AI can transform your business, redefining everything from how you operate to the products you create. It is, of course, far from being risk-free. There are clear challenges that your enterprise will need to solve before you can scale AI to its maximum potential. From shadow AI to agentic complexity, let us run you through some of the key challenges of enterprise AI readiness and how you can solve them. Read on for practical steps for building resilient, responsible systems that ensure you can achieve maximum return on your AI investment.
The cost of complexity
It’s likely that your enterprise is already dealing with AI complexity in various forms. Team members using AI tools that you haven’t authorized is an issue for many businesses right now. Tyk CEO Martin Buhr talked about the dangers of this kind of shadow AI recently in terms of leaking proprietary data, pointing out how easy it is for individuals to put your business data at risk:
“Companies are struggling with shadow AI. Employees are using unauthorized AI tools and exposing these organizations to data leaks. Suddenly, some document that was optimized with an AI tool becomes fodder for a recommendation engine on someone else’s integrated development environment.”
One of the problems is that AI is moving so fast in terms of tool development. Teams are, naturally, keen to try new tools that can boost productivity and optimize their workflows. But you need a clear and firm approach to how they can do so within your enterprise. Otherwise, shadow AI and fragmented tooling is putting your data at risk – as well as making it harder to scale. You can solve this by offering your own approved pool of tools (with appropriate training), having a clear process for individuals to suggest tools to add, and ensuring you have effective oversight of what is being used, where, and why.
The complexity that a fragmented approach to AI tooling and implementation brings can be costly. Inefficiencies, governance gaps, unoptimized processes, and more can all reduce your ability to operate effectively, as can a lack of visibility. When something goes wrong, fast, effective troubleshooting relies on the quality of your observability. If you can’t see what’s happening, finding and then fixing an issue becomes infinitely harder.
AI implementations aren’t cheap, but you can reduce the cost by cutting through the complexity. An AI governance solution, such as Tyk AI Studio, can help you monitor, control, and optimize your costs effectively.
Why oversight, observability, and security are non-negotiable for agentic AI
The growing use of agentic AI is adding to the potential cost of complexity. Agentic systems involve AI agents that have the autonomy to make decisions about how to achieve their goals. In simple terms, you set them tasks, then they achieve those tasks in whatever manner they deem best, using APIs to access the data they need and potentially assigning tasks to other agents.
This is an incredibly powerful approach but one that also requires robust oversight, observability, and security. We’ve already seen countless examples of customers using prompt hacking to achieve unexpected results from AI-powered chatbots. A Chevrolet customer, for example, prompted the firm’s chatbot to give them a legally binding offer to buy a car for $1. Chatbots can also go against company policy. An Air Canada customer took the firm to court after its chatbot gave him false information and caused him to pay a higher fare than he should have done. Air Canada claimed its chatbot was responsible for its own actions; the judge disagreed.
These examples show the problems that generative AI chatbots can cause when they don’t have sufficient oversight, observability, or security. Consider the vastly greater degree of autonomy that AI agents have when making decisions about how to do things, and the need for robust oversight, observability, and security becomes strikingly clear.
API-first thinking and the move toward machine-consumable interfaces
This is where API-first thinking comes to the fore. With an API-first approach, your enterprise can create modular, interoperable APIs that combine to deliver a platform with all the oversight, observability, and security you need baked in. Those APIs are what feed your LLMs and what your AI agents use to access the data and systems they decide they need. As such, to govern, secure, and observe your AI appropriately, you need to govern, secure, and observe your APIs.
As part of this, it’s crucial to consider that you’re not just designing APIs for human consumers. Gartner tells us that 30% of the increase in demand for APIs by 2026 will come from AI and tools using LLMs. This means you need to be designing machine-consumable APIs. Clarity, structure, and enforcement are at the core of doing so; overly complex, noisy, or inconsistent specs won’t make the grade. This is key to ensuring your APIs support your AI readiness goals and deliver the clarity you need to scale, instead of layering on additional complexity.
Structuring AI adoption around portals, gateways, and centralized oversight
Using portals and gateways with centralized oversight can address your enterprise AI challenges before you scale.
With an AI portal, you can create a curated, unified AI service catalog. Both AI agents and human developers can access it, supporting them to build, achieve, and innovate at pace using approved tooling. All with centralized oversight and governance in place. AI portals can integrate with both internal systems and external workflows, so you can use them in whatever way best suits your enterprise’s needs.
An AI gateway gives you a single point of control. You can implement security policies at the gateway layer, then seamlessly connect to AI models and tools, proxy to LLMs, and integrate custom data models and tools. All with that robust security in place.
Proxying LLM traffic through your AI gateway delivers not just security and control but visibility. It supports a centralized approach to oversight as you build observability into your AI systems and processes. That oversight is fundamental to your ability to scale.
Introducing your AI ethics policy: What to cover and why it matters
We’ve looked at technical challenges you must solve above, but AI also raises ethical questions. From lack of transparency to bias in decision making, which can impact what your AI agents choose to do, your ethics policy will need to address a range of issues. UNESCO’s Recommendation on the Ethics of AI provides an excellent framework for developing your AI ethics policy. You can explore this – along with a huge range of other useful information on becoming AI ready – in Tyk’s free, downloadable resource: AI, APIs, and AI readiness ebook.
