AI transformation assessment?
It is a structured look at how work actually gets done in your business, with one goal: decide where AI should go first. Some firms call it an AI readiness assessment. The name matters less than the output, which should be a plan you can act on.
Many employees haven't seen that plan yet. In Gallup's data as of May 2026, only 25% of U.S. employees said their organization had communicated a clear plan for integrating AI into its work.1 An assessment is one way to close that gap without committing to a large program up front.
Why do an assessment before building anything?
Because the companies reporting the most value from AI change specific workflows and measure the results, and you have to choose those workflows first. McKinsey's 2026 State of AI survey found that only 37% of respondents attribute any EBIT impact to AI. Among high performers (the roughly 6% of respondents who attribute at least 5% of EBIT to AI), nearly three-quarters report fundamentally redesigning workflows because of their AI use, compared with about a quarter of other respondents, and high performers are twice as likely to report that their organizations have defined processes to measure AI's impact.2
In other words, the companies reporting the most value tend to change how the work is done and measure the result. An assessment is where you decide which work to change first.
What does an AI transformation assessment look at?
Four things: your systems, your bottlenecks, your quickest wins and your longer-term goals.
| Area | Questions it answers | What you get |
|---|---|---|
| Tech stack | Which systems hold the work? Where does data live? What can be connected? | A map of systems and data, including gaps |
| Bottlenecks | Where does work wait, get retyped or depend on one person? | A ranked list of problem workflows |
| Quick wins | Which workflow could show a measurable result in weeks? | A recommended first workflow, with a baseline |
| Longer-term vision | Where should AI take the business over the next year or two? | A sequence of later projects |
| Risk and data | What data is sensitive? Who must approve what? Where can AI run? | Guardrails and deployment requirements |
NIST's AI Risk Management Framework describes a similar early step, which it calls Map: establishing the context of an AI system so risks can be understood before deciding whether to go ahead.3
What do you get at the end of an AI assessment?
A short, specific roadmap you can act on. A useful one fits on a page and includes:
- The first workflow to build, and why it was chosen.
- A timeline for that first workflow and what follows.
- A statement of work describing what will be built and by whom.
- The tasks your team and the builders each own.
- Measures of success, with today's baseline, so everyone can tell whether it worked.
The most common surprise in our assessments is how scattered a team's information is. Often the first recommendation is to pull documents and data into one place, for example with a searchable internal knowledge base, before automating anything else.
Who should run the assessment, and who should be in the room?
It should be run by people who will build the solution, and attended by the people who do the work and the people who can approve it.
Engineers can tell quickly whether an idea will work with your systems and data. The people doing the work know where the real bottlenecks are, which aren't always where leadership expects. And someone with budget authority needs to be there so the roadmap turns into a decision. If you're comparing partners, it helps to know how AI-native and traditional consulting differ.
How does the Soren AI Transformation Assessment work?
It starts with a 30-minute call, then a workshop with your team. It is run by our engineers. We look at your tech stack, your biggest bottlenecks, your quickest wins and the grand vision, and you leave with a one-page roadmap covering the timeline, statement of work, tasks and measures of success.
If you decide to go ahead, the first workflow is usually something with directly attributable ROI, and it is typically live within two to three weeks of getting access to the systems it needs. More on how we work, and on our work with law firms and banks and credit unions.
When can you skip the assessment?
When you already know the workflow, the systems involved and how you'll measure success. If your team can say "intake takes three days and we want it under one," you may be ready to start building.
AI use is still early for most businesses: the Census Bureau found that 19.8% of U.S. businesses were using AI as of early May 2026.4 If your team is new to it, a short assessment helps you pick the first workflow without committing to a larger program.
Find out where AI should go first
Start with a 30-minute call with our team. We'll look at your systems, your biggest bottlenecks and your quickest wins, and tell you honestly where to start.
Frequently asked questions
How long does an AI transformation assessment take?
A focused assessment can take a short call and one working session with your team, followed by a written roadmap. Larger organizations with many departments may need more sessions. If an assessment is scoped to run for months, ask what decision it will produce and when.
What's the difference between an AI readiness assessment and an AI strategy?
A readiness or transformation assessment looks at specific workflows, systems and data to decide what to build first. An AI strategy is broader and usually covers organization-wide goals, governance and investment over several years. In our experience, many businesses get more value from starting with an assessment and building the strategy from real results.
Who from our team should be involved?
Include the people who do the work in the workflows under review, someone who knows your systems and data, such as IT, and a decision-maker with budget authority. Compliance or risk should join if the workflows involve sensitive data. Without the people doing the work, assessments tend to miss the real bottlenecks.
What happens after the assessment?
You decide whether to act on the roadmap. If you go ahead, the first workflow is built, tested with the people who will use it, and measured against the baseline set in the assessment. Later projects follow the roadmap and get adjusted as you learn from real results.
Sources
- Global Indicator: Artificial Intelligence, Gallup, data as of May 2026
- The state of AI in 2026: On the road to ROI, McKinsey & Company, August 2026
- Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST, January 2023
- Large Firms With at Least 20 Employees Biggest AI Users, U.S. Census Bureau, May 2026
