Back in 2018, the scope of Tech DD began to shift. Cybersecurity rose to the top of every investor’s checklist. AI started to peek over the horizon. Then came the tech downturn, which forced companies to face uncomfortable truths about bloated teams, fragile processes, and the real cost of inefficiency.
Fast forward to today, and the role of Tech DD looks very different. It’s no longer just about code quality, scalability, and systems architecture. It’s about engineering efficiency, tooling leverage, and how quickly a team can adapt to a future we can’t quite define.
Doing More With Less
Elon Musk’s dramatic downsizing at Twitter sent shockwaves across the industry. Whether you agree with his approach or not, it proved one thing: engineering teams can lose headcount without losing technical delivery capability. Twitter didn’t collapse technically—but it did lose its brand aura. That distinction matters. Investors are now asking tougher questions about efficiency:
- How productive is the current team, really?
- What tools and processes amplify or waste their talent?
- Where could automation or AI remove bottlenecks?
Efficiency is no longer a back-office conversation—it’s a boardroom one. It has a direct relationship with value creation.
Data as the Enabler
Data has moved from being a hygiene factor to a strategic differentiator. It fuels automation, underpins AI initiatives, and gives firms the ability to scale intelligently. For Tech DD, this means assessing not just databases and pipelines, but also governance, ownership, and readiness for AI-driven leverage.
The AI Wildcard
Perhaps the biggest moving target is AI itself. The market is evolving faster than most diligence frameworks can keep up with. The ethos of ‘real’ AI centres around research, so expect your target firms to be undertaking projects that include expensive R&D projects that have no guaranteed outcome. The sort of risks you see in early-stage start-ups are appearing in more mature firms as they embark on modernisation.
A model or capability that looks leading-edge today could be obsolete tomorrow. The challenge isn’t just assessing what exists—it’s assessing adaptability. Can this company harness AI as it evolves, or will it be left behind?
Three Tips for Navigating Moving Goal Posts in Tech DD
- Track Efficiency, Not Headcount
Look beyond “how many engineers” and ask: how effective are they? What’s the deployment velocity, defect rates, and tooling adoption? Efficiency metrics tell you more than a simple cost-per-head. - Data and Value Creation
Don’t just check if the company has data—ask how usable, clean, and integrated it is. Data maturity today predicts how well a business can embrace tomorrow’s AI and automation. - Team’s Nimbleness
As AI is rewriting the rules quarterly, the winning firms are those that can pivot. During diligence, test the culture of experimentation, the speed of decision-making, and the ability to learn. A rigid org with good tech will get left behind; a nimble one with decent tech will catch up.
The goal posts in Tech DD will keep shifting. But that doesn’t make the job impossible. It means we need to evolve our frameworks, focus on adaptability, and ask questions that stretch beyond the present into the uncertain—but inevitable—future.
