At least one IT firm believes enterprises will question scaling pilots as agentic systems demand more due diligence than bargained for.
After two years of dazzling AI pilots, 2026 is the year businesses face a crucial reality check, according to one IT firm.
Elsewhere, one Big Four accounting firm, PwC, has noted: “Because AI feels easy to use, early wins can mask deeper challenges. But real results take precision in picking a few spots where AI can deliver wholesale transformation in ways that matter for the business, then executing with steady discipline that starts with senior leadership.”
According to Edward Funnekotter, Chief AI Officer, Solace, “we are moving away from the initial rush of excitement and getting back to real business value. While the models themselves continue to improve, the focus for enterprises is shifting from ‘what can this cool demo do?’ to ‘how do we run this safely in production?’”
With this thought in mind, Funnekotter has contributed four predictions defining the AI landscape in 2026…
- Production shift
After generative AI (GenAI) pilots proliferated in 2024–2025, enterprises this year will start to question large-scale deployment. Per an MIT Media Lab report, around 95% of GenAI investments yielded no returns. Going forward, production failures may expose gaps between prototypes and secure systems.
- Data access risks
Following trends about data exposure concerns in autonomous AI, including prompt injection attacks documented in 2025, the firm warns against feeding models raw data, and advocates tool-directed filtering to limit exposure, costs, and errors. - Context management
AI in 2026 will struggle with stateless interactions and context switching compared to humans. Emerging practices will extend beyond prompts to organize metadata and history dynamically, as noted in 2026 trend analyses. - Multi-agent orchestration
Multi-agent systems will gain traction, although scale will remain limited. Protocols such as (model context protocol) will enable agent-to-agent communication for specialized workflows. Event-driven coordination is predicted to improve efficiency over single-agent limits.
Taking the AI road less travelled?
Funnekotter believes that the “flashy phase of AI is peaking: “In 2026, AI will start having to earn its keep. AI’s competitive edge isn’t just in better models or smarter prompts, it’s in connecting AI to the live, operational pulse of the business from day one.”
If he is right, then readers can expect a year in which more enterprises will get down to the fundamentals of strong implementations that ensure data security, improve the context of every model, and allow agents to work together in a robust enterprise infrastructure.
