As investors, we’re evaluating whether artificial intelligence is present in your product, and we’re also looking at the extent to which it is embedded in the DNA of your business strategy. Founders who understand how to integrate AI across their organisation – from operations to product development – signal greater strategic depth and a higher likelihood of long-term success.

Here are the three core questions every startup should expect investors to ask when evaluating their AI strategy.

Is AI embedded in the founding team’s thinking?

A few years ago, incorporating AI might have been seen as innovative. Today, it’s the baseline.

We assess AI as a marker of strategic maturity. It’s not about checking a box – it’s about how deeply AI informs your company’s roadmap, product architecture, and go-to-market execution.

Take B2B enterprise SaaS as an example. In this space – where Navigate Ventures is particularly active – AI is often essential for parsing incompatible datasets, extracting actionable insights, and enabling real-time decision-making. Without this capability, a company will struggle to compete.

We recently backed a logistics SaaS platform that uses AI to optimise supply chain operations in real time – predicting demand, flagging anomalies, and automating routing decisions. This wasn’t AI as an add-on. It was foundational to their product and value proposition.

If you’re serving sectors like manufacturing, logistics, or finance, your ability to ingest large volumes of data and deliver meaningful outputs – quickly and accurately – is table stakes. Anything less risks making your offering obsolete.

On the flip side, when we see companies simply layering predictive analytics onto a legacy platform, it suggests they’re trying to retrofit innovation instead of building it in. That’s a red flag.

Is AI driving efficiency and capital efficiency?

Strong startups are leveraging AI not only to innovate externally but to operate more efficiently internally. Investors are increasingly focused on how startups manage capital – particularly in today’s market environment where the gap between Series A and B continues to widen.

AI should be improving operational efficiency across the board – accelerating time-to-insight, reducing customer acquisition costs, automating backend workflows, and enhancing support systems. The startups that stand out are those using AI to extend runway, reduce burn, and become more agile in navigating uncertain markets. They’re executing better, and they’re buying themselves time in the process.

In contrast, a superficial or underdeveloped AI strategy suggests an inability to operate with the kind of lean discipline that today’s funding environment demands.

Is your team equipped to go the distance with AI?

AI is not a set-it-and-forget-it toolset. Its effectiveness is directly tied to the team’s ability to understand, implement, and continuously refine its use.

Putting AI tools in the hands of team members assumes a certain level of technical fluency, critical thinking, and willingness to experiment. Training and upskilling are essential. So is a candid assessment of whether your team has both the aptitude and appetite to engage meaningfully with AI in their roles.

Investors will want to know: Do your people understand the “why” behind AI initiatives, not just the “how”? Can they evolve alongside the technology?

Communicating your AI prowess to investors

Founders often ask, “Are investors looking for AI?” The answer is: we’re looking for intelligent use of AI that creates measurable business value. We want to see it working as a lever for product differentiation, operational leverage, and scalable insight.

In every pitch, we’re scanning for signs that the team understands the broader AI landscape – where it’s heading, and how they plan to stay ahead of it.

If you’re not using AI to enhance performance and scale intelligently, it will show up in your metrics, your customer traction, and ultimately, in your ability to raise capital.

A weak AI strategy doesn’t just make you less efficient – it makes you less investable. The bar is rising. Startups that fail to evolve risk being left behind.

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