AI It Till You Make It: The Enterprise Evolution Of ‘Fake It Till You Make It’

Distinguished Engineer at a global bank, ambassador for Microsoft & The Linux Foundation, transforming the future one giant step at a time.

gettyFor years, professionals were told to “fake it till you make it”—a shorthand for projecting confidence until capability caught up. But we are now in a new era. This time, the AI era. Now we have started to say something more powerful:

This shift is about accelerating your existing capability through intelligent augmentation. In enterprise environments—particularly in finance, regulated industries and open-source ecosystems—this evolution is already reshaping how professionals ramp up, contribute and lead. I see this through FINOS, the open-source foundation I am heavily involved in. Originally an AI skeptic, I’m now fully turning toward AI use and building on the best of AI principles.

But like other technologies, it just accelerates capabilities; it would not try to replace competence. Working in a large enterprise, I see every day that learning curves are long. Regulatory complexity, legacy systems, layered governance structures and more are slowing down onboarding and innovation. AI suddenly changes this dynamic.

Today, engineering teams use AI to understand legacy codebases, generate refactoring proposals, draft migration plans from legacy orchestration platforms and produce missing documentation.

This reduces ramp-up time significantly. Professionals can contribute meaningfully earlier in their tenure.

In financial institutions, this is extended, as AI copilots help analysts draft first-pass regulatory summaries, run stress-testing simulations, analyze trade anomalies and produce documentation for complex risk models.

Many of these steps are part of Morphir, Legend, Waltz, Rune and other tools from FINOS and accessible as open source.

However, accelerated output does not automatically translate to deep understanding. That distinction is critical. Leaders must ensure that AI is used as a learning multiplier—not a substitute for reasoning, thinking or innovation.

Open-source participation, particularly in regulated industries, often requires familiarity with governance models, licensing structures and interoperability standards. AI is lowering this barrier, too, by enabling contributors to summarize issue histories across repositories, draft compliant pull requests, validate license compatibility and align proposals with foundation governance standards.

This expands participation and increases inclusion. It allows capable professionals to contribute earlier and more confidently and enables non-developers to contribute as well, as explained in the Open Source Readiness playbooks. But it also introduces new responsibilities for maintainers and foundations. Governance frameworks and open-source program offices must clarify expectations regarding AI-assisted contributions, intellectual ownership and accountability.

The conversation evolves further with the introduction of AI agents. Enterprises are deploying agents that monitor trading signals, reconcile operational discrepancies, trigger remediation workflows and coordinate cross-system orchestration.

Product leaders and architects can prototype agent-driven systems faster than ever. AI tools assist with architecture diagrams, workflow simulations and scenario testing. This enables faster experimentation and innovation, as explained in “The State of AI in 2016.” Yet in regulated industries, output must withstand audit, regulatory review and operational stress. AI-generated designs must be defensible, traceable and explainable. This is an important part of the “Innovation under Regulation” approach. Therefore, leaders should prioritize:

AI agents expand execution capability. They should not eliminate human accountability.

One emerging risk of “AI it till you make it” is what I call the “competence illusion”—see more on the topic in this World Economic Forum article. AI-assisted outputs often appear polished and authoritative. Strategy decks read well. Code compiles. Reports look complete. In enterprise environments, especially in finance, this surface-level polish can delay the detection of weak reasoning.

Organizations must evolve their evaluation models beyond output quality alone. Instead, leaders should ask:

• Can the individual explain the assumptions behind the result?

• Can they operate effectively if the tool is unavailable?

AI should amplify judgment and not conceal its absence or replace human intuition.

1. AI Literacy As Core Competency: AI fluency must extend beyond technical teams. Risk officers, product managers and executives should understand AI capabilities, limitations and governance implications.

2. Structured Human Oversight: Define clear human decision checkpoints for AI-assisted workflows. Particularly in regulated environments, accountability must remain explicit.

3. Learning-Oriented Usage Models: Encourage teams to use AI to understand systems, critique assumptions and explore alternatives—not just generate outputs.

4. Governance Frameworks For AI Contributions: In open source and internal platforms alike, clarify policies for AI-assisted code, documentation and architectural decisions.

The organizations that succeed will not be those that slow adoption out of fear, nor those that automate indiscriminately. They will be those that embed AI augmentation into capability-building processes.

“Fake it till you make it” relied on psychological resilience. “AI it till you make it” relies on structured augmentation.

The competitive advantage now belongs to professionals who can use AI to compress the distance between ambition and competence—while simultaneously deepening their own understanding. In enterprise environments, especially in finance and other regulated industries, trust remains the ultimate currency. AI can accelerate contribution, reduce onboarding friction and unlock broader participation.

But trust will continue to depend on judgment, accountability and explainability. AI can help professionals start sooner. Only disciplined leadership can ensure they truly make it.

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