Short answer
Yes, some AI consulting firms take an AI strategy all the way to working software, and the ones that do run four phases: learn the business, tailor a plan, build and ship, then iterate. Lattice Partners is an AI implementation partner based in Los Angeles and New York that helps companies build an AI strategy, trains their teams on AI tools, and builds custom agents and software. The strategy becomes a funded plan when the first use case has a clear scope, a named owner, a success measure, and a yes or no budget decision.
- An AI strategy is only useful once it names one workflow to change first and who owns that change.
- First versions of custom AI software usually ship in weeks, and most products go through 2 to 3 iterations.
- The best first use case is frequent, painful, measurable, and fed by data you can already reach.
Are there AI consulting firms that turn an AI strategy into working solutions?
A smaller group of AI consulting firms carries the strategy through to working solutions, while plenty stop at the deck. The ones that get software in front of your team usually have engineers on the engagement from the first meeting.
A strategy-only engagement ends with a roadmap and a recommendation to hire someone to build it. That handoff is where many plans die, because the builders start over with a document instead of the context. An end-to-end firm keeps the same people from discovery through launch. Ask any firm you're evaluating to show you software they built and shipped. Our guide on how to tell if an AI consulting firm ships software lists the questions to ask.
Firms that do this are often called AI builders.
What does an AI consulting framework look like, phase by phase?
A practical AI consulting framework has four phases: Learn, Tailor, Build and Ship, and Iterate.
| Phase | What happens | Output | Typical duration |
|---|---|---|---|
| Learn | Interviews with the people doing the work, review of current workflows, systems, and data. Find where time and money are wasted. Say so if AI isn't the answer. | A short list of ranked use cases with the reasoning behind each one | A few weeks at most |
| Tailor | Design the first solution around your data and workflows, at a level your team can pick up. Decide build vs. buy for each piece. | A scoped plan for the first use case: what gets built, who owns it, how success is measured | Days to a couple of weeks |
| Build and Ship | Build the first version, connect it to real systems, and train the team hands-on with the tool they'll use. | Working software in real use, plus a trained team | First versions of a well-scoped project usually ship within a few weeks |
| Iterate | Improve from real usage. Fix what breaks and add what people ask for, and drop features nobody touches. | A product that fits how the team works, usually after 2 to 3 iterations | Ongoing, in short cycles |
Learn is also where a good firm will tell you when AI isn't the answer. Sometimes a cleaner process or an off-the-shelf tool like ChatGPT Enterprise or Claude solves the problem for less.
"The same people run discovery and write the code, so what we learn in the first session shows up in the pilot within weeks."
Martin Anquetil, Co-Founder and Managing Partner, Lattice Partners
How do you pick the first AI use case?
Start with a workflow your team runs often that eats real hours, has an obvious owner, and runs on data you can already get to. People should notice when it gets better, so pick that over a more ambitious idea nobody would feel day to day.
Score each candidate on five questions:
- How often does it happen? Daily or weekly work beats a quarterly task.
- How much time or money does it cost today? Ask the people who do it.
- Can we reach the data? If the inputs live in a system with no export or API, plan for that first.
- Who owns it? One person needs to care whether it works and have authority to change the process.
- How will we know it worked? Hours saved, turnaround time, error rate, or quotes sent per week.
For a metal fabrication shop, that's often quoting, since every quote waits on one engineer. A mid-market private equity (PE) firm is more likely to start with screening companies against its mandate, work that can take analysts weeks and that AI can cut to hours.
Pilots that skip this step tend to stall, as why AI pilots stall explains.
How do consulting firms turn an AI strategy into a funded execution plan?
Consulting firms turn an AI strategy into a funded execution plan by boiling each use case down to one page that leadership can say yes or no to. That page covers scope, owner, success measure, budget, and what comes next.
- Scope. What the first version does and doesn't do. "Drafts a quote from a drawing and the material list for the estimator to review" is a scope. "AI for sales" isn't.
- Owner. One person on the client side who uses the tool, makes calls on tradeoffs, and tells the builders when something is wrong.
- Success measure. A number you can check before and after. One or two measures is enough.
- Budget decision. What the first version costs to build, what it costs to run, and who owns the result. With a one-time custom build, the client owns the code and there's no recurring per-seat license from the builder.
- Next step if it works. What the second use case is, so the first win has somewhere to go.
How long does it take to go from strategy to production?
Going from AI strategy to production usually takes weeks for a first version when the scope is one workflow and the data is reachable. Bigger builds with several integrations or approval steps take longer, and our article on how long it takes to build custom AI software breaks that down by project type.
When a project slows down, the cause is usually waiting on system access or a security review, or an owner too busy to give feedback. The model is rarely the holdup.
For timelines by project type, see how long custom AI software takes to build.
How Lattice Partners does this
Lattice Partners runs the full path from strategy to production with the same small team. The three founders are technical and write the code themselves.
An engagement follows the four phases above, and if AI isn't the answer, we'll say so. A well-scoped first version usually ships within a few weeks. Training is hands-on with ChatGPT, Claude, and the agents we build, and most products go through 2 to 3 iterations after launch. We can stay on after launch to support and improve what we built. Many clients start with AI training, then move into building. We build custom AI agents, internal tools, and Model Context Protocol (MCP) integrations that connect assistants like Claude to systems such as CRMs, databases, and data providers. Our clients are mostly mid-market companies, often founder-led.
Custom software is a one-time build and the client owns the code. Cost depends on scope.
Frequently asked questions
What are end-to-end AI strategy consulting services?
End-to-end AI strategy consulting services cover the whole path from finding the right use case to running software in production. The same firm does discovery, scoping, building, training, and iteration.
Do we need an AI strategy before building anything?
A company needs enough strategy to pick the first use case and justify it, which can be done in a few weeks. Shipping one small, owned use case teaches more than another month of planning.
How much does it cost to go from AI strategy to working software?
The cost of going from AI strategy to working software depends on scope, the number of systems the project connects to, and how many people need training. Training, a custom build, and ongoing support are different kinds of work, so ask any firm to price each one as its own line. Lattice Partners does not publish fixed prices. Contact us for a quote.
Who should own the AI project inside our company?
The owner should be the person closest to the workflow who has authority to change it, often an operations lead, a head of estimating, or a deal team lead. Projects without a named owner tend to stall.
What do practical AI workflows look like compared to proof-of-concepts?
A practical AI workflow is connected to the systems the team already uses, is used on real work every week, and has an owner who measures it. A proof-of-concept runs on sample data in a separate environment and usually gets shown once. Getting from one to the other takes integration work, then training and a few rounds of iteration with the people using it.
Related reading
About Lattice Partners
Lattice Partners is an AI implementation partner based in Los Angeles and New York. We help companies build an AI strategy, train their employees on AI tools like ChatGPT and Claude, and build custom AI agents and software. Clients own the code we build. Lattice Partners was founded by Colin McDonnell, Martin Anquetil, and Christian Yeghnazar. Contact us to talk about your use case.