Most organisations are experimenting with AI. Here's how to turn those experiments into governed production systems that return meaningful ROI.
This article is a sneak peek at what we'll cover in our upcoming webinar Pilot to Payback, on Wednesday 25 November. PhoenixDX's Sean Chamberlin and Charl Enne will share the frameworks, worked examples and lessons learnt that help organisations move AI from experiment to enterprise value. Here's a taste of what's in store.
According to MIT research, more than 80% of organisations have explored or implemented AI pilots. Around 88% of those pilots never reach production. And around 95% are delivering zero return.
The technology is rarely the problem. Most pilots stall at design time, because they are built to prove something can work, not that it should be run, owned and paid for at scale.
AI pilots start where the technology is exciting, not where the business case is strongest.
The way forward: A simple prioritisation framework that weighs value against cost, effort and risk, so your first use case is one that can actually pay back. The best first use cases sit in a sweet spot where what AI does well (working with documents, supporting decisions, and personalising at scale) overlaps with genuine process bottlenecks: high volumes, time-consuming tasks, high repetition, and not enough people. A few simple lenses then separate strong candidates from weak ones. Look for the "shadow tasks" hidden in informal daily workarounds. Check the decision blast radius, starting where an AI error is low-consequence and a human stays in the loop. And favour use cases that can prove measurable ROI within a quarter.
At the webinar: Discover the full six-lens selection framework and an AI prioritisation matrix, with worked examples.
Most AI pilots get by with a policy and a register. Yet, AI production needs owners, risk treatment, human oversight and monitoring, and retrofitting them often means starting again.
The way forward: Design AI pilots as production-intent from day one, with the right controls in place at the right stage. In practice, every AI system should answer five simple questions. Are we focusing on the right things? How will we measure success? Who owns it? What could go wrong? Can a person stop it? It also helps to treat risk the way ISO 31000 does: not just as stopping bad things happening, but as managing uncertainty on objectives, both positive and negative.
At the webinar: Find out which governance controls you need for AI production, and the common mistakes that stall pilots.
Business cases overstate early returns, and nobody measures after go-live.
The way forward: Measure the same way twice: baseline before the AI pilot, agree a success threshold, and track benefits against it after go-live. The business case and the benefits realisation that follows should mirror each other step for step, using the same formulas for value and net cost. At go-live, the handover needs a frozen baseline, an agreed measurement plan, and run-level cost logging already in place. Honest numbers also mean honest timing. In a worked example of an invoice exception-handling agent, the three-year case is solidly positive, but year one runs at a loss, and under a pessimistic scenario the case loses money. Knowing that upfront is what makes a business case credible to Finance.
At the webinar: Explore the anticipated-versus-actual ROI model, the worked example in full, and how to stress-test a case across four scenarios.
IT or an innovation team runs the AI pilot, and nobody in the business owns the outcome.
The way forward: Name cross-functional owners upfront, from business outcomes through to who has the right to turn the agent off. The strongest approach is to make ROI a governance decision. The governance committee sets the ROI target and go/no-go criteria at approval. A named owner is accountable for the value as well as the risk. Results are reported monthly against the baseline, and the committee decides whether to scale, optimise or retire. Involving the people who will implement the system from the first draft of the business case keeps it grounded in reality.
At the webinar: Learn about a RACI matrix for the key roles in AI agent deployment, and how a benefits register keeps accountabilities visible.
Good AI governance is not a brake applied at the end. It decides what to build, how to run it, who owns it and how to maximise the benefits. Standards such as ISO/IEC 42001, the world's first AI management system standard, provide that governance structure and credibility with customers and regulators.
Join Sean Chamberlin and Charl Enne from PhoenixDX for our must-see webinar Pilot to Payback, on Wednesday 25 November at 12:30 pm AEDT.
Over just 45 minutes, the session will go well beyond the outline above and cover: