Pilots and proof-of-concepts demonstrate that an AI solution can work. Production introduces new organisational questions around governance, monitoring, accountability, data quality, human oversight and operational responsibility. These challenges often only become apparent when AI is operating as part of day-to-day business processes.
Organisations everywhere are identifying opportunities where agentic AI could create value. Hackathons, workshops and business-led innovation have generated a steady stream of ideas, with teams finding opportunities to improve customer experiences, automate routine work, develop richer insights and help employees make better decisions.
With a growing list of potential use cases, the next decision is which of those ideas should become production solutions.
It's typical for organisations to approach that decision in the same way they would any other technology investment. They look for expected productivity gains, the breadth of business impact, implementation costs and potential risks. Those are all sensible questions to ask, but they don't capture everything an organisation is about to learn.
What many organisations are discovering is that some of the most important lessons only emerge once an agentic AI solution moves into production. Questions about governance, data, monitoring, human oversight and operational responsibility become very real in a production environment. They are difficult, and often impossible, to fully understand through a pilot or proof of concept alone.
That changes how organisations should think about early production deployments. Rather than evaluating them solely on the business outcome they are expected to deliver, I think decision makers should also consider the organisational capability they will build.
The business outcome still matters, but so do the knowledge, operating practices and organisational confidence that come from putting AI into production. Those capabilities become the foundations that make every deployment that follows faster, safer and more valuable.
Your first production deployment builds more than an AI solution
One of the reasons production deployments are so valuable is that they force organisations to answer questions they haven't needed to answer before.
A pilot or proof of concept can demonstrate that the AI behind a solution works. Production introduces a different set of considerations. How will it be monitored? Who is accountable for the decisions it supports? What level of autonomy is appropriate? How should exceptions be handled? Can the outputs be trusted? What happens when the underlying data changes?
Working through those questions is one of the most valuable outcomes of a first production deployment. Production holds a mirror up to an organisation, exposing questions that are difficult to identify in a pilot because they only become real when agentic AI starts operating as part of the business.
There are very few established playbooks for these decisions, and every organisation will arrive at its own answers based on the way it operates, its appetite for risk and the outcomes it is trying to achieve. Answering them is what builds capability. It clarifies responsibilities, establishes governance, strengthens operating practices and gives organisations a framework they can apply to future AI deployments.
First production deployments often take longer because organisations are also defining how AI will operate within their business. Those decisions become reusable capabilities that reduce uncertainty, provide a starting point for future projects and give teams greater confidence as they move from one deployment to the next.
Every production deployment changes what comes next
The lessons learned during production don't end with that deployment. Before a production deployment, each opportunity is often evaluated on its own merits. The conversation is centred on the potential use case, the expected return and the effort required to deliver it. Once organisations have taken AI into production, they often discover that some of the most valuable outcomes have little to do with the original solution itself.
Production has a way of exposing where capability needs to be strengthened. It might highlight gaps in data quality, reveal the need for better governance, expose limitations in existing processes or show where additional skills and capacity are required. Those investments often aren't obvious when a project is first evaluated, but they often become the capabilities that unlock progress across many future AI initiatives.
Another shift happens at the same time. Early AI projects often focus on improving an existing task or making a current process more efficient. As organisations gain experience, they become more confident questioning whether the work should be done that way at all. Rather than asking where AI can be added to an existing process, they begin redesigning how workflows, where decisions are made, and how people and agentic AI work together to achieve the best outcome.
That growing confidence changes future decision making. Organisations become better at recognising where capability already exists, where further investment is needed and where the greatest long-term value can be created. Over time, the conversation becomes less about individual AI solutions and more about building the organisational capability to continually redesign and improve the way work gets done.
Best practice comes from practice
There are very few established playbooks for introducing agentic AI into production. Every organisation is working through its own combination of people, processes, technology, data, governance and risk, which means many of the most valuable lessons can only be learnt by doing.
That's why I think organisations should view their first production deployments as more than individual technology projects. They become an opportunity to build the capability, confidence and operating practices that will shape every deployment that follows. Along the way, they give organisations the confidence to question long-established ways of working and rethink how work should be designed in a human-agent workforce.
Each production deployment leaves an organisation more capable than before. The experience gained, the governance established and the confidence to rethink how work gets done become assets that support every deployment that follows. That's why, in a field evolving as quickly as AI, best practice still comes from practice.
That's one of the themes explored in the AI Operating Model white paper. Rather than focusing on the technology, it looks at the organisational capabilities, governance and operating model required to move AI into production with confidence.
For organisations beginning that journey, the challenge isn't simply choosing the right AI use cases. It's building the capability to learn, adapt and redesign work as each deployment builds on the last.
Craig Westcott I Executive Director of Strategic Growth
Craig leads a number of Fusion5's growth areas, including AI, Managed Services, Data and Integration. He partners with organisations to turn technology investment into practical business value.
Craig Gerken | General Manager Solution Strategy - Data & Integration NZ
Craig partners with organisations to simplify complexity and unlock value from data, integration, and emerging technologies. He offers real-world guidance that helps leaders make confident decisions and prepare their businesses for what's next.
FAQ
Why is moving AI into production different from running a pilot?
What should organisations learn from their first AI deployment?
A first production deployment should be viewed as more than a technology project. Alongside delivering business value, it helps organisations build governance, operating practices, organisational confidence and reusable capabilities. Those lessons make future AI deployments faster, safer and more effective.
How does production AI help organisations redesign work?
As organisations gain experience running AI in production, they often move beyond simply automating existing tasks. Production experience gives leaders the confidence to question established ways of working, redesign workflows, rethink decision-making and determine how people and agentic AI can work together most effectively.
Why is organisational capability important for successful AI adoption?
Long-term AI success depends on more than deploying individual solutions. Organisations also need governance, trusted data, operating practices and clear accountability. Building these capabilities creates a stronger foundation for future AI initiatives and helps organisations continually improve the way work gets done.
