AI spend to focus on cost, outcomes & data in 2026 shift

Enterprise technology investment in artificial intelligence is expected to shift sharply in 2026, with a stronger focus on tangible outcomes, cost optimisation and data management, according to leaders at OpenText. As the adoption of generative AI tools intensifies, North American organisations are reportedly spending an average of USD $5.4 million per year on related technologies and talent. In the coming year, chief information officers are forecast to prioritise application rationalisation, data sovereignty and workforce transformation, as scrutiny of AI returns mounts.

AI evaluation

Organisations are moving away from acquiring a proliferation of AI tools and will instead evaluate their usefulness by the number of applications that can be replaced or consolidated.

"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 rationalising applications to deliver measurable 10% year-over-year reductions across their technology estate. The real proof point will be cost optimisation through secure information management: consolidating data environments, governing access, and ensuring that every AI deployment enhances, not fragments, the enterprise information landscape," said Shannon Bell, Chief Information Officer and Chief Digital Officer, OpenText.

Bell predicts that gains will first be seen in customer-facing and operational areas such as help desks and frontline support, where AI systems can replace low-risk, high-volume tasks. As efficiencies reach significant scale, organisations are expected to reinvest savings into further technology innovation and operational resilience rather than into additional software.

Data sovereignty

The transition to hybrid cloud environments is expected to become a permanent state for enterprises, bringing new challenges in data security and governance. Bell highlights that the sovereignty question for cloud "isn't where the cloud sits; it's where the data resides and how securely it flows between environments." As companies handle sensitive, proprietary data across multiple platforms, the emphasis will be on secure, governed data mobility and architecture portability. "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," said Bell.

This will prompt wider adoption of portable architectures and clearer data governance, supporting orchestration of information across private, public, and edge networks.

AI orchestration

The approach to AI is also set to evolve from discrete experimental projects to a more integrated, outcome-driven model. "CIOs will move from experimenting with AI to orchestrating it, governing outcomes, agents, and data," said Bell. Increased accountability for return on investment and clear frameworks aligning AI outputs with business performance will become the norm. In this environment, managing a hybrid workforce comprising both human employees and digital agents will require new governance, job definitions and KPI structures, underpinned by secure and reliable data management. "Good data results in good AI outcomes," Bell said.

Network security and IT operations are also expected to undergo transformation, adapting to support the demands of "always-on, agent-driven enterprise" models.

Workforce transformation

The changing nature of work under AI adoption will urge companies to focus on workforce development centred on continuous learning and adaptability. Bell said, "Workforce strategy will start to centre on transforming people from task-takers to task-givers-individuals who design, direct, and evaluate AI systems rather than execute every process manually." Bell adds that businesses will invest in environments to encourage hands-on AI experimentation and prompt sharing, while universities will shift towards teaching critical thinking and adaptability. Change management is cited as key to ensuring staff are engaged in shaping the evolving workplace. "The goal is not to automate people out of relevance, but to equip them to leverage AI to deliver higher-value, human-centred innovation and outcomes," said Bell.

Contextual AI

On the technology side, the next phase of artificial intelligence will be marked by growing reliance on systems that understand information context, rather than simply deploying larger models. "The next leap in AI will come from smarter context, not bigger models. Success will depend on how well organisations 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

With rapid implementation of AI, Berry foresees increased risk of high-profile brand damage from AI misuse.

"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 behaviour 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.

Proof of value

Organisations are expected to come under growing pressure to demonstrate clear business value from their AI investments.

"The time for counting AI pilots and projects is over. In 2026, organisations 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.