Find where AI pays. Then build it.
The Modern Thesis strategic assessment is where every engagement starts. For non-tech companies with $10 million or more in annual revenue, it maps how work really moves, measures time, cost and error rates on key workflows, ranks AI opportunities by return and defines the first build.
What is the strategic assessment?
The strategic assessment is the first week of a Modern Thesis engagement. We learn how your business actually runs, measure the workflows that matter and work out where AI will pay for itself first. Every result we report later is measured against the numbers we take here, so you have a baseline you can hold us to.
Who is it for?
The assessment is for CEOs, COOs and founders of non-tech companies with $10 million or more in annual revenue. You don't need a CTO or a technical team. You may already pay for ChatGPT or Copilot, and those are good tools for individuals. The assessment looks past individual use to the business as a whole, starting with where AI fits your systems and what it should return.
What happens, and in what order?
- A first conversation. You tell us where the business is today and what you want AI to do for it. We agree the scope, the workflows in view and the time we need from your team.
- Interviews and workflow reviews. We talk with the people who do the work and follow how it moves through the parts of the business that matter most.
- The baseline. We measure time, cost and error rates on your key workflows.
- The ranking. We rank the opportunities by return: where AI pays first, what each should cost to build and what it should return.
- The roadmap. We walk you through the findings and define the first build.
What do you leave with?
- A map of how work really moves. Drawn from interviews and workflow reviews, so it shows how the work gets done today, handoffs and workarounds included.
- A baseline you can measure against. Time, cost and error rates on your key workflows, so every result can be proven later.
- Opportunities ranked by return. Where AI pays first, what it should cost to build and what it should return.
- A roadmap with the first build defined. What gets built first, in what order, and how success will show up in numbers you already track.
Why is it the first week of the build and not a report?
The people who run the assessment are the people who build what comes next. Our AI specialists and Forward Deployed Engineers work with Jim from the first interview, so the findings come from the team that will act on them. Nothing in the assessment is written for a shelf. The baseline becomes the yardstick for the build, and the first build is defined well enough to start.
What does your team need to give?
Less than you might expect. We need some of your time to set priorities and review the findings, time with the people who do the work in the workflows we review, and access to the systems and records those workflows use. We agree the time commitment up front and keep to it. Access to your data is agreed in writing before we touch anything and limited to what the work needs. Our data policy has the details.
How is the assessment priced?
Every engagement is priced to its scope. After a first conversation, we know how many workflows are in view and how much of the business they touch, and we price the assessment to fit. You see the scope and the price before any work starts. We don't publish a rate card, because no two companies need the same assessment.
What if AI won't pay for itself somewhere?
Then we'll tell you. Some workflows aren't worth automating, and knowing which ones to leave alone saves money. We rank every opportunity by return, so the ones that won't pay sit at the bottom of the list, and we say so plainly.
How Modern Thesis handles this
Jim leads the assessment and works directly with you and your leaders. Our AI specialists and Forward Deployed Engineers shape the findings with him and lead the build that follows. Change management starts in the first week: we talk with your people early, so the team knows what is coming and why. After launch, stewardship keeps your systems current and reports the return against the baseline from the assessment.
Questions
How long does the assessment take?
The assessment is the first week of the project. The exact schedule depends on how many workflows are in view and how many people we need to talk with, and we agree it with you before any work starts. Because the same team carries the work into the build, the end of the assessment is the start of implementation, so the first build can begin as soon as you decide to go ahead.
Do we need a CTO or technical staff?
No. The assessment is built for companies without a CTO or an in-house technical team. Jim works with you and your leaders on the business questions, and our AI specialists and Forward Deployed Engineers handle the technical side. What we need from your company is time with the people who do the work and access to the systems those workflows use, both agreed up front.
Will you look at the AI tools we already pay for?
Yes. If you already pay for ChatGPT, Copilot or the AI features inside your industry software, the assessment looks at where those tools fit and keeps them in the plan wherever they do the job. We recommend custom builds only where your existing tools can't connect to your systems, run the workflow or deliver the return. The ranking shows you which is which.
Our data and processes are messy. Should we clean them up first?
No. Almost every business we see is messy, and untidy data is the most common implementation problem there is. The assessment is designed to find that mess and size it. Where cleanup is needed before AI can pay, the roadmap says so and includes it in the plan, so you don't spend months tidying records that no workflow depends on.
What happens after the assessment?
You leave with a roadmap and a defined first build, and you decide whether to go ahead. If you do, our team builds it, tests it and brings your people along through training and a change plan. After launch, stewardship begins: we monitor and maintain the system, keep it current as tools change and report the return against the baseline measured in the assessment.
Is our data safe during the assessment?
We agree how your data is handled, in writing, before we access anything. Access is limited to what the work needs, we never use your data to train AI models, and you keep ownership of your data, accounts and documentation. Our data policy sets out the details in plain English, and we are glad to walk your team through it in the first conversation.