Experts say organisations need to first understand how work is actually performed before attempting large-scale automation with AI.
As organisations rapidly increase investment in artificial intelligence, a key question is gaining attention in boardrooms and executive discussions: whether they are truly prepared for the AI journey they are eager to begin.
This issue was recently raised within the CXO community, as AI continues to promise greater productivity, faster decision-making, improved customer experiences, and new business opportunities. However, beneath the optimism surrounding these technologies lies a more complex reality—many organisations are trying to implement AI without first clearly understanding how their core business processes actually function.
Technology leaders are increasingly expected to lead AI adoption and drive innovation across enterprises. However, in many organisations, IT teams have traditionally concentrated on infrastructure, applications, cybersecurity, and service delivery, rather than end-to-end process optimisation. As a result, some leaders may have only limited visibility into the interconnected workflows that create value across different departments
Business processes typically evolve over many years through incremental changes, acquisitions, regulatory requirements, and local optimisations. While workflows may appear straightforward on paper, in practice they often involve manual interventions, exceptions, duplicated steps, and disconnected systems. Without a clear understanding of these realities, AI initiatives risk being built on unstable foundations.
The challenge is not a lack of technical capability—modern technology leaders are highly skilled in data, automation, and digital transformation. Instead, the issue is that many organisations have not sufficiently invested in documenting, measuring, and continuously improving their operational processes before advancing into large-scale AI deployment.
When process excellence is lacking, artificial intelligence can end up amplifying inefficiencies instead of resolving them. A poorly designed workflow remains flawed even when automated, and in some cases AI may simply accelerate decisions based on weak or inconsistent processes—leading to greater complexity and unintended outcomes.
Experts in operational transformation argue that organisations should first understand how work is actually performed before introducing large-scale automation. This includes identifying bottlenecks, reducing process variation, clarifying responsibilities, improving data quality, and defining meaningful performance metrics. These foundations are essential for ensuring AI delivers real and sustainable value.
A key concern is data reliability. Since AI systems depend on data generated by business processes, any inconsistency, fragmentation, or weak governance in those processes can result in incomplete or inaccurate inputs. This, in turn, can produce unreliable AI outputs and reduce trust among employees and decision-makers.
Despite these risks, many organisations continue to prioritise AI adoption due to competitive pressure and market expectations. Executives often worry about falling behind rivals that are rapidly adopting new technologies, which can lead to AI initiatives being launched before foundational process improvements are fully in place.
The most successful organisations are increasingly recognising that AI and process excellence should not be treated as separate priorities, but developed in parallel. A strong understanding of how business operations actually function helps organisations identify where AI can deliver the most value while avoiding costly missteps. In turn, AI can also highlight inefficiencies, generate insights, and support continuous improvement when applied to well-structured processes.
Looking ahead, enterprise success in AI is likely to depend less on how quickly technology is adopted and more on how effectively it is combined with operational discipline. Rather than starting with what AI can do, leaders may first need to ask a simpler question: do we truly understand how the business works today?
The answer to that question may ultimately determine whether AI becomes a genuine driver of transformation or simply another costly experiment in enterprise technology.
