From reactive to predictive to prescriptive: The evolution of industrial decision-making
Prescriptive decision-making has transformed maintenance management, output optimisation and sustainability from cost-centre activities into a strategic competitive advantage. Yet despite clear data showing that adopting prescriptive systems can significantly increase output and sustainability while reducing downtime and errors, many boards have yet to grasp the full business implications of predictive, let alone prescriptive, capability. This article explores why that gap persists and offers a framework to help leaders assess where they stand on the journey.
For manufacturers operating at the sharp end of energy and resource volatility, data-driven decision-making has moved from an engineering concept to a boardroom differentiator. In energy-intensive industries, where a single unplanned shutdown can erode millions in EBITDA, predictive and prescriptive systems have proven their value.
Specifically, evidence from multiple sectors shows that moving from reactive to predictive maintenance can deliver up to a 75% reduction in unplanned downtime, a 60% reduction in maintenance costs, and a 40% increase in equipment lifespan, with a direct EBITDA uplift through increased throughput and avoided lost production [1][2]. In one widely cited case, Duke Energy detected a turbine anomaly before failure, avoiding catastrophic damage and saving 34 million US dollars in lost generation and repair costs [3].
These aren’t marginal gains. Rather they represent a structural shift in how industrial assets create value, extending capex cycles, enhancing utilisation, and improving sustainability performance through reduced waste and energy intensity. Instead of seeing maintenance as a cost centre it becomes a competitive advantage. Yet many boards still see predictive analytics as a technical initiative rather than a strategic lever. Full value can’t be realised until leaders connect digital capability directly to financial and sustainability outcomes.
Why predictive remains misunderstood
Most senior executives understand what predictive analytics does but not what it changes. Predictive capability is often treated as an operational add-on rather than a transformation in how risk, capital efficiency and resilience are managed. Boards that evaluate predictive projects purely through engineering KPIs rather than EBITDA, deferred capex or energy intensity inadvertently cap their return. The result is “pilot purgatory”: technically sound initiatives that never scale because they are not anchored in governance or accountability [4].
Predictive and prescriptive systems alter who makes decisions, how quickly and on what basis. That demands leadership alignment on data ownership, organisation-wide trust in the algorithms and performance incentives. Until boards recognise predictive analytics as a new operating model rather than a new IT platform, transformation will remain stalled.
Understanding the maturity curve
For board leaders, understanding where their organisation sits on the decision-making maturity curve isn’t a technical exercise. It’s a matter of governance, resilience and value creation. Reactive operations rely on experience and manual intervention; problems are discovered only after they occur, creating volatility in output, safety and cost. Predictive operations introduce visibility, allowing early intervention, but often remain a capability rather than a systemic advantage.
Prescriptive operations represent a structural shift, where integrated data across assets, supply chains and external variables allows algorithms to recommend or automatically execute optimal actions. Decision-making becomes continuous, contextual and closed loop. Producing higher asset productivity, deferred capex and reduced energy intensity [5][6]. As a result, industrial reliability becomes a predictable business outcome.
In this context, maturity in industrial decision-making is emerging as a new indicator of competitiveness. In a volatile energy and resource environment, prescriptive capability can’t be about automation for its own sake. It must be about embedding consistency, optimisation and learning into the fabric of enterprise performance.
The leadership fault lines
Despite the proven upside, many manufacturers remain stuck between predictive insight and prescriptive action. In this case, progress is typically constrained by a lack of alignment between leadership intent, governance and investment logic, rather than technology [7][8]. Three leadership fault lines consistently distinguish businesses that advance from those that stall: culture, governance and investment.
Many businesses still celebrate firefighting heroics. The engineer who saves the shift, the team that restores production overnight. This mindset rewards reaction over prevention. Boards shape culture through story, and when digital transformation is framed as cost control, it stays in the back office. In contrast, when framed as resilience and competitiveness, it becomes a strategic mission.
Governance and ownership are equally critical. As algorithms recommend or automate decisions, accountability blurs. Most manufacturers lack formal structures for data trust, algorithmic transparency and cross-functional responsibility. Boards must define governance, embed AI explainability within enterprise risk frameworks and make data trust a standing agenda item, not an IT matter [9].
Finally, digital infrastructure and analytics talent are too often funded as short-term projects rather than enduring capabilities. This limits integration and locks businesses into dependency on external partners. Treating data systems as strategic infrastructure and workforce capability as a competitive moat allows returns to be evaluated over asset lifecycles rather than quarterly cycles.
These three fault lines, culture, governance and investment, are the true barriers to prescriptive maturity. Technology can be purchased but alignment requires leadership.
Progress through discipline, not disruption
In an uncertain economic climate, few manufacturers can justify wide-scale transformation projects, but progress doesn’t have to be revolutionary. The most successful organisations take an incremental, evidence-led approach, focusing on areas where financial return is clearest and operational risk lowest [10]. Improving existing processes often delivers faster value than disruptive reinvention. For industrial firms, that means layering predictive and prescriptive capability onto existing assets rather than replacing them. Incremental transformation doesn’t signal hesitation. It’s disciplined progress that builds confidence while protecting continuity.
Prescriptive decision-making is reshaping industrial performance, turning data into insight and insight into sustained value. For energy-intensive manufacturers, it offers a route to higher productivity, stronger resilience and measurable sustainability gains. We have the technology, but the differentiator is leadership maturity. By that I mean, the ability of boards to convert predictive intelligence into governance discipline and operational insight into enterprise advantage. This evolution doesn’t require organisations to rip out and replace what they already have but the intent to collect data and critically empower it to make decisions.
References
[1] (2023) Predictive Maintenance Using AI to Prevent Equipment Failures, AVEVA Blog.
[2] (2023) Predictive Maintenance: The Future of Manufacturing Productivity, McKinsey & Company.
[3] (2021) Case Study: Duke Energy Predictive Analytics Prevents Turbine Failure, IBM.
[4] (2024) Explainable Predictive Maintenance: A Survey on the Intersection of Predictive Maintenance and Explainable AI (XAI), arXiv.
[5] (2024) AI in Predictive and Prescriptive Maintenance, Siemens / Business Insider.
[6] (2024) Global Prescriptive Analytics Market Report 2024–2033, IMARC Group.
[7] (2023) Digital Transformation in Energy-Intensive Manufacturing, PwC.
[8] (2023) AI Governance and Trust: Board Imperatives for Industry, EY.
[9] (2024) The Industrial Data Maturity Index, Capgemini Research Institute.
[10] (2025) Top Business Intelligence Trends in Manufacturing to Watch in 2025, Moldstud.
