Taking AI into production means moving beyond a proof of concept or pilot and deploying an AI solution that people rely on in day-to-day operations. It involves much more than proving the technology works. Organisations also need to consider governance, monitoring, human oversight, operating costs, security, scalability and how the solution will be managed over time.
For decades, technology teams have built and refined ways of delivering enterprise technology.
Whether it's ERP and CRM platforms, cloud infrastructure, custom applications, software development or data environments, we've developed approaches to planning, architecture, development, governance, testing, deployment and ongoing operations. Those disciplines, and the best practices they've produced, have been refined through thousands of iterations.
It's only natural that we're bringing much of that thinking into the development and deployment of agentic AI solutions.
The more work we do taking AI into production, the more we're realising it doesn't fit as neatly within those tried and tested approaches. The disciplines we've built over decades remain essential, but taking AI into production is exposing where they need to evolve.
That's where many organisations are finding themselves today.
Taking AI into production changes the relationship between technology and people
One of the first things we've discovered is that taking AI into production blurs boundaries that used to be quite clear.
In most enterprise technology projects, it's relatively obvious where the system ends and people take over. Technology follows defined rules, while people deal with judgement, ambiguity and the unexpected.
As soon as you start designing an AI solution for production, you're also deciding where human oversight sits, what the agent should be trusted to do, how it behaves when it encounters uncertainty, and what evidence it needs before taking on greater responsibility.
Those questions are no longer answered after deployment. They influence the design of the solution from the very beginning.
Taking AI into production isn't simply asking technology teams to build something new. It's asking them to rethink the relationship between technology, people and the work they do together.
What production teaches you
A proof of concept can show whether AI can do something useful.
That matters, but it's only one dimension of the decision. Many organisations are now looking across teams and processes, identifying where AI could remove manual effort, speed up work, create insight or support better decisions.
The harder part is that the most influential factor in deciding what should move forward is often not visible at the proof-of-concept stage. Two AI ideas can look equally attractive on paper and both might demonstrate value in a pilot. Once you start looking at what it takes to operate them safely, reliably and economically, however, they can become very different propositions.
Moving AI into production raises questions that a pilot simply can't answer. How will the agent be monitored? How will quality be evaluated? What happens when it produces an unexpected result? What architecture is needed to support traceability and scale? What will it cost to operate at production volume? What level of human oversight is required, and how does that change over time?
These are the questions that emerge once a promising idea starts moving towards everyday use, and they're also where the real learning begins.
Every production deployment challenges assumptions. It helps teams understand which ideas are ready to scale, which need more work and which may not be worth pursuing yet. It also improves the way future opportunities are evaluated. Once an organisation has worked through the production complexity of one agent, it becomes much better equipped to assess the next one.
The first production deployment isn't just about the value of that solution. It builds the capability to evaluate, govern and scale the ones that follow.
Best practice is being built through practice
One of the most important lessons from this phase of AI is that there is no tried, tested and trusted playbook waiting to be downloaded.
There are useful frameworks, principles and reference architectures. They help organisations get started in a more structured way, but many of the answers only emerge by working through real deployments, and that's always a little uncomfortable.
Technology leaders are used to having a high degree of confidence in timelines, budgets, operating costs and expected outcomes. Taking AI into production doesn't remove the need for those disciplines, but many of the answers only become clearer as you work through production deployments.
Architecture choices influence cost. Evaluation approaches influence reliability. Governance decisions influence how much autonomy an agent can safely take on. Every design decision affects the operating model that follows.
That doesn't mean organisations should wait. It means they should treat early production deployments as capability-building exercises rather than individual projects. Each one teaches something about design, cost, monitoring, governance, human oversight and organisational readiness, making the next decision better informed.
Best practice isn't being created in theory. It's being shaped by organisations willing to do the work, learn from the edge cases and refine their approach as they go.
That's one of the ideas behind the AI Operating Model white paper.
It brings together many of the patterns we're seeing emerge as organisations move AI from experimentation into production, offering a framework for thinking about the organisational questions that come with that transition.
For organisations beginning that journey, the goal isn't to avoid uncertainty. It's to build the capability to learn through it.
Tim Way | Content Editor
Tim spends time with tech leaders and customers to understand how transformation really plays out. He turns real-world examples into clear, practical content focused on what changed, what worked, and what others can learn.
FAQ
What does it mean to take AI into production?
Why is moving AI into production more difficult than building a proof of concept?
A proof of concept demonstrates that AI can perform a task. Production introduces a different set of questions, including how the solution will be monitored, how its performance will be evaluated, how it will behave in unexpected situations and what level of human oversight is required. These operational considerations often determine whether an AI solution can deliver sustainable business value.
Why should organisations start with a small number of AI deployments?
Taking one or two high-value AI solutions all the way into production often provides more organisational learning than running many disconnected pilots. Each production deployment helps build the capability to evaluate future AI opportunities, improve governance, refine operating models and better understand the practical requirements for scaling AI across the organisation.
How does AI change traditional technology delivery?
Traditional technology delivery focuses on planning, development, testing and deployment. AI introduces additional considerations that influence design from the beginning, including trust, human oversight, evaluation, monitoring and ongoing governance. As a result, organisations are adapting established delivery disciplines to support AI in production rather than replacing them.
What is an AI Operating Model?
An AI Operating Model provides a framework for introducing, governing and scaling AI across an organisation. It helps leaders think beyond individual AI projects by addressing areas such as governance, operating models, workforce capability, human oversight and the progressive introduction of AI into everyday business operations.
