By Selvi Shanmughavel, CIO, Straive
Enterprise AI is entering a more demanding phase. The early question was whether organisations could use AI to solve a particular problem. Today, as models become more capable and AI moves into everyday business processes, the harder question is whether organisations can make those systems work consistently, responsibly and economically at scale.
A solution that performs well in a controlled environment can behave very differently in a live enterprise. Data comes from multiple systems, processes vary across teams, exceptions multiply, and decisions increasingly require coordination between AI systems and people. The question is therefore no longer simply whether the model works, but whether the organisation is ready to run it.
That readiness extends beyond the model. It includes data quality and context, workflow integration, ownership, human judgement and performance measurement. Gartner found that 63% of organisations either did not have or were unsure whether they had the right data management practices for AI. Gartner also predicted that 60% of AI projects unsupported by AI-ready data would be abandoned through 2026.
Taken together, this points to a broader enterprise gap: AI adoption and investment are moving faster than the operating models needed to support them at scale, while early productivity gains are not yet consistently translating into measurable business transformation.
From proving AI works to making it work
A pilot asks whether the technology can solve a problem. Production asks whether it can do so consistently, whether the inputs can be trusted, who owns the outcome, what happens when it is wrong and whether the workflow can absorb it without creating new friction. These are very different tests. In a controlled experiment, teams typically work with curated data, domain experts are closely involved and edge cases can be resolved quickly. Production removes those advantages.
Data arrives continuously from different systems, definitions may vary across functions, documents may lack metadata and critical context may sit outside structured databases. Processes that appeared straightforward during a pilot become more complicated when they interact with legacy systems and real users.
The model has not necessarily become worse. The operating environment has become harder. Data readiness is therefore critical, but it is only one component of production readiness.
Data readiness is necessary, but it is not the finish line
For an AI system inside a business workflow, data needs more than volume. It needs context. The system needs to know what information means, whether it is current, where it came from, who owns it and whether it is appropriate for the decision being made. Some of that context may also sit in undocumented rules, exceptions and institutional knowledge rather than in a database. Making that knowledge usable is part of making the enterprise AI-ready.
Even high-quality data and a capable model will not create value if the AI system is disconnected from the workflow where the decision or action actually happens. The objective is not simply to make AI accurate. It is to make AI useful, reliable and accountable within the context in which it operates.
What changes when AI enters the workflow
Once AI becomes part of someone’s work, the acceptable level of error changes with the consequence of the decision. Human oversight therefore needs to be designed, not simply added.
Lower-risk activities may move through automated checks, while higher-impact decisions require structured human validation. The objective should be to reduce friction across the end-to-end workflow, not simply move the bottleneck from one queue to the next. That means determining where human judgement adds the most value, rather than inserting manual review at every stage.
This becomes even more important as enterprises move towards agentic AI. When systems move beyond generating information and begin taking actions across enterprise applications, the question changes from whether the AI can perform a task to what authority the organisation should give it to perform that task. That is an operating-model question, not simply a technology question.
The overlooked layer: the operating model
Organisations that successfully scale AI will not necessarily be those with access to the most sophisticated models. They will be the ones that build the operating environment around those models.
That means clearly defined ownership and accountability, established escalation paths, performance monitoring after deployment and feedback loops that allow systems to improve as the business changes.
It also means integrating AI into existing workflows rather than treating it as a separate technology layer.
This is particularly relevant as AI moves towards broader enterprise orchestration, where the scaling challenge extends beyond technology to data, legacy integration, cybersecurity, skills, governance and the ability to understand and manage costs.
For CIOs, production readiness therefore has to be designed alongside the AI use case, not after the pilot succeeds.
The question every CIO should be asking
The enterprise AI conversation has spent considerable time asking which model to choose, which platform to buy and how quickly a pilot can be launched. Those questions still matter, but they are no longer sufficient. Before approving the next AI initiative, leaders need to consider whether the data is fit for purpose, whether the AI system can operate within the existing workflow, who owns the outcome, what happens when the system is wrong, where human judgement is required and how business value will be measured once the pilot is over.
These questions shift the conversation from AI experimentation to AI operations. As AI capabilities become more accessible, the differentiator will increasingly be an organisation’s ability to build the data, workflows, governance and human capabilities needed to make those capabilities work reliably in the real world.
The AI pilot is proof that something is possible. Production is the proof that an organisation is ready to make it work.