AI strategy consulting
An AI strategy consultant for the decisions before the build.
You may have several AI tools, a promising pilot and competing proposals. We work with your team to decide which problems are worth solving, what the systems need underneath them and how to judge the first release.
Based in Dubai. Working with businesses in the UAE, UK and US.
Trace one real example before choosing a tool.
Start with the person who does the work today. Follow an input through the process: where it comes from, which records get checked, who makes the decision and what happens when information is missing. That makes the constraints concrete enough to decide whether AI can help.
An AI strategy consultant should separate the problem from a proposed product. A team asking for an agent may first need consistent client records. A request for a dashboard may be a question about which source is current. Identifying that dependency changes what should be built first.
Compare opportunities on value and readiness.
The most visible problem is not always the best first release. We compare the importance of the work with how often it happens, the quality of its inputs and the consequences of an error. The people using the result need enough time and authority to test it.
Record the baseline before deciding what improvement to expect. For an approval workflow, that might be preparation time and the corrections reviewers make. For a knowledge assistant, it might be whether an answer is supported by the right source and respects the user’s permissions. The measure follows the job.
- Value: what changes for the business if the work improves?
- Readiness: are the information, access and owner available?
- Risk: which errors need to stop the workflow or reach a person?
- Evidence: what would make the team accept or reject the release?
Define the foundation the first builds actually need.
Several agents may need to recognise the same client, read the same documents or ask the same person for approval. We identify those shared needs and design the connections, permissions and checks once. The foundation is scoped around the work it has to support.
The resulting plan identifies the systems to connect, the information they expose, the actions an agent may take and the decisions that remain with people. It also names the unresolved dependencies. A missing data owner or unavailable integration belongs in the plan before it becomes a delivery surprise.
Turn the decision into a testable release.
We agree the first-release boundary with your team: inputs, outputs, exceptions, owners and acceptance tests. The engineers involved in the strategy can carry that context into implementation, demonstrate the build with its users and review what happens in operation.
Some opportunities should wait. If the source records cannot be trusted, the approval owner is unavailable or the result cannot be checked, the next useful step may be to fix that condition. The strategy should make that decision clear, with a reason to revisit it.
What we have built.
Client delivery, cash and account ownership were spread across separate systems. The whole-company view brings them together, with each view using the same definitions.
Owners needed to see cash move before the monthly accounts were ready. The daily cash visibility system uses live bank feeds while the accounting platform keeps the official books.
Invoices had to fit a legacy system the business was keeping. The invoice batch build reads, matches and prepares the batch, then leaves final import and approval with the executive.
See the case studiesChoose where to start.
What buyers ask us first.
What does an AI strategy consultant deliver?
A useful strategy identifies which workflows to pursue, why they matter and what the first build depends on. It sets out the data, system access, delivery owners and acceptance tests, including the conditions that need resolving before implementation.
How is strategy different from implementation?
Strategy decides what is worth building and what is needed to make it work. Implementation builds, tests and deploys it. At TwoHundred.ai, the engineers work with your team across those decisions so the plan reflects the systems they will actually have to connect.
How do you decide which AI opportunity comes first?
We compare business value, input quality, system access, risk and the team’s ability to review the result. A clear baseline and an accountable owner make an opportunity easier to evaluate. A large potential benefit on its own is not enough.
Do you promise a particular productivity gain?
No fixed percentage applies to every workflow. We agree a baseline and a relevant measure with your team, then test the working system against it. The result depends on the task, the information available and how the team uses the build.
Can we bring a pilot that has already stalled?
Yes. Bring the pilot, the intended workflow and what stopped it reaching daily use. We can examine whether the issue is the data, integration, permissions, evaluation or fit with the work before deciding whether to extend it or change direction.
How is the work priced?
We price the specific scope after understanding the problem, systems and people involved. The first call helps establish what needs deciding and what would be required to build it; a generic package does not determine the scope.
Book a 30-minute call.
Bring your current AI tools, the pilot that stalled or the proposals you are comparing. We’ll discuss the decision you need to make and what evidence would help.
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