The 1.5 Billion Dollars Question – Why Top Boards Still Miss What's Hidden in Their AI Strategy
- Brainz Magazine
- 7 minutes ago
- 8 min read
Prince Adenola Adegbesan is an Amazon best-selling author, legal strategist, and an AI-powered Business Innovator whose book, The Legal Lifeline of Global Businesses in the post-Pandemic Era, has been translated into six languages. As Founder & CEO of InspireCraft Global Limited and architect of RecovCart, he combines.
In 2024, Builder.ai was untouchable. Backed by SoftBank and Qatar Investment Authority, the platform embodied everything the venture capital world wanted to believe about artificial intelligence. No more manual coding. No more bottlenecks. Just elegant automation building applications with the simplicity of ordering pizza.
The founder, Sachin Dev Duggal, became the voice of this future. Confident. Visionary. Unreachable.
The valuation: $1.5 billion
Then something happened that no one saw coming, except perhaps a few people inside the organization who were too junior to speak up or too afraid of being wrong.
Revenue figures were restated. Within weeks, the entire edifice began to unravel. By early 2025, the CEO stepped down. Investors went quiet. The story that had seemed so inevitable suddenly looked like a cautionary tale.
But here's the part nobody talks about. Builder.ai didn't fail because AI failed. It failed because something else failed first.
It failed because the people who were supposed to verify what the technology actually did, the board, the CFO, the audit function, did not ask the right questions until it was too late. They did not insist on transparency. They did not demand verification. They accepted a narrative because the narrative was compelling.
They accepted a story instead of demanding a structure. And that distinction matters far more than you might think.
Imagine this conversation happening right now in a boardroom somewhere in London, New York, or Singapore.
The CTO presents the AI initiative. Growth projections are ambitious. The use cases are compelling. The technology is, admittedly, impressive. Board members nod.
Then one director asks the question that should not be difficult, but somehow always is. "How do we verify this is working as promised? Who is responsible if it isn't? And what happens to the business if we are wrong?"
Silence. Not because people do not have answers. But because the answers, if given honestly, would reveal something uncomfortable. Nobody has really thought about it. This is not rare. It is the default state.
We have spent the last 18 months conducting detailed governance assessments across the financial services, healthcare, technology, and public sectors. Organizations with sophisticated boards. Organizations with serious audit functions. Organizations that pride themselves on rigor.
And every single one has had the same blind spot, not in their AI capability. Their governance infrastructure is where the vulnerability lives.
They know what the AI can do. What they do not know, what they are actively avoiding asking, is this. What is the AI actually doing? Who verifies it? And when the story we are telling does not match the reality the system is producing, who catches it before investors do?
The findings, across organizations, were consistent enough to be unsettling.
When organizations deploy artificial intelligence without governance infrastructure, they are typically operating with invisible costs in three categories:
First: unrecovered or unrealized value, between $2.5M and $50M+. Not lost to bad decisions. Lost to the absence of the structure that would have made good decisions possible. Customer relationships that should have been reactivated but were not, because nobody had a system to identify which customers were worth reactivating. Revenue opportunities that should have been obvious but were not, because the data existed, but the governance did not. Cost optimization that could have been straightforward but was not, because accountability had never been clarified.
Second: compliance exposure that has been quantified. When organizations deploy AI systems to make consequential decisions, lending decisions, hiring recommendations, customer segmentation, resource allocation, the liability is real. We found organizations running AI-driven systems that violated their own compliance policies, regulatory requirements, or emerging legislation. Not because the AI was inherently unethical. But because nobody had built verification protocols to check.
Third: strategic drift. Thirty to forty percent of technology budgets going toward AI initiatives that had never been clearly mapped to organizational strategy. Not fraud. Not poor execution. Just the default consequence of deploying emerging technology without forcing the hard question. Why does this matter to what we actually do?
The deeper insight. These were not technology problems. They were governance problems. And that distinction is crucial.
Technology problems have technology solutions. Governance problems require structural change.
This is where conventional wisdom fails most organizations.
The assumption is that governance slows things down. That building verification structures, defining accountability, documenting decisions, all of this creates friction. Board oversight becomes committee bloat. Policy becomes bureaucracy. Speed becomes impossible.
What we actually observed was the opposite. The organizations that moved fastest with AI were the ones that had invested in governance first. Why? Because governance eliminates the hidden delays. The rework. The reversals. The moments when executives discover that what the AI is producing does not match what they thought it would. The crisis meetings when compliance finds an issue. The strategic pivots when leadership realizes an AI initiative does not actually move the needle on what matters.
Speed without governance is not speed. It is forward motion that ends in expensive corrections. Governance is speed, just with confidence attached.
Truth 1: Verification isn't paranoia, it's accountability
Builder.ai's board did not have a verification problem. They had a structure problem.
Nobody had created a mechanism to verify that what the founder was claiming matched what the system was actually producing, not out of distrust, but because verification structures did not exist. Governance was not embedded into the operational rhythm of how decisions got made.
This is fixable. And in organizations that do it, the benefits compound.
Decisions get made faster because leaders have confidence in the data. Surprises get caught earlier because verification is continuous, not crisis-driven. Strategy gets executed better because measurement is built into the process, not added as an afterthought.
Truth 2: Policy that works protects while enabling
Most organizations think of governance policy as a set of constraints. Do not do this. Document that. Get approval here.
What we have observed in organizations with mature governance is the opposite. A policy that is well designed becomes an enabler.
A clear policy about how AI decisions get made actually means teams can move faster, because they know what is expected. Clear accountability means leaders can delegate with confidence. Clear documentation means regulators do not question, they trust.
The organizations we have worked with report forty to fifty percent faster time to value on AI initiatives once governance is embedded. Why? Because teams move with clarity rather than caution.
Truth 3: AI value lives in the gaps
Here is something that surprised us. The governance audit process itself becomes a source of strategic value.
When organizations map out what they are actually doing with AI, they often discover things they did not know were there. Revenue opportunities hidden by process blindspots. Cost reductions that become obvious once accountability is clear. Customer insights that were invisible until data governance made the data actionable.
We have seen organizations identify anywhere from $1M to $2M+ in hidden value simply through the process of asking. Where is AI being deployed? How is it being verified? Are we capturing all the value it is creating?
From observation, this is what gets built:
Clarity. Not compliance theater. Real clarity about what AI is actually being deployed, how it is being verified, who is accountable when something goes wrong, and whether it is moving the needle on what the organization actually cares about.
Verification protocols. Built-in mechanisms to test that AI systems are producing promised outcomes. Not post hoc auditing. Real, continuous verification. Audit trails that leave no ambiguity about what happened and why.
Accountability structure. Clear lines of responsibility. Not diffused accountability. Not "the team is responsible." Specific people making specific decisions, with documented reasoning and clear escalation paths when something diverges from expectation.
Strategic alignment. Measurement tied to organizational mission, not just technology metrics. Does this AI initiative move the needle on revenue, cost, risk, or strategy? If the answer is unclear, the initiative probably should not be approved.
Regulatory readiness. Governance built for the regulatory environment that is coming, not just for compliance that is required today. The EU AI Act goes live in 2025. U.S. regulations are coming. Organizations that are preparing now will adapt faster than those that wait for mandates.
This requires discipline. Rigor. A willingness to ask uncomfortable questions. But it is not revolutionary. It is structural.
For CEOs, board members, and policy leaders, this is clear. The organizations that will lead in AI are not those that adopt AI first. They are the ones that govern it best.Builder.ai's collapse should serve as a catalyst, not a shock.
The question facing your organization is not whether to deploy AI. It is how to deploy AI in a way that survives regulatory scrutiny, board oversight, and the inevitable moment when reality diverges from the narrative.
Before your next AI initiative goes live, ask yourself:
Can we verify this is working as promised? Not theoretically. In practice. With evidence. With confidence.
Who is responsible if it is not? Not a department. A person. Specific accountability.
What changes if we are wrong? And do we have a structure to catch it early rather than late?
Is this moving the needle on what we actually care about? Strategy first. Technology second.
These are not difficult questions. They are just questions that require structure to answer well.
The organizations getting this right are moving with speed and confidence. They are not slowed by governance. They are enabled by it.
The organizations getting this wrong are moving fast into uncertainty, and discovering too late that the structure they did not build was the thing they needed most.
About InspireCraft Global Limited
InspireCraft Global Limited is a governance and strategic AI transformation firm led by Adenola Adegbesan, an internationally recognized executive bringing together two decades of legal mastery, compliance expertise, AI strategy, and proven business leadership.
Adenola's work spans governance architecture for organizations deploying AI at scale, designing verification protocols, accountability structures, and compliance frameworks that survive regulatory evolution. Transformation implementation across regulated sectors (financial services, healthcare, public sector), where governance is both risk mitigation and a competitive advantage. Value extraction through systematic analysis of organizational processes, identifying hidden revenue, cost reduction, and strategic optimization opportunities often worth $1M to $2M+ per engagement. And board-level guidance on AI strategy, risk, and opportunity, translating emerging technology into coherent organizational capability.
InspireCraft does not advise from a distance. We embed ourselves in organizational structures, working with boards, C-suites, and operational teams to ensure that governance becomes practice, not paperwork.
If you are leading an organization deploying AI, you face a choice. You can move fast and assume that the structure will take care of itself. You can accept narratives about what your AI systems are doing without demanding verification. You can delegate accountability and hope that alignment happens naturally. Or you can build the structure first.
The organizations that are choosing the latter are moving with confidence. They are not delayed by governance. They are accelerated by it. They are not constrained by verification. They are liberated by it.
Because they know something that Builder.ai's board learned too late, structure is not an obstacle to progress. It is the foundation that makes progress sustainable.
For boards, CEOs, and policy leaders serious about AI strategy that actually works, this is your moment to ask the questions that matter.
Contact InspireCraft Global Limited to discuss your organization's AI governance readiness.
Read more from Prince Adenola Adegbesan
Prince Adenola Adegbesan, Global Business Strategist & AI Innovation Leader
Adenola Adegbesan is an Amazon bestselling author, legal strategist, and AI-powered business innovator whose book The Legal Lifeline of Global Businesses has been translated into six languages. As Founder & CEO of InspireCraft Global Limited and architect of REcovCArt, he combines deep legal expertise with cutting-edge technology to empower SMEs globally. He combines comprehensive qualifications(Law Degree, MBA, Chartered Secretary, BIDA, FMVA) with real estate and financial expertise to deliver transformational business solutions. His leadership extends business through cross-continent mentoring initiatives spanning the UK, South Africa, and Nigeria, consistently turning adversity into opportunity for hundreds of individuals.
