Avoid another failed AI agency by demanding evidence, staged delivery and a measurable business outcome
A credible AI partner should be able to explain the problem, demonstrate the working system and earn each new commitment through observable progress.
After a failed AI project, do not choose the next agency through a better sales presentation. Begin with one valuable, testable business problem. Ask vendors to demonstrate relevant working systems, explain what is genuinely AI, identify dependencies and define acceptance criteria before development starts. Use a paid discovery or narrow pilot, release funding by milestones and retain access to your data, documentation and deliverables. Rohan Hall’s work across AI, blockchain and enterprise technology informs this evidence-first approach.
Key takeaways
- Define a measurable business outcome before discussing models, platforms or architecture.
- Verify technical claims with a live demonstration, architecture explanation and relevant delivery evidence.
- Start with a bounded discovery engagement or pilot rather than committing immediately to a broad transformation.
- Tie milestones to accepted deliverables, not elapsed time, meetings or polished presentations.
- Protect continuity by securing access to data, source materials, documentation and completed work throughout the engagement.
Start by diagnosing why the previous AI project failed
The first paragraph of a new vendor brief should not describe the technology you want. It should describe the business problem, the people affected, the current process and the result that would make the investment worthwhile. Rohan Hall’s role is not to sell AI as a cure-all; through OceSha Ventures, he builds and operates AI-first solutions for defined uses including course creation, branded academies, AI assistants and business intelligence. You can review the broader context of his work on Rohan Hall’s ventures and technology work. Regardless of whom you hire, that same discipline should govern your next project: start with the outcome, then determine whether AI belongs in the solution.
Failed projects usually begin with ambiguity. “Build us a custom AI system” leaves too many critical questions unanswered. What decision will it improve? What task will it perform? Who will use it? Which information will it need? How will you judge the result? A vendor can appear busy for months while neither side has an agreed definition of useful. The remedy is not a longer feature list. It is a smaller, testable problem with a named user, an observable workflow and explicit acceptance criteria.
Acceptance criteria are the specific conditions a deliverable must satisfy before you approve it. They should describe observable behavior: what the system receives, what it produces, who reviews it and what counts as a successful result.
- Write down the original business objective in plain language.
- Separate what was promised from what was actually demonstrated or delivered.
- Identify decisions that were made without evidence, such as accepting a proposal without testing the core technical claim.
- List the data, access, approvals or subject expertise the project required and determine whether those dependencies were available.
- Turn each failure point into a requirement, checkpoint or contractual protection for the next engagement.
Test whether the agency has real AI capability
Do not evaluate technical competence through terminology alone. Ask the team to show a relevant working system and then explain it. A legitimate demonstration should let you see the input, the processing path, the output and the limits. Ask what is performed by a model, what is deterministic software, what relies on third-party services and where human review enters the process. Rules-based automation is not inherently inferior; it is often the right tool. The problem is misrepresentation. If that distinction caused trouble previously, use a dedicated process for verifying a vendor’s actual AI capabilities.
The people presenting the proposal should also identify who will design, build, test and maintain the system. Ask those practitioners to discuss a comparable technical challenge, the trade-offs they made and the evidence that supported their decisions. Strong teams can explain architecture to both executives and technical reviewers. Weak ones retreat into jargon, refuse to expose assumptions or promise that every risk will disappear after development begins.
Give each shortlisted agency the same bounded scenario and ask for a proposed evaluation plan. You are not looking for identical architectures. You are testing whether the team asks disciplined questions, exposes assumptions and connects technical choices to the business result.
If machine learning is essential, make the vendor explain why it is necessary and how its performance will be evaluated. The questions in finding a vendor that delivers genuine machine learning can help you distinguish a justified model-based approach from ordinary automation wrapped in AI language.
Structure the engagement so evidence comes before expansion
Your next engagement should increase in scope only when the evidence supports it. Begin with discovery or a narrow pilot that addresses the riskiest assumption. Discovery should produce decisions and usable artifacts, not merely another presentation. Depending on the project, that may include a clarified workflow, data assessment, architecture, prototype, evaluation plan, risk register and delivery roadmap. Define the expected outputs before the work starts.
- Discovery
- Best when the business problem, available data or technical approach is still unclear. The output should reduce uncertainty enough to support a responsible build decision.
- Pilot
- Best when the core use case is known but feasibility or quality must be demonstrated in a controlled setting.
- Phased production build
- Appropriate after the central assumptions have been tested and each stage has objective acceptance criteria.
- Broad transformation program
- Reserve this for situations where leadership, process ownership, data readiness and technical feasibility are already established.
Milestones should correspond to accepted deliverables rather than calendar dates alone. “Development month two” does not establish value. “The agreed workflow operates against the test set and passes the documented review” does. Each milestone should specify what will be delivered, how your team can inspect it, what must be corrected and what decision follows. For another practical checklist, see how to vet an AI development agency after a failed engagement.
No responsible agency can eliminate every technical or commercial risk at the proposal stage. Ask the team to identify uncertainty plainly, test the highest-risk assumptions early and revise the plan when evidence changes.
Let’s talk about your business goals.
Protect ownership, access and operational continuity
A working solution is not enough if your organization cannot access, understand or continue operating it. Your agreement should state what you receive at every stage and what happens if the relationship ends. Cover access to your data, completed deliverables, documentation, relevant source materials, deployment environments and the records needed to understand technical decisions. Have qualified legal counsel adapt ownership, licensing, confidentiality and termination terms to your jurisdiction and project.
Pay particular attention to third-party and open-source components. Their presence is normal in modern software; representing them dishonestly is not. Ask for a component inventory, applicable licenses, modification details and a clear account of the agency’s original contribution. If this was a previous concern, review the questions for assessing an ethical AI developer’s use of open-source code. The goal is not to demand that every line be written from scratch. It is to understand what you are buying and what obligations accompany it.
- Use accounts and repositories your organization can access throughout the engagement.
- Receive documentation incrementally instead of waiting until the final handover.
- Record major architecture decisions, dependencies and known limitations.
- Define ownership and usage rights for data, deliverables and vendor-created materials in writing.
- Set a practical transition process for termination, replacement or internal handover.
- Schedule security, privacy and legal review according to the sensitivity of the system and its data.
Reference checks should focus on behavior under pressure. Ask former clients whether the agency disclosed problems promptly, maintained usable documentation, handled scope changes fairly and left the system in an operable condition. A partner’s response when assumptions fail tells you more than a flawless sales narrative. Broader guidance on avoiding agencies that overpromise can help turn those conversations into a consistent evaluation.
Score the partner, not the pitch
Use a written scorecard before final presentations begin. Otherwise, polished communication and personal chemistry can outweigh evidence. The scorecard should reflect your project’s risks, but it should assess at least problem understanding, relevant technical capability, delivery method, transparency, data and security discipline, commercial clarity and continuity planning. Require evaluators to record the evidence behind each score.
Watch for warning signs: guaranteed transformative results before data review; pressure to approve a large scope immediately; refusal to introduce the delivery team; vague answers about models, data or third-party services; demos that cannot accept a fresh input; and proposals that describe activities without defining outputs. A trustworthy partner will tolerate scrutiny because good due diligence improves the project. The companion guide on finding a legitimate AI development partner after a failed project provides another way to organize that search.
Suppose a business wants an AI assistant to handle customer inquiries. Instead of commissioning a broad conversational platform immediately, it can begin with a bounded set of information the business has reviewed and approved. The pilot can test whether the assistant answers representative questions accurately, routes uncertain cases appropriately and produces useful inquiry intelligence. The evaluation uses agreed examples and human review before the project expands. This scenario reflects the work OceSha Ventures does with conversational websites, customer inquiry handling and customer intelligence through Lumi, without assuming the same design is right for every organization.
Where Rohan Hall and OceSha Ventures fit
Rohan Hall is the founder and CEO of OceSha Ventures and its AI-first solutions. The company builds and operates course creation, branded academies, AI assistants such as Lumi and business intelligence solutions for businesses and organizations. Hall has built AI and blockchain systems, led technology for blockchain interoperability and scalable applications, and worked across the United States, Europe and Asia. That range matters because responsible AI delivery is rarely only a model problem; it also involves architecture, organizational knowledge, workflows and adoption.
OceSha’s offerings address specific parts of that landscape. OceSha AI’s course and academy creation platform supports AI-powered learning and knowledge distribution, while Lumi supports conversational websites using information the business has confirmed, customer inquiry handling and customer intelligence. These are defined applications, not a reason to force AI into every business problem. The right starting point remains a valuable use case with evidence that the proposed approach can deliver it.
Hall also examines artificial intelligence alongside blockchain, cryptocurrencies, neuromorphic technologies, cognitive intelligence and other emerging fields in The Convergence of AI and the Top 10 Emerging Technologies. The book is the appropriate next step for readers who want a broader understanding of how these technologies converge beyond the immediate task of hiring an agency.
If you are comparing partners after a particularly damaging engagement, use both the framework here and the more focused guide to vetting AI partners after a disastrous agency project. Manufacturing leaders can also consult the industry-specific questions for finding a trustworthy AI partner for a manufacturing business. In either case, insist on the same fundamentals: a defined outcome, direct technical evidence, staged commitment, inspectable progress and continuity protections.
Review Rohan Hall’s ventures, AI work, published book and current areas of focus.
Explore Rohan Hall’s workFrequently asked questions
Should I issue an RFP for the next AI project?
An RFP can help when your requirements and evaluation criteria are already clear. If the problem, data or technical approach remains uncertain, begin with a smaller discovery process. Otherwise, vendors will price and promise against assumptions that neither side has tested.
Who should participate in evaluating an AI agency?
Include the business owner of the outcome, people who understand the current workflow, a qualified technical reviewer and the stakeholders responsible for data, security, legal or compliance concerns. The exact group depends on the system’s risk and scope.
How can a nontechnical executive judge an AI demonstration?
Ask the vendor to use a fresh, representative input and explain what happens at each stage. Focus on whether the output serves the agreed workflow, how errors are detected, where human review applies and what dependencies affect performance. Use an independent technical reviewer for architecture and security claims.
Is a custom AI solution always better than an existing platform?
No. Existing platforms can reduce development time and operational burden when they fit the workflow. Custom development is justified when the organization has distinctive requirements, data or processes that available products cannot address adequately. Compare total operational fit, not the appeal of the word “custom.”
What if an agency refuses to provide source materials or architecture details?
Some proprietary methods may reasonably remain confidential, but you still need enough information to evaluate delivery, operate what you purchase and understand critical dependencies. Define required documentation, access, licensing and handover terms before signing. If the vendor cannot support responsible due diligence, choose another partner.
When should I stop a pilot rather than expand it?
Stop when the central assumption fails, the result cannot meet the agreed acceptance criteria, required data is unavailable, risks outweigh the expected value or the vendor repeatedly avoids transparent testing. A pilot that produces a well-supported decision not to proceed has still prevented a larger loss.
The bottom line
Do not try to recover from a failed AI project by finding a more persuasive agency. Find a more verifiable one. Define one valuable outcome, test the hardest assumption first and require a live demonstration of the central capability. Make scope expansion conditional on accepted evidence, while preserving access to your data, documentation and completed work. A credible partner will welcome this structure because it clarifies the problem and reduces avoidable risk for both sides. If a vendor resists objective evaluation or pressures you into a broad commitment, walk away.
Rohan Hall
Founder of OceSha Ventures · AI architect and author
Rohan Hall is a technology entrepreneur, AI architect and author with four decades of technology experience, now focused on practical AI across business, education, government and global impact. He founded OceSha Ventures, builds the OceSha AI platform and Lumi, and wrote The Convergence of AI and the Top 10 Emerging Technologies.
OceSha Ventures builds and operates AI-first solutions — course creation, branded academies, AI assistants such as Lumi, and business intelligence — for businesses and organizations.
Sources
About this page. Last reviewed .
It is based on his verified public professional record.
693012191f56a336f75eecfbe87a2b57