Artificial Intelligence (AI) is often framed as a breakthrough in efficiency that will sharply reduce working hours and resource demands. History suggests a more complex pattern. Nineteenth-century economist William Stanley Jevons observed that as steam engines became more efficient, Britain’s coal consumption rose because lower operating costs made steam power practical for broader economic use.
That paradox offers a helpful lens for understanding AI in today’s corporate environment. As automation becomes more capable and less expensive to deploy, organizations are likely to expand the volume, variety, and ambition of work they expect AI-enabled teams and systems to support.
The implication is not that AI will eliminate the need for capacity planning. It is that AI can change the demand curve. Leaders should expect successful automation to create new expectations, higher throughput, broader use cases and greater pressure on the operating model that supports them.
This is an important shift for business leaders moving from AI experimentation to implementation. The planning question is not only how much efficiency AI can create, but whether the organization has the data, governance, process discipline, technology architecture and change capacity to absorb the demand that successful AI adoption may generate.
Why Leaders Must Build for Expansion
AI conversations often begin with cost reduction. Leaders want to know how many hours can be saved, how quickly workflows can be automated and where labor can be redeployed. Those are reasonable questions, but they can narrow the conversation too early.
When the cost of completing a task falls, organizations rarely keep the same volume of work. They typically do more of that work, extend it into adjacent functions or apply it to activities that were previously too manual, too slow or too expensive to justify.
For leadership teams, AI adoption should not be planned as a contained experiment with a static demand profile. If the technology proves effective, demand is likely to increase. The organizations best positioned for that growth will be those that have invested early in the foundations required to support scale.
Real-world Example
An organization may begin by automating invoice processing within accounts payable. Once the cost per document declines and accuracy improves, the same approach may become attractive for contracts, claims, audit support materials and other document-heavy workflows that were previously too manual to scale.
While the original use case may have been modest, the success of that deployment changes the economics of similar work elsewhere in the business. Over time, volume grows, the number of stakeholders increases, and the burden on systems, controls and operating models expands with it.
The Infrastructure Required for Strong AI
High-profile outcomes tend to capture the most attention in any industry, whether people are lauding a successful new chatbot or the record-breaking journey of NASA’s Artemis II. The world typically focuses on the achievement, while the painstaking work and foundational alignment can fade into the background.
Many boardrooms are pushing for the next greatest AI strategy, but technology must have the right infrastructure to be effective and foundations that can scale. Similar to the launch of a spacecraft, where the 18 years of engineering prior to the mission and thousands of experts working behind the scenes can be glossed over, implementing an AI strategy requires depth beyond the desired outcome.
Without the right foundations, tools that appear promising in a pilot can become difficult to operationalize in production. AI readiness must therefore be treated as an enterprise capability, not as a tool deployment.
Data Foundations
AI systems are only as useful as the data they rely on. A strong, reliable data foundation gives organizations confidence that the information feeding AI models is accurate, accessible, governed and fit for decision-making.
Process Discipline
AI performs best when introduced into environments with clear, repeatable processes. Organizations need workflows that are documented, auditable, and sufficiently standardized to support automation and intelligent decision support. Without process discipline, AI can amplify inconsistency rather than reduce it.
Governance and Security
As AI becomes embedded in decision-making and operational workflows, governance cannot be an afterthought. Leaders need clarity around accountability, access, privacy, explainability, control design and auditability. They also need practical frameworks for deciding where AI can be used, where human review is required, and how outputs should be monitored over time. Done well, governance makes innovation more sustainable and creates the trust required for broader adoption.
People and Change Management
AI readiness is as much an organizational challenge as a technical one. Teams need clear ownership, defined responsibilities, and confidence in how AI will support their work, rather than disrupt it.
Many organizations also need to reduce dependence on informal knowledge and move toward more transparent, repeatable ways of working. Training, communication, leadership alignment and change management are essential if AI is going to move beyond access into effective enterprise use.
Technology Readiness
Legacy architecture, fragmented applications and under-managed platforms can all limit the impact of AI. Technology environments must be prepared to support integration and production-scale use. That may require modernization, cleanup of obsolete logic, stronger platform management or a more deliberate approach to systems integration.
From AI Interest to AI Readiness
For many organizations, the real challenge lies in channeling the initial enthusiasm around AI into a durable strategy that aligns business priorities, operating realities and underlying capabilities to scale. That work calls for a more disciplined approach. Companies should assess where AI can create meaningful value, identify foundational gaps that may limit adoption and sequence investments to support both near-term results and long-term scalability.
How Cherry Bekaert Helps Organizations Build for Scale
Cherry Bekaert helps organizations move beyond AI experimentation and toward enterprise readiness. Our Artificial Intelligence Services professionals work with you to identify and implement scalable AI solutions. Our team collaborates with services throughout the Firm to offer comprehensive support across:
- Analytics & Automation
- Process Optimization
- Digital Advisory
- CFO Advisory
- Risk & Cybersecurity
- Tax & Assurance
- Transaction Advisory
As demand for AI grows, the organizations that benefit most will not necessarily be the ones that launched the earliest pilot or adopted the most visible tool. Instead, the businesses that built a foundation strong enough to support expansion will excel. If you have questions or are ready to get started, contact an advisor to learn more about our analytics and automation services.
