Across dozens of client conversations this year, I’ve noticed a pattern: everyone has internalised the fail‑fast strategy yet only a handful of organisations actually practice it. Most enterprises and even many mid‑market firms still treat artificial intelligence initiatives like five-year ERP rollouts. Remember those waterfall plans, gated funding rounds, six‑month discovery projects? Meanwhile, the ground is moving, AI frontier models evolve every quarter, coding assistants cut development costs in half and young disruptors prototype on Friday what legacy vendors propose for Q4.
If you’re an AI consultant, venture partner or digital transformation lead, you can no longer rely on the traditional "assessment -> roadmap -> pilot -> release" conveyor belt. The game has changed from precision to pace, from certainty to optionality. In this post, I’ll lay out a consulting‑friendly, board-room‑palatable version of fail‑fast but I'd like to call it, Validate Fast. I'll show how to use agentic workflows, rapid prototyping and collaborative governance to deliver value (or disprove it) within two weeks.
Why the classic fail‑fast slogan rings hollow in board rooms
“Fail fast” started life in startup garages, where burning venture capital was an accepted rite of passage. But in corporate corridors, especially outside of tech hubs, this phrase still triggers CFO eye twitches. Leaders hear failure and imagine sunk costs, reputational damage and compliance nightmares. They forget the second half of the sentence …and learn faster.
Yet Andrew Ng's recent talk at Startup School makes an indisputable point: in AI‑driven markets, speed to insight beats perfect foresight. Even a single misunderstanding on architecture, latency budget or user needs can waste months. Conversely, a two-week prototype that disproves a bad assumption saves a year of dev time and seven figures in cloud spend.
So the first consulting trick is linguistic: re‑brand fail fast as “validate fast” (or “learn fast” if your client’s culture is particularly risk‑averse). The principle remains: make it cheap, quick, and psychologically safe to discard unpromising ideas.
The Validate Fast loop: four steps, two weeks
Here, I'd like to share a micro‑framework we've implemented at ITKnocks which is now being used with one of the State Government clients. It borrows heavily from Ng's theme of concrete ideas, agentic prototypes, rapid feedback, responsible acceleration. However, I've couched them in a service‑delivery rhythm that risk managers can easily sign off.
Frame fast (Day 1-2)
Vague goals like "optimise onboarding processes" may win applause but quickly create problems for engineers and consultants. Forcing executives to articulate a concrete user story such as "residents can apply for permits, pay rates, and book facilities, reducing in-person visits by 60% within two years and increasing customer satisfaction scores by 30%", guarantees the prototype sprint begins with clarity.
The deliverable of this short starting phase can be a single-page Problem Framing Canvas that states the business objective, user story and success metric including the data reality check.
Prototype fast (Day 3-12)
The deliverable for this can be an agent orchestrated demo in the client's cloud tenancy (Azure OpenAI, Vertex AI or equivalent) with stubbed authentication and an intentionally not-so-polished UI.
The main focus is to get the very basic stuff working by utilising the tools of today i.e. coding assistants, agentic frameworks such as Semantic Kernel, LangChain or even SaaS offerings like Foundry Agents etc.
Validate or kill (Day 13)
Success and "smart failures" are both wins. If the prototype misses the mark, you can celebrate the budget saved and pick a new use-case. This is not uncommon with the fast modern AI apps. Read the latest MIT findings here.
Deliverable for this step can be a 90-minute stakeholder demo, a scorecard against the original success metric and a red/amber/green decision.
Secure and scale
Once your prototype earns green status then you can harden code, integrate with the production APIs, wrap governance like privacy, prompt guardrails, model bias, evals and so on. This is when you start caring about the token-cost optimisation. No need before that!
The secret sauce
Agentic workflows can be a differentiating factor for what is working vs. not working. To the uninitiated, agentic orchestration sounds like a marketing gloss. In practice, it is a tangible architectural layer that lets language models think, plan, and retry instead of spitting out a single pass of text. Why does that matter for consultants?
Easy, the first reason is low data barriers. With one‑shot prompting, you often need pristine knowledge bases. Agentic loops can call retrieval, critique and self‑repair routines. This masks the gaps in source data and reduces upfront cleansing work. Secondly, it helps in easier stakeholder buy-in. You can run a demo where an agent outlines, researches, drafts, critiques and refines. It just feels magical and converts AI sceptics faster than a static dashboard ever could. Lastly, it helps with switching cost agility. If tomorrow’s open‑weight model outperforms GPT‑5 for your domain, swap it in behind the orchestration layer. Clients appreciate knowing they won’t be locked into a single vendor.
Shifting the bottleneck
Coding assistants have inverted the traditional project bottleneck. Earlier, when we used to wait weeks for a feature branch, we can now rebuild a whole codebase multiple times a month. The new lag sits in decision making i.e. getting the right people to test, give feedback and green light iterations.
During the 10-day prototype sprint, we can have iterative demos on the alternate days with the product owner and one frontline user (I know it's not easy but if you convince your client then that'd be a great win). This gives you a chance to demo the progress live, capture gut-level reactions and reprioritise the backlog instantly.
If the prototype doesn't involve any proprietary assets then I'd suggest you to take this unbranded piece of software to your network and try to get a few interested people in that domain. This will give you a real sense of your work with the real feedback.
By institutionalising feedback, you ensure that engineering pace translates into validated learning rather than churn. This way, micro-research yields insights faster than enterprise usability labs.
Responsible acceleration
Move fast and break things died for good reasons. In this age where privacy breaches, biased models and regulatory fines are everywhere. We need to move fast but also be responsible for our actions. I believe this can easily be done without throttling the momentum.
One way to do that is by putting a quick framework for privacy, fairness, security and model-drift (or bias) risk. If any box goes unticked, the decision maker can sign a waiver explaining the reason. This transparency replaces the red tape. The other way is to put guardrails via code (not just the policy docs). Specifically with the prompt-level filtering and monitoring hooks (like Evals), the prototype can meet the compliance requirements, right within the Git discussions instead of PowerPoint. Eventually, the kill-switch contract (where client can have one-click shutdown) can provide a legal comfort and speed up procurement.
Empower clients so they don't fear the black box
A frequent concern about rapid AI consulting is, “We’ll depend on you forever,” but the remedy lies in empowering clients with hands-on control. Workshops that let them adjust prompts, swap models and re-run evaluation pipelines themselves build stronger trust. Building on this, consultants can counteract gate-keeper regulation by promoting frontier model alternatives, so that clients remain future-proof and innovation stays democratised. Emphasise optional AI literacy paths, such as "everyone codes" bootcamps for marketing, finance, and HR staff. They don’t need to be developers, but learning even some code can turn corporate sceptics into citizen co-creators by showing them how to build simple automations with AI pair programmers.
The fail-fast ethos has graduated from a Silicon Valley slogan to management hygiene in the AI era. But in enterprises accustomed to certainty, it needs translation. That’s where Validate Fast shines. It preserves the spirit of fail-fast while softening the semantics and anchoring it in governance. By combining Andrew Ng’s emphasis on concrete ideas with agentic workflows, AI-accelerated coding and ruthless feedback loops, consultants can deliver results at market speed. The outcome is binary but always valuable either a green-lit prototype ready to harden or a dignified early sunset that frees budget and leadership attention for the next opportunity. Either way, the client wins and so does the consultancy that helped them learn faster than the competition.
Until next time.