Experimenting with artificial intelligence (AI) is easy; executing it at scale is hard. After years of pilots and prototypes, a stark reality has emerged: over 80% of AI projects never realise their goals. The barrier isn’t the technology—it’s the failure to integrate AI into the fundamental flow of business.
Nowhere is this execution gap more visible than in the core operational systems that manage how companies make, move, and sell. These platforms are rich with data and transactions where AI should thrive, yet most deployments falter due to integration challenges. Success isn’t about adding an ‘AI feature’; it’s about embedding intelligence directly into the processes themselves.
ERP: From System of Record to System of Action
If AI’s challenge is execution, ERP is its engine. Historically, ERPs have been passive systems of record. The opportunity now is to transform them into active systems of action—platforms that can sense, decide, and respond automatically. This means embedding domain-specific AI agents that operate inside the ERP, aligned with business logic and guardrails. These agents don’t sit in a lab; they live in procurement, production, and finance—aware of rules, roles, and permissions—executing tasks safely and auditably.
This shift moves AI from insight to action. Instead of simply alerting users to a late shipment, an ERP-integrated agent can reroute orders or trigger contingency plans. These agents act as digital co-workers that are context-aware, policy-compliant, and measurable in their impact.
AI Agents in Action
Across operations, AI agents are already demonstrating what execution at scale looks like. In supply chains, for instance, when a supplier runs late, an agent can immediately detect the delay, flag it, and reassign orders or adjust production schedules without the usual firefighting. In forecasting, agents track market shifts and anomalies, fine-tuning supply plans in real time and involving human decision-makers only when strategic judgement is required.
In finance, AI is quietly taking on the time-consuming tasks of invoice matching and exception handling, cutting down manual workloads and accelerating month-end closes. Even in the contract-to-cash cycle, intelligent agents are managing order generation, invoicing, and payment reminders, compressing cash cycles and improving cash flow.
These are not futuristic examples—they are happening today. The result is faster execution, fewer errors, and humans freed to focus on higher-value work. AI doesn’t replace judgement; it scales it.
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Operational Readiness, Not Technical Feasibility
What separates successful deployments from stalled pilots isn’t the technology—it’s operational readiness. The friction lies in data, processes, and people. Fragmented systems and inconsistent data can cripple even the smartest algorithms; clean, connected, and well-structured data is a non-negotiable foundation.
The human factor is equally decisive. Teams that distrust or misunderstand AI are unlikely to embrace it, which is why engaging process owners early, explaining the purpose clearly, and investing in AI literacy are essential steps towards adoption. Furthermore, governance is critical: clear guardrails, audit trails, and human-in-the-loop checkpoints ensure accountability and build trust. When KPIs are aligned with AI-enabled outcomes and incentives encourage responsible innovation, confidence in automation grows.
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Ultimately, operational readiness isn’t about technical feasibility; it’s about aligning people, processes, and data to turn potential into performance. It’s less glamorous than experimentation—but it is the difference between hype and real-world impact.
From Pilot to Production: The Playbook
Successful organisations follow a disciplined playbook:
- Start with a real business problem, not a shiny model.
- Fix the process first—AI only amplifies efficiency if the foundation is sound.
- Run small pilots with measurable outcomes and refine based on results.
- Invest in people and change management as much as in technology.
- Embed governance early, defining what AI can do safely.
- Scale gradually, process by process, using each success to build confidence.
- Lead from the top, making AI a cultural and operational priority.
The Way Forward
AI’s technology is ready. The winners will be those who execute—embedding intelligence into operations where decisions and pounds move fast. In supply chain, finance, and manufacturing, execution is everything. The mission for technology partners is to help companies cross that gap, operationalising AI through domain-specific agents that act within context and under control.
The real challenge with AI isn’t invention—it’s execution. The companies that master it will move beyond hype and transform how work gets done. The technology is ready. The question is: are we?
