AI and Enterprise Technology Predictions from Industry Experts for 2026
As enterprises prepare for the future of digital transformation, industry experts have shared their insights and forecasts for Artificial Intelligence (AI) and enterprise technology trends expected to shape 2026. These predictions, compiled by Solutions Review editors as part of the 7th Annual Insight Jam LIVE event, represent perspectives from top solution providers, consultants, and thought leaders across multiple technology domains including data management, cloud computing, cybersecurity, business intelligence, and workforce technology.
Addressing the AI-Readiness Gap
Guy Adams, Co-Founder of DataOps.live, highlights that despite significant investments in AI, many enterprises still struggle with AI-ready data—data that is trustworthy, governed, contextualized, and aligned to specific use cases. He predicts that in 2026, the AI-readiness gap will emerge as the primary cause of AI project failures and drive major new spending. Adams expects organizations to pivot toward operationalizing AI readiness through automated pipeline orchestration, in-line governance enforcement using policy-as-code, continuous observability, and automated data quality validation. Emphasizing the importance of DataOps, he notes that automation, orchestration, observability, testing, and governance are becoming vital for scaling AI models securely and reliably within enterprise environments.
Rise of AI Agents and Governance Challenges
Tyler Akidau, CEO at Redpanda Data, foresees a future where AI agents—autonomous software workers—will outnumber human employees by 2027, initially in the most operationally mature organizations. However, he cautions that in 2026, many companies will lack the infrastructure or readiness to adopt these agents fully, often mistaking rudimentary chatbots for true AI transformation. Akidau emphasizes a looming governance crisis, as enterprises realize that traditional identity and access management (IAM) and role-based access control (RBAC) solutions cannot keep pace with the dynamic nature of AI agents operating across diverse services. He predicts an accelerated adoption of open frameworks and shared standards such as MCP (Managed Connector Protocol) and A2A (Agent-to-Agent communication) to manage this complexity. By the end of 2026, enterprise data stacks will evolve to become “agent-ready” by default, incorporating connectivity, governance, and context provisioning for seamless human-machine collaboration.
AI Agents as Intermediaries of Digital Ownership
Carlos Armada, Head of Product at name.com, anticipates that AI agents will increasingly serve as intermediaries managing digital ownership on the web. As AI agents gain operational control, domain and hosting providers will play key roles in defining how these agents interact with online assets, shaping new frameworks around ownership, identity, and security. Clear and transparent guidelines for agent-driven activity will become essential to establishing trust and securing digital property management in the AI era.
Agent-as-a-Service and Enterprise AI Infrastructure
Tiago Azevedo, Chief Information Officer at OutSystems, projects exponential growth in the agent-as-a-service market—from $5.1 billion in 2024 to $47.1 billion by 2030. By 2026, employees will increasingly command groups of AI agents orchestrating workflows across multiple systems without toggling between various software interfaces, driving real business outcomes beyond mere platform usage. Azevedo further stresses that evolving enterprise AI workloads demand robust data infrastructure, including high-performance compute resources (CPUs, GPUs, TPUs), scalable storage, real-time data streaming, and stringent security and governance. He cites the enterprise AI data infrastructure market’s forecast valuation of $7 trillion by 2030. Importantly, as AI automates mundane tasks, it will “rehumanize” enterprise work by freeing employees to focus on creativity, strategy, and collaboration. HR leaders anticipate a 30% productivity boost per employee alongside new roles that leverage uniquely human skills.
Focus on Real Return on AI Investment (ROAI)
Savinay Berry, Executive Vice President and Chief Product and Technology Officer at OpenText, calls 2026 the year organizations must prove tangible ROAI. Moving beyond pilot projects and activity metrics, companies will need to demonstrate measurable improvements in performance, reliability, and customer experience enabled by AI. Key indicators will include shortened release cycles, increased uptime, and quicker recovery from incidents, signaling AI’s value as a trusted business enabler.
Hybrid Strategy for Enterprise Resilience
Martin Bitzinger, Senior Vice President of Product Management at Mitel, highlights that flexibility and control will become non-negotiable pillars of IT success in 2026. With hybrid cloud identified as the default enterprise strategy by analysts such as IDC, organizations will seek 24/7 reliable communications and workflows that seamlessly adapt and scale. Hybrid architectures blending on-premise and cloud environments will provide the balance of adaptability and security needed to safeguard mission-critical workloads, underpinning enterprise resilience and long-term relevance.
Responsible AI and Guardian Agents
Matt Blumberg, CEO at Markup AI, warns that following widespread generative AI adoption, enterprises are confronting the challenge of responsible AI usage. In 2026, the emphasis will shift toward managing risks and ensuring compliance before scaling autonomous AI systems. He identifies Gartner’s emerging category of “guardian agents”—AI designed to monitor other AI systems—as a crucial development poised for rapid growth. Guardian agents will help organizations verify that AI acts responsibly and mitigates operational risks, marking 2026 as the dawn of the “age of responsibility” in AI deployment.
Conclusion
The collective insights from industry leaders point to 2026 as a transformative year for AI and enterprise technology. Success will hinge on closing the AI-readiness gap, building governance frameworks for autonomous agents, investing in scalable and secure AI infrastructure, adopting hybrid cloud strategies, and ensuring responsible AI practices that produce measurable business value. As enterprises embrace these priorities, AI is set to become a foundational and trusted partner in driving innovation, efficiency, and human-centric work in the coming years.
