AI adoption rates are soaring, with generative tools and agentic infrastructures delivering transformational capabilities across all industries. The pace at which adoption is moving means that enterprises can no longer afford to approach AI in silos. Instead, it’s time for a strategic rethink – one that’s anchored in API governance and an AI-ready mindset.
Accelerating adoption
We talked recently about how AI has reached an inflection point, with enterprises now able to go down a closed ecosystem route or take a more modular approach, courtesy of the AI value chain. This value chain is unlocking next-level AI tooling possibilities, notching up the pressure on enterprises to move beyond strategizing to action in embracing AI initiatives.
In the past two years, the use of generative AI has surged, resulting in AI-driven automation, efficiencies, personalization, innovation, and advancements. As tools have become more user-friendly, the use of AI has become mainstream, with businesses using it to power everything from customer experiences to enhanced decision-making.
The figures speak for themselves. Gartner tells us that by next year (2026), over 80% of enterprises will have used generative AI APIs or models, or deployed GenAI-enabled applications in production environments. That’s up from under 5% in 2023.
A significant element to consider as part of this large-scale adoption effort is that it’s not all human-driven. Another Gartner projection for next year is that over 30% of the increase in demand for APIs will come from AI and tools using large language models (LLMs). APIs are hugely relevant to secure and scalable AI adoption, as we recently explored.
However, BCG points out that this spiralling usage isn’t always smooth sailing, with 74% of companies struggling to achieve and scale value when adopting AI.
Stumbling blocks
There are many reasons why enterprise AI strategies fail. In some cases, shadow AI has already run rife, with individuals and teams using tools that haven’t been approved, with data those tools have not been authorized to access. In other cases, a lack of suitably managed API infrastructure leads to tool fragmentation, with disconnects between systems, apps, and data leading to unexpected or misinformed outcomes.
Governance gaps are a problem that many enterprises also face, with siloed AI implementations lacking that all-important centralized oversight that delivers standardized security, reliable observability, and so much more. A lack of understanding about how to move effectively from proof of concept to production is yet another challenge resulting from the rapid pace of AI adoption.
Rethinking your AI strategy
As we said at the outset, it’s time for a strategic rethink – one that puts AI front and center of future business strategy. That doesn’t mean throwing endless money and time at developing AI initiatives in silos; it means focusing on the crucial elements of an AI-first approach.
A future-facing AI-first strategy has three core elements:
- Integrated API management: This is your first crucial step to AI readiness. APIs feed data to LLMs, so unless you manage your APIs in a way that focuses on consistency, security, and trust, you’re in trouble.
- Secure workflows: Robust identity and access controls need to secure the flow of data between systems, models, and users. These need to scale with your AI use cases, ensuring auditability and encryption as you grow while also supporting the agility and flexibility required for ongoing innovation.
- Composability: Build a modular architecture that lets you experiment and change direction at pace. By building flexibility into your services, tools, and data sources, you can assemble, reassemble, and replace as needed to keep up with changing use cases and new tech breakthroughs. You can also maintain control over your costs – a capability that was fundamental to our design of Tyk AI Studio.
Rethinking your AI strategy with these at the core moves away from individual teams trialing specific projects to an enterprise-wide approach. It’s a shift in mindset that acknowledges the importance of AI readiness not as something for the future but as an urgent current priority. With adoption now so widespread, the opportunity cost of delay is becoming too great to justify any other approach.
Get started today by downloading our free ebook, AI, APIs, and AI readiness, to discover your strategic blueprint to enterprise AI success.
