AI Agent Proof of Concept in Dubai: Scope, Cost, Timeline and Success Criteria

Use the proof of concept to answer a decision

An AI agent proof of concept should resolve a defined uncertainty before the organisation commits to a larger implementation. The question is not simply whether an agent can complete a demonstration. It is whether one bounded workflow can produce a useful business outcome under the conditions in which people would rely on it.

The sponsor should decide in advance what the pilot will allow management to choose. The decision may be to scale, revise the design, use a simpler intervention or stop. Treating continuation as the only successful result weakens the experiment because the team is rewarded for preserving the project rather than learning whether it deserves further investment.

Choose one complete workflow

The strongest pilot boundary follows one outcome from a clear starting event to a clear ending condition. It may cover triaging a service request, preparing a supplier review, resolving a defined class of finance exception or assembling evidence for a decision. It should not attempt to prove an enterprise-wide agent platform through several loosely connected demonstrations.

A complete workflow includes the information the agent receives, the reasoning or classification it performs, the tools it can use, the actions it can take, the points where people intervene and the record left when the case is complete. The scope should include ordinary cases and the exceptions most likely to determine whether the workflow is workable.

Start with the current performance baseline

An AI pilot needs a comparison with current performance and a simpler alternative. Before development, the team should measure how the workflow performs now. Suitable measures can include end-to-end time, active handling time, error, rework, unresolved cases, repeat contact, service quality, supervision, customer outcome and operating cost.

The baseline prevents the pilot from counting activity as value. An agent may produce an answer quickly while increasing the time people spend checking it, recovering failures or moving information between systems. The relevant comparison is the completed outcome after review, exceptions and failures are included. A simple rule-based or non-AI alternative may also provide a useful benchmark.

Define success before building

Success criteria should follow the claim the pilot is intended to test. They normally need more than one measure:

• Business outcome: whether the workflow improves the result management cares about.
• Task quality: whether outputs and actions meet the required standard across representative cases.
• Failure and containment: which errors occur, whether they are detected and whether their consequences remain controlled.
• Human workload: how much review, correction, escalation and recovery the workflow requires.
• Operating fit: whether the agent works with the required data, tools, systems, permissions and service conditions.
• Economics: whether the complete operating cost remains plausible after usage, integration, supervision, evaluation and support.
• User and affected-party experience: whether the people using or affected by the workflow can understand, challenge and recover from it where required.

Thresholds should be set before results are known. Otherwise a team can reinterpret any outcome as evidence that the pilot worked.

Build only what the experiment needs

A proof of concept needs enough of the operating system to make its result meaningful. If the claim concerns retrieval from approved policy, the pilot needs a representative knowledge source and evidence of source fidelity. If the claim concerns action, it needs a controlled tool connection, permission boundary and recovery route. If the claim concerns staff productivity, it needs real users and the complete review workload.

An AI pilot does not need every production feature. The team can constrain volume, users, data, channels, tools or action authority as long as those constraints are explicit. Synthetic or replayed cases may be appropriate early in testing. A limited field pilot can follow only after the organisation has decided that the exposure and approvals are suitable.

Plan the evaluation in layers

Evaluation should progress from controlled tests to increasingly realistic use. Component tests examine retrieval, classification, calculation and tool calls. End-to-end scenarios test whether the whole workflow reaches the right outcome. Adversarial and unusual cases examine prohibited actions, misleading instructions, missing evidence and failure recovery. Human-centred testing examines whether people can use, review and challenge the system. A limited field pilot then tests the workflow under real operating conditions where appropriate.

NIST's ARIA pilot similarly distinguishes model testing, red teaming and field testing. The lesson for a business pilot is that one benchmark or demonstration cannot answer every evaluation question. Evidence must match the intended use and the consequence of failure.

What determines the cost

The cost of an AI agent proof of concept depends more on the uncertainty and operating work than on access to a model. Important drivers include:

• the breadth and variation of the workflow;
• the condition, sensitivity and accessibility of data and documents;
• the number and complexity of integrations and tools;
• the authority the agent receives and the controls that authority requires;
• the languages, channels and user groups included;
• the volume and quality of test cases;
• the human review, specialist input and approvals required; and
• the amount of production-like infrastructure needed to support the claim.

A credible proposal should separate discovery, build, integration, evaluation and pilot operation. A low development price can conceal work the client must later absorb in data preparation, testing, security, supervision or redesign.

What determines the timeline

A timeline should be built around evidence gates rather than a promised number of weeks. The usual sequence is to confirm the workflow and baseline, prepare data and access, design the experiment, build the bounded workflow, run controlled evaluation, address material failures, conduct limited operational testing where appropriate and prepare the management decision.

The longest constraint may be obtaining representative data, integration access, user time or approval rather than configuring the agent. The plan should make these dependencies visible. Compressing the schedule by removing evaluation or exception design produces an earlier demonstration, not an earlier answer.

Reach a stop, revise or scale decision

At the end of the pilot, management should receive the result against the pre-agreed baseline and thresholds, the observed failure modes, the supervision and operating demands, the remaining uncertainties and the conditions required for a wider deployment.

Scaling is justified only when the evidence supports the intended use and the organisation can supply the ownership, integration, controls and monitoring production requires. A result from one set of users, data, permissions or volumes should not be assumed to generalise unchanged. A sensible decision may be to narrow the use, redesign the workflow, improve the data, choose another intervention or stop.

How Marketways approaches an AI agent proof of concept

Marketways connects the agent to the business workflow, evidence and management decision. We define the pilot boundary, reconstruct the current process, establish the baseline, design the system and evaluation, implement the bounded workflow and report the stop, revise or scale decision.

AI Agent Development and Deployment owns the implementation route. AI Workflow Audit is relevant when the right workflow or intervention remains unclear. AI Agent Evaluation and Assurance provides a separate assurance route when management needs independent evidence. AI Vendor Selection is relevant when the organisation must compare external platforms or delivery partners.

How to Move an AI Pilot into Production addresses the later operating transition after a pilot has produced sufficient evidence to justify it.

What to bring to the first discussion

Prepare an evidence pack before defining the proof of concept. The evidence should include the workflow or decision, the outcome management wants to improve, ordinary and difficult cases, available data and documents, existing systems, current performance, known constraints and the people accountable for the completed outcome. If the workflow is still broad, the first task is to identify a pilot boundary that can produce a clear decision.