Enterprise AI adoption is a marathon, not a sprint

Sprints are good. They are even necessary to kickstart AI adoption. Quick pilots, rapid demos, and early wins build initial confidence for enterprise AI. But for many organisations, the effort stops right there - they build up plenty of momentum but often have no clear path to turn it into a lasting advantage.

The AI Business Value Radar from Infosys Knowledge Institute reiterates this fact: while about 50% of AI initiatives achieve some or all of their business objectives, one in five delivers no measurable value and is frequently cancelled after deployment. Many projects that are greeted with initial applause may be quietly shelved later.

To move past this plateau, organisations must tackle real operational hurdles: from scattered tools, legacy data silos, and weak governance to severe skills shortages and software overload. Consider this example: a mid-sized Australian retailer that operates more than 300 software applications across three different eCommerce platforms. Since these systems don't talk to each other, even basic initiatives like a customer loyalty program get stuck. Overcoming these operational roadblocks requires stepping back from quick fixes and committing to a long-term capability model.

Build foundational capabilities for scale

Sustained AI value is rarely achieved through single tools; it requires connecting vision, governance, integration, and people. A clear strategy ensures that the organisation treats AI as a core business driver, and not just a routine tech upgrade. Governance must be established early, and clear risk boundaries and ethical rules must be set right from the start, long before the first pilot goes live. These controls then need to be built directly into software tools as operations grow.

Data readiness is what truly separates successful deployments from failed ones. AI models are only as effective as the data they can access. A leading Australian retailer shows how this works in practice: online shoppers see up-to-the-minute stock levels and exact aisle locations because the digital store draws straight from live inventory feeds. On physical sites, blending visual cameras, IoT, and Light Detection and Ranging (LiDAR) sensors provides a far clearer operational picture. For example, local technology firm SenSen.ai applies this multi-sensor approach to manage urban traffic and public safety, demonstrating that combined data streams yield far better outcomes than isolated inputs.

Equally important is embedding AI directly into core business processes through agentic workflows. Rather than operating as standalone tools, intelligent agents handle multi-step actions across operational boundaries - such as predicting equipment failure in manufacturing and automatically reconfiguring settings. However, complex tech architecture means little if teams lack the skills to run it. People come last in this list but not in importance. That is why a layered AI learning path matters. It starts with basic literacy for everyone. It then moves to role-based skills, and finally to hands-on agent building for the people who will design and run these workflows. Leaders need their own layer, focused on judgement, risk and accountability rather than tools. Without this progression, we end up with a few experts and a large workforce unsure how to work with agents.

Know where you are on the course

Building AI capabilities is not just about turning on a switch: it happens in stages. Organisations typically progress through five distinct phases: Explore, Enable, Establish, Expand, and Excel.

Early stages focus on establishing low-risk proof-of-concept projects and defining clear roadmaps. As organisations mature into the Establish and Expand phases, they centralise governance into live platforms and empower business units to innovate within shared guardrails.

At the ultimate Excel stage, the organisation operates as a modular system of reusable data, model, and agent assets, and leads its industry in agentic process reinvention.

Translate plan to action

A maturity assessment that lacks funding is just a diagnosis. Rather than spreading resources thin, executives need to group AI spending into 10-20 well-defined projects, making sure every initiative has a single owner, a dedicated budget, and clear performance metrics.

One major Australian retailer turned things around by replacing isolated trials in favour of a board-approved, fully funded roadmap. By building out the core platform first, the retailer set up enterprise-wide guardrails that now support every new digital rollout.

Before the next pilot

Before approving the next pilot, organisation must be clear about where their real capabilities lie. They must identify which foundational steps are being overlooked and confirm whether they hold a fully funded execution strategy and roadmap, and not just a list of isolated trials.

Organisations that win will not be the ones that launch pilots the fastest, but those that systematically build the operational endurance to compound value year after year. So, keep sprinting where pertinent, while focusing the larger picture on the marathon.