Ahead of Nordic APIs Summit 2026, we catch up with speaker Claire Barrett on the mindset shift needed for adopting AI-enabled strategies.

“Imagine driving around an old town,” responds Claire Barrett when asked to describe what it means to be AI-ready from an integration perspective.

“Imagine a European city that’s been inhabited for thousands of years, and they’ve built roads and sewers and buildings all on top of each other. Parts of it are suitable for pedestrians to go up and down steps. You can’t drive around at speed in a Ferrari, let alone a road train, as you could on an American motorway that runs as a straight line between Point A and Point B.”

“If you’re working in your own space, with your own team, or you’re a micro-business of one, two, three, or four employees, where you control everything, including the data, and you don’t have regulators and people breathing down your neck, you can get a massive amount of integration productivity from AI very quickly.”

Since the launch of ChatGPT at the end of 2024, every organization and legacy software developer has been scrambling to figure out how to incorporate AI into their products and workflows, whether teams, the data, or all the processes were ready yet or not. AI evangelists like OpenAI’s Sam Altman see AI as ushering in a golden age of entrepreneurship, creating a new wave of one-person companies worth more than a billion dollars.

Others, like Microsoft’s Bill Gates, warn that AI could replace humans in the workplace for most things. Alarmist stories from the likes of Elon Musk even warn that the existence of AI increases the likelihood of human extinction by 20%.

Everyone has an opinion.

As with every significant technological advancement, humans both benefit and pay a price. In an AI-everywhere world, finding the balance between the scale and scope of opportunity, while keeping eyes wide open on the true risks and impact for the people and teams involved, is where the best decision-making happens.

What AI-Ready Means For Barrett

This comes as no surprise to Claire Barrett, whose career as an API economist, tech leader, and consultant spanning several decades across four continents has always worked in the white space between strategy and execution, and between technology solutions and human impacts.

It’s one of the main points she’ll be discussing in her upcoming presentation for this year’s Platform Summit, “Why Being “AI-Ready” Is as Much Psychology as Technology,” where she’ll explore the importance of shifting your mindset when adopting AI-enabled strategies and integration, and the dangers of ignoring the human factors.

“It’s no different than any other new or changing technology, just on steroids,” answers Barrett when asked if she feels AI is as revolutionary as advertised. “The latest model or the latest tool or the latest AI feature in one of your applications might allow you to be far more productive, but without judgement and pragmatism, can waste time or even do more harm than good. If you’re automating the wrong process, it’s just going to do the wrong process more quickly.”

Where AI Adoption Runs Amok

When asked about the dangers of adopting AI-enabled integration without an AI-ready mindset, she adopts the invasive species metaphor from an article she wrote earlier this summer, “Less Haste, More Speed: Rethinking AI Adoption Before It Goes Feral“. She starts with the seemingly benign example of Google Search as a well-intentioned technology that ran amok.

“Google Search was incredibly useful for people to find stuff that they never had access to before, but it can also send you down a rabbit hole that’s inappropriate for you and either gets you into thinking that you’ve got some horrible disease that you don’t, or seeing things that are age-inappropriate,” says Barrett. “There’s a huge amount of dark stuff that the internet was never designed for.”

She goes on to use the example of invasive species in Australia, where she lived for many years, to illustrate the dangers of adopting AI without proper forethought. In the early 20th century, Australian scientists introduced toads to limit a beetle infestation in sugar cane crops, but the toads never effectively limited the beetle population, and they themselves thrived, becoming a new form of pest and disrupting the broader ecosystem.

It’s a cautionary parable for using AI without anticipating its long-term effects and how it fits into a larger, evolving system. “If the people who are making these spontaneous decisions for one intention are missing discipline and an understanding of others, they may be unaware of their blind spots,” adds Barrett.

Think Like a Naturalist to Use AI Safely

To use AI safely and securely, we need to think like naturalists and recognize the evolution of systems, agents, and wider ecosystems as AI adoption becomes more pervasive. This will call for us to spend more time than ever problem-solving and horizon watching with people and teams outside our traditional and familiar disciplines.

Although AI may just be the latest example of a breathless tech hype cycle, there are some things that are unique to this particular moment. “Whatever it is, it’s much more pervasive and just faster, and it’s having a significant impact. But then also there are some things that are very similar. I think it’s the degree at which it can be adopted, and everybody can have a go. It flattened the whole kind of tech-to-non-tech debate, which is good… But many people are feeling they don’t have time to catch up.”

The risks associated with cybersecurity, data compliance, and privacy have never been more heightened. Arguably, with the ease of access to agentic tools and AI-assisted development now in the hands of many people less traditionally “technical,” the question of how to manage API-related governance has never been more important to answer.

“When APIs were seen as the realm of technologists,” Barrett answers, when asked how AI adoption compares to APIs going mainstream, “API governance guidelines and standards could be opaque to commercial colleagues and teams. This is challenged, for example, when a new community of API consumers (or their agents) are expected to follow the enterprise minimum standards for integration safety and performance.”

The sheer scope of the hype cycle, alone, makes AI different. “The pace at which new versions, new models, new tools are coming out is extraordinary. It’s on steroids and already having an impact on things as simple as how the underlying cost models are changing, and so how people manage their IT budgets.”

AI Pushes All Areas To Evolve

AI’s innovation is also causing certain things to stay the same, however. The sheer amount of vendor noise, paired with the absolute flood of new AI-driven tools, requires adopting time-honored practices like the need for testing or having well thought out business practices.

“Some of this is applying the disciplines and mindset that have powered the tech industry’s growth since the start of the century. The practices of good digital product design, lean startup, experimentation, testing, and pivoting with a small, select group of people are all fundamental to AI adoption. On steroids.”