Building businesses in the age of AI
For many companies, and especially companies for whom software is essential for value creation, the rapid rise of AI presents both an opportunity and a challenge.
This is true even for AI-native startups. In times of disruptive change, the initial instinctive reaction is often to fixate on ‘how does this change how I create value?’. But, rather than trying to predict where AI will impact and to what extent (a major challenge for even the most informed experts), a potentially more productive question to ask is ‘what remains constant during such change, and will it enable me to create even more value?’
AI is the next step in a long evolution of software: from mainframes, to client-server, to cloud and mobile, and now to solutions that build on top of that infrastructure and data to unlock the development and deployment of AI at scale. But unlike the steps that preceded it, this one feels less like an evolution and more like a leap. And yet, even in rapidly changing and ultimately uncertain landscapes, certain convictions drawn from first principles thinking about what to build and how to build hold firm.
AI represents a profound opportunity for challenger companies whether a new breed of AI-native startups, or companies built before the current AI wave that run on modern, agile tech stacks and are led by visionary, customer-centric leaders who know how to shape innovation into scalable and sustainable business. It is precisely these teams that tend to deliver particularly well against long-term value creation, in both what they build and how they build and operate it.
Certain drivers of value creation in business model design have proven remarkably durable, transcending successive paradigm shifts in software. These drivers reflect the underlying reasons why customers choose a solution, stay with it, and expand their use of it, regardless of which technology layer changes beneath them. The primary value creation drivers (often referred to as competitive moats) are:
- Ownership of complex workflows
- Ownership of proprietary data
- The trust a solution earns with its users and other stakeholders
- Ecosystems that are built around a software and its resulting network effects
- Controlling distribution
Companies that build their business models around these moats have historically been better positioned to harness technology shifts and achieve sustainable success. It is plausible that the same logic applies now, making these drivers a useful compass for business model design in the age of AI. The following section revisits these competitive moats and provides an example of each from our portfolio of companies.
1. Ownership in complex workflows
Software that orchestrates complex, multi-stakeholder, multi-system workflows is structurally harder to replace. When a single workflow touches multiple departments, systems, and operators, customers require high reliability and accuracy. That kind of workflow knowledge, embedded in a platform that hundreds or thousands of operators rely on every day, compounds over time and becomes increasingly valuable as the platform grows. Complex workflows are costly to learn and even costlier to replace and organisations and their people rarely want to start over.
2. Ownership of proprietary data
Companies that accumulate proprietary data such as expert-labelled outcomes and real world feedback loops build an advantage that compounds over time and is hard to replicate from the outside. What makes this especially powerful is knowing not just that a decision was made, but what actually happened as a result. That kind of outcome data is rare, and a new entrant simply cannot conjure it.
3. Trust a solution earns from its users and other stakeholders
In complex industries, especially in mission critical and highly regulated ones such as healthcare, compliance and certifications are key to obtain customer trust and are among the hardest things for new entrants to replicate. This trust, from customers and regulators, requires years of real-world operation.
4. Ecosystems that are built around a software and its resulting network effects
Digital platforms tend to become more valuable as more participants join, whether those participants are users, developers, or in future even bots (so called AI agents). True network effects happen when more usage attracts more activity and contribution, and more contributions make the platform more useful for users. Ecosystem depth reinforces this. Once a customer has wired its adjacent systems and workflows around the platform, the platform becomes the connective tissue across the entire stack.
5. Controlling distribution
Companies with established customer bases, embedded distribution, and the scale to launch additional products faster than new single-product challengers can have a structural advantage that new entrants must spend time building first. A good product with great distribution may well beat a great product with weak distribution, and as AI lowers the cost of building new products, distribution becomes more valuable as bottlenecks may shift from product development to customer acquisition.
In concluding on the above, it is clear that successful challenger companies share some common DNA and mindsets. Firstly, they have a strong and unrelenting customer focus and, secondly, they strive constantly for operational improvement and advantage. Such companies will typically embrace AI precisely because they have more to gain than to lose.
1. Customer focus
In challenger companies, customer centricity tends to be deeply embedded in the culture and felt across the entire organisation. AI sharpens that edge. Those who truly understand their customers and embrace a ‘working backwards from the customer’ mindset are now able to ship new features faster, personalise experiences at scale, and close the gap between what a customer wants and what the software delivers, more efficiently and effectively than ever before.
2. Operational improvement
Challenger companies refuse to accept that the current way of doing things is good enough. They constantly question, challenge, and look for better ways to operate. Teams that were already pushing for efficiency gains will find AI to be a genuine multiplier for operational improvement: lower cost-to-serve, faster internal workflows, and human intelligence freed up for the work that benefits the most from it.
To benefit from the latest wave of technological change, companies will need to discern the signals in the noise and set clear and achievable priorities accordingly. Not all competitive moats may have moved as drastically as one may think, but the ways of broadening and deepening them have. The degree to which AI defines, shapes or enables business models varies – understanding that, moving quickly, and keeping the customer front of mind will define success. Now is the time to build the foundations that will continue to drive customer adoption, satisfaction and trust over time.
