Creating capacity matters, but only when technology investments produce measurable business value.
Enterprise leaders are under intense pressure to act on AI. The technology is evolving quickly, expectations are high and every function can identify a potential use case. But urgency is not the same as strategy. Starting with AI can lead teams to fund technology before defining the business problem, improving the underlying process or establishing how the investment will create measurable value.
That was one of the clearest messages from Rimini Street President and CEO Seth Ravin in a recent CNBC Squawk Box interview. His advice was direct: solve the business process first, fix and automate it and only then selectively apply AI. In other words, business strategy first, technology second.
Applied indiscriminately, AI can add cost, operating complexity and governance requirements faster than it creates value. The right question is not, “Where can we put AI?” It is, “What business result must improve, and what is the most effective way to improve it?”
Watch the full interview below:
A three-part test for every technology investment
In the interview, Ravin offered a practical screen for deciding whether a project deserves investment. It should do at least one of three things:
1. Reduce operating costs
Remove unnecessary expense, simplify the technology estate or lower the ongoing effort required to operate critical systems.
2. Improve profitability
Increase productivity, strengthen margins or help the business convert resources into better financial performance.
3. Build competitive advantage
Enable differentiated capabilities, stronger customer outcomes or a faster response to market change.
If a proposed investment does none of these, Ravin’s view is that it is not worth pursuing. This test shifts the conversation away from novelty and toward business accountability. It also gives technology and business leaders a common basis for prioritization when budgets, talent and attention are constrained.
Creating capacity starts with the systems you already have
A business-first AI strategy also changes how leaders think about existing enterprise systems. The goal does not have to be wholesale replacement. Core systems still execute thousands of essential processes, from paying employees and invoicing customers to operating in regulated environments. Replacing them can consume substantial funding and talent without corresponding improvement in critical business processes.
Instead, leaders can examine where the current operating model is absorbing resources without creating appreciable value. Examples include mandatory upgrades that offer limited business benefit, large migration programs driven by vendor timelines and fragmented operational processes. Reducing those demands can release budget and expertise for initiatives tied to the three-part business test.
This is where “creating capacity” becomes specific. It’s not simply a call to spend less. It is a deliberate reallocation of money, time and talent from low-value technology work to higher-value business priorities. Savings only become strategic when leaders decide where the released capacity will go and what result it will produce.
Fix the process before adding intelligence
AI cannot compensate for a poorly designed process. If a workflow contains unnecessary steps, unclear decision rights, inconsistent data or weak controls, adding AI may merely automate the friction or make it more expensive. A more disciplined sequence is:
- Define the business outcome. State the operating cost, profitability or competitive advantage the initiative must support.
- Map and simplify the process. Remove unnecessary steps, clarify ownership and identify the decisions that genuinely require better intelligence or automation.
- Automate what is repeatable. Use the simplest, most effective technology for stable, rules-based work rather than defaulting to AI.
- Apply AI selectively. Use AI where it adds a distinct capability, such as handling complexity or supporting decisions, and where the value can be measured.
- Govern cost and performance. Track ongoing consumption, quality, risk and business impact so the initiative does not become an open-ended expense.
This process may reveal that your problem doesn’t require AI at all. That is a useful outcome, not a failure of ambition. The objective is to improve the business, not to maximize the amount of AI deployed.
Build on the core instead of replacing it by default
Ravin also described an approach in which AI is layered over existing core systems to solve targeted business issues rather than forcing a rip-and-replace transformation. This preserves the processes that must remain stable while allowing organizations to introduce new capabilities where they can generate a clear return.
This creates a more pragmatic path to innovation. Leaders can protect reliability in the systems that run the enterprise, reduce avoidable maintenance and migration expense and direct investment toward business problems that merit change. Capacity is created through disciplined operating choices, and then invested through disciplined strategic choices.
The measure of progress is business value
The next phase of enterprise AI will require more maturity in how projects are selected and governed. Enthusiasm alone is not a business case, and technical deployment is not proof of value. Leaders need to connect each initiative to a result the organization can recognize: lower total operating cost, improved profitability or stronger competitive advantage.
That is a more useful definition of innovation capacity. It is the ability to move resources away from work that does not differentiate the business and toward initiatives that do. AI may play an important role, but it earns that role only after the strategy, process and desired outcome are clear.
Watch the full CNBC Squawk Box interview for Seth Ravin’s perspective on enterprise AI, existing core systems and a more thoughtful path to innovation.
