Companies worldwide are exploring how to integrate artificial intelligence into their operations. Most are taking incremental steps, using AI to enhance existing systems and processes. A smaller group, however, is pursuing an ‘AI-first’ model, rebuilding their businesses around AI from the ground up. This approach carries greater risk but also the possibility of reshaping markets entirely.
Lessons from Earlier Disruptions
Technology shifts have repeatedly caught incumbents off guard. When the internet first emerged, many businesses dismissed online models as impractical. Retailers, publishers, and video rental chains that failed to adapt lost out to younger, more flexible competitors. AI is creating a similar moment of reckoning.
Disruptive technologies often start with shortcomings. They may cost more, perform inconsistently, or meet resistance from customers. Over time, however, they improve and surpass the systems they replace. Unlike previous disruptions, AI is widely accessible, and startups can build products quickly with open-source models and cloud infrastructure, eroding traditional advantages held by established players.
Defining AI-First
An AI-first business assumes that tasks requiring professional expertise, such as writing code, handling customer inquiries, or producing content, can be designed around AI from the start. Human experts step in only where the technology falls short. By contrast, the gradual approach common among larger organizations focuses on integrating AI into specific steps of existing processes without rethinking the foundation.
For companies with extensive legacy systems, a sudden shift is rarely practical. The operational, cultural, and financial changes required are considerable. Still, the potential payoff is significant. Firms that embraced online channels early in the 1990s, such as discount brokerages, turned early risk-taking into decades of market leadership.
Risk and Potential
AI-first strategies can unsettle customers, disrupt employee roles, and demand large upfront investments. Yet they can also enable unprecedented efficiency and scale. Lean startups have already demonstrated this: some firms generate hundreds of millions in revenue with teams smaller than twenty people.
This dynamic places CIOs at the center of decision-making. They must assess which technologies are mature enough to deploy, identify areas where AI can deliver measurable value, and guide their organizations through fundamental changes in process and infrastructure.
Building Capabilities
For AI adoption to succeed, employees must see it as an enabler rather than a threat. Training and cultural adaptation are as important as software investments. Infrastructure also needs attention: data pipelines, application layers, and operating models must often be redesigned before AI systems can scale reliably.
Securing investment remains challenging. Many organizations lack metrics to measure productivity gains, which makes it harder to demonstrate impact. CIOs must establish frameworks that prove value, generate quick wins, and justify further funding.
The Path Forward
Not every organization needs to become fully AI-first. Incremental adoption may suffice in industries less exposed to automation. But companies built around specialized expertise face higher risks if they move too slowly. Focusing only on cost savings is unlikely to provide a lasting advantage, since competitors can replicate those efficiencies quickly.
The greater opportunity lies in rethinking business models entirely. For CIOs, the decision is whether to cautiously optimize what exists or take bold steps that could position their companies as the next wave of industry leaders.
