A team can have capable AI tools and still prepare the same report by hand every Friday. The missing piece may be quite specific: nobody has brought together the instructions, source material, output format and tests that would make the job repeatable.
That is delivery work. It connects a general-purpose tool with the way a particular business operates. Our view is that this is a major opportunity for businesses that already have AI and want to get more useful work from it.
A signal from Anthropic.
On 4 May 2026, Anthropic announced an AI services company with investors including Blackstone, Hellman & Friedman and Goldman Sachs. The announcement describes engineers working alongside mid-sized organizations to identify opportunities, build systems around their operations and support them. Anthropic's CFO, Krishna Rao, said:
“Enterprise demand for Claude is significantly outpacing any single delivery model.”
Anthropic's announcement describes the model in more detail. Our reading is that delivery capacity matters alongside model capability: someone has to understand the business and turn that understanding into working tools.
The business supplies the knowledge.
Consider what is needed to build a useful handoff. The system fields tell you what information exists. The operations lead tells you why a particular customer needs an extra approval, which document controls the billing terms and what happens when an order is incomplete.
Some of that knowledge is in an SOP. Some is in a checklist, an old example or the person's explanation of how they handle the job. Capturing it is part of the build, alongside agreeing what a good output looks like and testing the workflow on real work.
The technology and the person who knows the job need to meet. That is the service a business is buying when it asks for a working capability.
Make the first engagement fit the team.
For a business of 20–200 people, a useful first step can be one recurring task with a clear output and a fixed scope. The team gets something it can use, and the owner gets a concrete basis for deciding whether to invest further.
That is the approach we built Fully Powered AI around. Fully Powered Studio handles repeatable build and test work. A builder works alongside the customer to capture the procedure, resolve questions and help the team run the workflow in its own ChatGPT, Claude, Copilot or Gemini.
The 3-Day Workflow Sprint builds from one agreed written procedure. The 2-Week Team Sprint captures one team's procedures and builds one priority workflow. Both start with files and exports and end with a workflow, test findings and handover notes the customer owns.
Delivery includes putting it into use.
A workflow on a builder's screen is only part of the job. The person who will use it needs to run representative work, understand what to check and know what to do when the inputs are incomplete.
Measure preparation, review and correction together. Name an owner to keep the procedure current. Decide whether the next step is another workflow, a direct connection or support as the work changes; those are separately scoped decisions.
The ambition is straightforward: make the expertise already in a business useful to more of its people. Start with one job the team recognizes, get it working and build from there.