Enterprises to focus AI spend on cost savings & data control

Enterprise investment in artificial intelligence remains high, with organisations in North America spending an average of USD $5.4 million annually on generative AI tools, infrastructure and talent. As companies plan for 2026, technology leaders at OpenText have outlined a shift away from a proliferation of tools toward a focus on cost optimisation, data sovereignty, and the tangible business outcomes of AI deployments.

Tool consolidation

OpenText predicts that AI will increasingly be judged by the tools it replaces rather than the new features it brings. The pressure on Chief Information Officers to demonstrate cost reductions is expected to intensify, with measurable year-over-year reductions across technology estates seen as the new standard.

"In 2026, AI will be judged not by how many tools it adds, but by how many it replaces. CIOs will face pressure to demonstrate that AI is actively rationalizing applications to deliver measurable 10% year-over-year reductions across their technology estate. The real proof point will be cost optimization through secure information management: consolidating data environments, governing access, and ensuring that every AI deployment enhances, not fragments, the enterprise information landscape. Early gains will come from customer-facing and operational tools-help desk, call centres, frontline support-where generative and agentic AI can replace low risk, high volume tasks done today by humans, while at the same time improving experience. As organizations see billion-dollar efficiencies emerge, CIOs will redirect those savings into innovation and resilience, not more software," said Shannon Bell, Chief Information Officer and Chief Digital Officer, OpenText.

This approach means that rather than layering more technology on top of growing technology estates, companies are expected to measure the success of AI by its ability to bring down operational expenses and consolidate fragmented systems.

Hybrid cloud permanence

Hybrid environments are predicted to become a permanent fixture rather than a temporary solution. Bell says that the debate around cloud strategy will move from infrastructure choice to data management.

"The future of cloud is hybrid, and sovereignty will be defined by data, not infrastructure. By 2026, there will be broad acceptance that hybrid cloud is not a transitional state but a permanent one. The real sovereignty challenge isn't where the cloud sits; it's where the data resides and how securely it flows between environments. Every enterprise holds 'keys to the castle' data that must remain protected, even as it interacts with public and private AI models. According to recent OpenText and Ponemon Institute research, 73% of CIOs and CISOs say reducing information complexity is critical to AI readiness, reinforcing that secure, governed data mobility is what will enable safe, scalable AI. CIOs will focus on portable architectures, clear governance, and the seamless orchestration of information across private networks, hyperscalers, and edge environments," said Bell.

This focus on data mobility and governance reflects rising concerns around compliance, privacy and operational resilience amid growing AI adoption.

AI orchestration

Bell also anticipates that the coming years will see an evolution from AI experimentation to orchestration, with CIOs held to account for quantifiable business outcomes linked to AI investments.

"CIOs will move from experimenting with AI to orchestrating it, governing outcomes, agents, and data. AI leadership will evolve from pilots to performance. CIOs will be accountable for tangible business outcomes, defining clear frameworks that connect AI investments to enterprise KPIs and ROI. That means managing a new hybrid workforce of humans and digital agents, complete with job descriptions, correlated KPIs and measurement standards, and governance guardrails. Yet none of this will succeed without secure information management, ensuring that the data fueling and training these agents is accurate, compliant, and trustworthy. Simply put, good data results in good AI outcomes. As AI accelerates, traditional network and security operations will be reimagined for an always-on, agent-driven enterprise, where value is derived as much from data discipline as from innovation itself," said Bell.

Workforce transformation

Bell expects workforce development to adapt as organisations encourage employees to design and direct AI systems, not just execute manual processes. Companies are predicted to invest in continuous learning initiatives, aiming to increase adaptability and reduce resistance to AI-driven change.

"The AI-ready enterprise will redefine workforce development around continuous learning and change management. Workforce strategy will start to center on transforming people from task-takers to task-givers-individuals who design, direct, and evaluate AI systems rather than execute every process manually. Enterprises will invest in AI marketplaces, sandboxes, and prompt-sharing communities to accelerate hands-on experimentation, while universities and employers alike will emphasize problem-solving, critical thinking, and adaptability over static technical skills. Success will depend on a strong change management culture that reduces fear, communicates 'what's in it for me,' and ensures every employee has a stake in shaping how AI transforms work. The goal is not to automate people out of relevance, but to equip them to leverage AI to deliver higher-value, human-centered innovation and outcomes," said Bell.

Contextual AI

OpenText, believes that moving forward, context will be more critical to AI effectiveness than model size. Enterprises will need to understand their data in context, with an emphasis on traceability and explainability in AI outputs.

"Context will define the next stage of AI. The next leap in AI will come from smarter context, not bigger models. Success will depend on how well organizations understand their data, where it comes from, and what it means in different business settings. Context engineering will become essential to help enterprises get the most out of their data and connect AI results back to original sources. That's what will separate AI pilots from scalable enterprise-grade systems. When information context stays intact, AI becomes accurate, compliant, and explainable. Without it, even the best models risk producing outputs that can't be trusted," said Savinay Berry, Chief Technology Officer and Chief Product Officer, OpenText.

AI accountability

Berry predicts that incidents of AI misuse, including prompt injection attacks, could severely impact brand trust for some companies. He urges organisations to verify AI behaviour as closely as their cybersecurity protocols.

"A Major brand fallout will force AI accountability. In the next year, we'll likely see a major brand face real damage from AI misuse. It won't be a cyberattack in the traditional sense but something more subtle, like a plain text prompt injection that manipulates a model into acting against intent. These attacks can force hallucinations, expose proprietary or sensitive information, or break customer trust in seconds. Enterprises will need to verify AI behavior the same way they secure their networks, by checking every input and output. The companies that build AI systems with accountability and transparency at the core will be those that keep their reputations intact," said Berry.

Measuring ROI

Berry further notes that proving real return on AI investment will be essential in 2026, with emphasis shifting towards measurable improvements in performance and reliability rather than the volume of AI activity.

"2026 will be the year to prove real ROAI. The time for counting AI pilots and projects is over. In 2026, organizations will need to prove real return on AI investment (ROAI) through outcomes that improve performance, reliability, and customer experience. Measuring the percentage of AI-generated code or model activity doesn't say much. What will matter is whether AI shortens release cycles, improves uptime, and helps teams recover faster from incidents. When AI delivers measurable improvements in speed, quality, and stability, that's when it will become a trusted business advantage," said Berry.