An AI pilot can produce a good demonstration while leaving the business process almost unchanged. The model may answer selected questions correctly, but the team still has no agreed owner, reliable integration or way to decide whether the work is ready to use.
There is no single failure percentage that explains every AI project. Definitions of a project, failure and return on investment vary between studies. The more useful question is where your own delivery path stops producing evidence.
Start with the task, not the demonstration
Identify a specific piece of work and the people responsible for it. Record the current process, exceptions, systems and checks. Then define what an improved process would look like and how you would measure it.
“Use AI in customer operations” is too broad to test. “Prepare a response from approved policy documents for a staff member to review” has a clearer input, boundary and decision owner.
Some parts of the task may need a model’s interpretation. Others may be ordinary software rules or a workflow change. Choosing the simpler approach where it fits reduces the number of uncertain components you have to operate.
Make acceptance concrete
Agree a representative set of tasks before optimising for the best-looking outputs. Include missing information, conflicting sources and requests the system should not fulfil.
Define the evidence needed for acceptance. A knowledge answer may need a supporting passage. A document workflow may need reproducible calculations and a reviewer’s decision. A tool action needs confirmation from the system that owns the record.
Keep technical success separate from business value. A high score on a prepared test does not establish that staff will adopt the process or that the project has a positive return.
Name the operating owner early
Decide who owns the workflow, who approves consequential actions and who handles exceptions. A project that depends on the implementation team interpreting every unusual case has not yet established its operating model.
Security and privacy reviewers should see the proposed data flows while they can still shape the design. Include prompts, retrieval, tools, logs and subprocessors in that review.
The NIST AI RMF offers a voluntary structure for this risk work. It is not a substitute for a project owner or evidence from the actual application.
Budget for integration and operation
The model call is one part of delivery. Source preparation, access controls, interface contracts, evaluation, user training and support can be material parts of the work.
A prototype may use a manually prepared file or a privileged test account. Production needs a repeatable ingestion path and permissions appropriate to real users. It also needs a plan for source changes, service failures and interrupted tasks.
Estimate recurring costs from a representative workload. Include retries, review effort and unresolved exceptions. Avoid presenting a token-price comparison as the full business case.
Release a useful boundary
A narrowly scoped release can produce better evidence than a large promise. Keep the task bounded, make limitations visible and decide what must be true before expanding it.
For an illustrative internal-policy assistant, a first release might cover one approved collection and return source-linked drafts to a small review group. Expansion would depend on retrieval quality, permissions, user feedback and operating ownership. This is a planning example, not a reported client outcome.
Measure the work after release and compare it with the starting process. If the system shifts effort from preparation to correction, that needs to be visible in the evaluation.
Decide what to continue
A pilot may justify expansion, a redesign or stopping the project. Each is a useful result when the decision follows clear criteria.
Do not treat a fixed delivery timetable or a successful demonstration as a guarantee of production readiness. Ask what evidence remains missing and who is responsible for producing it.
Our How We Work page sets out the requirements, scope, build, verification and support stages. The aim is to give the organisation a working process it can understand and own.
Written by Anshul Kaushik, Managing Director at Ryzolv.
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