How to Accelerate AI Readiness in an Industrial Organization
Many industrial leaders are reviewing AI investment proposals while their organization is still working out what AI can support in day-to-day operations. In McKinsey's survey of more than 100 manufacturing COOs, only 2% said AI was fully embedded across their operations. An AI readiness assessment helps leadership see which AI ambitions the organization can support today and what should be strengthened before more budget is committed.
1 Start with a specific business objective
A readiness assessment is most useful when it is tied to a decision leadership needs to make. A question such as whether two plants can use AI to support maintenance planning within the next 18 months gives the assessment a clear focus and keeps the findings connected to the business case behind the investment.
2 Include leadership, technology management, and operations
Leaders, technology managers, and operations teams each see a different part of the organization's readiness, from investment priorities and system integration to how work is carried out across shifts and sites. An assessment that draws on all of these views gives leadership a more complete picture. Frontline input deserves particular attention, since 45% of respondents in PwC and Manufacturing Institute research said leaving frontline leaders out of design or rollout contributed to unsuccessful AI initiatives.
3 Assess capabilities your ambition depends on
Industrial AI relies on data that can be accessed, connected, and trusted across historians, ERP systems, maintenance platforms, and OT systems. This is an area where many manufacturers are still building capability, with 46% of COOs in the McKinsey survey reporting limitations in their data or IT/OT systems.
Processes and people matter as much as data. An AI application is easier to scale when the processes it supports are reasonably consistent across sites and when the people using it have the skills and time to work with it. People readiness also needs to be considered. In PwC and the Manufacturing Institute’s 2026 research, 49% of respondents reported that existing training programs do not prepare frontline employees for AI-driven role changes at all. Only 2% expressed high confidence that their teams were ready for how AI will reshape daily work.
Governance completes the picture. Leadership needs clarity on who owns each AI application, who reviews its outputs, and how concerns are raised when AI influences decisions in maintenance, quality, or safety.
4 Base AI Readiness conclusions on evidence
Views about readiness are a useful starting point, and they become more reliable when they are checked against how work is done at sites and in systems. Evidence helps leadership see which capabilities are already working in practice and which exist mainly in plans or policies.
5 Pay attention to where views differ
In most large industrial organizations, leadership and operations see some capabilities differently. A platform may be considered widely adopted at headquarters while some sites still rely on spreadsheets, or a governance policy may be in place while frontline teams are unsure how to escalate a questionable AI output. Identifying these differences early gives leadership the chance to address them before they affect the results of an AI investment.
6 Connect the findings to investment decisions
The commercial value of an assessment comes from the decisions it informs. When AI initiatives move ahead of the capabilities they depend on, budgets often need to be re-planned and expected returns take longer to arrive. A clear view of readiness helps leadership decide which initiatives can proceed, which supporting capabilities need investment first, and how to sequence spending across technology, data, processes, and people. Revisiting readiness after a defined period also lets leadership track progress as the organization develops.
How Volar Alta helps
Through IARA, we help industrial leaders build an evidence-based view of where their organization stands across leadership, technology management, and operations before they commit to major AI and technology investments.
If your organization is planning a major AI or technology investment in the next 12 months, contact us to discuss where your organization stands today and what to address first.
