Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Ariel Katz, CEO of Sisense
Enterprise AI has moved quickly over the past few years, with speed driven by hype around scale. Large models were looked at as progress. Autonomous agents were seen as replacements for entire workflows. Natural language tools made it seem like you could ask questions and get answers quickly. For a while, intelligence appeared cheap, universal, and frictionless.
But reality comes in 2026. Companies will stop paying for potential and start requiring accountability. It’s no longer a “one size fits all” model. Intelligence becomes vertical. Agents are weighed by results. Applications won’t matter as much. Governed data and logic will take precedent. Natural language will hit its limit.
The shifts have already begun, but their impact becomes clear in the year ahead. We are moving away from testing the waters and toward structural changes in how companies construct, purchase, and use intelligence. After doing research and consulting with colleagues, I’ve developed the following predictions to lay out the direction AI is heading in 2026.
AI Deepens the Value Gap: Why 5M Vertical-Trained Params Beat 2T Generic Ones
In 2026, the universal-model fantasy collapses as enterprises realize that trillion-parameter generalists can’t match the precision of small, domain-trained models. FinTech needs risk logic, LegalTech needs argument structure, Healthcare needs diagnostic nuance – and only vertical models trained on governed, industry-specific data can deliver it. The future is an ecosystem of sharp, specialized AIs – many running on-prem, where privacy, semantics, and cost control make deep vertical intelligence inevitable.
From Agent Hype to Outcome Accountability
2025 was the year agents exploded. 2026 will be the year enterprises demand proof that they actually work. After millions spent on tokens, tools, and experiments that never reached production, companies shift from buying AI components to buying measurable business outcomes. The winners will offer outcome-as-a-service – owning the workflow, the integration, the semantics, and the last mile – because customers won’t pay for agents. They’ll pay for certainty.
Applications Become Transitional – The Value Pyramid Flips
In 2026, applications stop being the center of value as switching costs collapse and AI interfaces become interchangeable. Users stay in their existing workflows like Slack, CRM, ERP, while the real value shifts downward to the intelligence layer: the semantics, lineage, and logic embedded in the data itself. Apps become distribution, governed intelligence becomes the product, and whoever owns that wins.
Natural Language Hits a Wall Until Companies Fix Their Data
In 2026, enterprises discover the hard truth behind last year’s �ask your data anything’ hype: natural language can’t compensate for unclear, inconsistent, or poorly governed data. AI models fail not because of speed or compute, but because they’re asked to reason over semantic chaos. The future of NLQ depends on fixing meaning first – through governance, lineage, and semantic clarity.
Enterprise AI is going in a more feasible direction. Businesses are now focusing on what actually works instead of big models and shortcuts. We are learning that intelligence is only significant when it’s built on real, governed data and knowledge that shows results.
Success in 2026 will come from making smarter choices rather than bigger ones. Smaller, industry-specific models will perform better than generic ones. While the transition has already started, it will take time. It also won’t be the same across every organization.
This change doesn’t mean AI is slowing down or losing momentum. It’s actually becoming even more useful. Businesses that do well will be focused on building trustworthy systems that are simple to understand and operate. The question is no longer if AI will be adopted, but whether it will be believed in and work in a way that gets things done efficiently and accurately.
