The recent Iran-Israel conflict is beginning to reshape the economics of enterprise technology. A sustained energy shock is raising fuel, transport, and input costs, hardening inflation expectations and making capital more selective, just as investors grow less tolerant of long dated technology bets. Some Gulf states are already reviewing sovereign investment strategies as regional financial pressure rises, suggesting that a meaningful pool of global capital that has supported technology and infrastructure growth may become more selective while enterprises face greater pressure to prove returns. Capital is not disappearing; it is becoming more discriminating.
The Terms of AI Investment Have Changed
For enterprises, that changes the terms on which AI will be funded. It will continue to attract investment, but less as a result of innovation ambition and more as a test of operating leverage. Boards, investors, and lenders will back AI where it improves productivity, compresses cycle times and protects margins. They will be less patient with programs that require another round of infrastructure build before value appears.
This is where many enterprises are exposed. The constraint is rarely access to AI models; it is the cost and complexity of delivering live, trusted, and governed enterprise data to those models at speed. Many organizations are still struggling to move AI from pilots into broad production because the underlying data estate was built to serve traditional reporting needs, not a much wider set of consumers that now includes AI agents, real-time applications, operational workflows, analytics teams, and business users. In a harsher capital environment, that is no longer just an architectural challenge. It is an operating risk.
The strategic requirement, then, is not another multi-year transformation program. It is a faster path to enterprise data readiness. Every credible AI strategy requires a data strategy behind it. Preparing for AI means preparing data not only for BI, but for a broader set of consumers across the business. That demands a more modern approach built on a rich semantic layer, shared governance, consistent security, and a trusted data marketplace. It also requires a break from traditional extract-and-move data models, which multiply cost, latency and governance overhead before business value is realized.
What This New AI Economy Rewards
This creates an opening for an alternative approach to enterprise data delivery. In a tighter capital environment, enterprises need a way to serve AI agents, applications, analytics teams, and business users from the systems already in place, without multiplying copies, pipelines, and governance overhead. That points toward an enterprise data services approach built on zero-copy access, federated governance, unified semantics, and access to live, trusted data. Vendors such as Denodo are well positioned here because they offer a way to operationalize AI faster while improving reuse and tightening control.
In a market defined by more selective capital and sharper pressure on margins, the winners will not be the enterprises with the most ambitious AI rhetoric. They will be the ones that can turn AI into measurable operating leverage without allowing data cost, complexity, and governance risk to rise in parallel. That is the strategic value of the model I described above: helping enterprises prepare data once, govern it consistently, and deliver it wherever it is needed, so AI can move from promise to performance. Denodo is well positioned because it aligns closely with that requirement.
Drawing on his extensive experience across diverse industry verticals, Addy is eager to share lessons learned and success stories related to IT modernization, Customer 360, self-service analytics, operational resilience, building a data-driven culture, and establishing effective data governance.
His career, spanning tech startups and high-growth companies across four countries and three continents, gives him a global perspective and an adaptable approach to problem-solving. Addy holds an MBA from AIM Manila and an Honors degree in Computer Science Engineering, which gives him a unique edge in bridging technical know-how with business insights.