Blogs
January 27, 2026
Six Predictions That Will Shape AI Success in 2026
AI is entering a new era, and in 2026, the difference between progress and stalled experimentation will come down to one thing: execution.
AI is entering a new era, and in 2026, the difference between progress and stalled experimentation will come down to one thing: execution. Organizations that can stay outside of the hype cycle and focus on six key points will see measurable outcomes from their AI initiatives.
1) Agentification will fuel automation’s next act
Automation platforms are no longer limited to executing predefined steps. They’re beginning to think, plan, act, learn, and optimize, becoming orchestrators instead of simple task executors. Plenty of vendors claim agentic capabilities, but few truly deliver. Ask your technology partners to prove real autonomy (not scripted flows), measurable outcomes, system maturity, and overall transparency.
2) Agentic AI will amplify structured workflows, not replace them
Agentic AI and structured workflow automation will converge. Some processes will be primarily agent-driven but will still execute parts of the work through structured workflows. Other processes will remain structured overall, yet be enhanced by agents performing smaller, autonomous sub-tasks. This eliminates the need for your organization to “rip and replace”, avoiding a costly rebuild.
3) The “boring work” will outshine fancy AI
The flashiest AI model won’t be the one that thrives; the model with a strong foundation of data strategy and governance will win. The tip of the AI iceberg gets the attention (copilots, GenAI, agents), but the true success driver is what’s below the surface. Siloed data, unclear policies, distrust of AI outputs, and restricted/unsafe access prevent AI from delivering at scale. Put data-readiness first, then project AI outcomes.
4) Open orchestration will win and walled copilots will lose
Open orchestration will become the “center of gravity”, rewarding organizations that can connect tools, systems, data and agents across the enterprise. Data and information have to be made available and useful in a flexible, vendor-agnostic fashion (think Microsoft + Salesforce + SAP, etc.). Technology investments should have MCP or other open integration approaches that allow interaction with your systems of record and line of business applications.
5) Specific AI models will beat general models
General-purpose AI can impress, but domain-specific AI drives adoption and real value. Context is the cornerstone of relevancy for AI models, and domain-specific language models (DSLMs) deliver maximum enterprise-specific context. What that means for organizations is more accuracy, faster performance, lower computing costs, and a tailored fit to industry-specific decisions and tasks. Large language models (LLMs) are handy, but struggle to deliver relevant enterprise value.
6) 2026 is the year AI must deliver ROI or risk getting defunded
AI can’t be a science project anymore. Executives are asking:
- What’s the ROI?
- What outcomes will change?
- How fast will value show up?
- Is it worth the investment?
Many organizations have run dozens of AI pilots and proofs-of-concept with limited production success and growing skepticism. The shift will be from “can AI do this?” to “can AI do this and is it worth it?”. Countless factors influence ROI, but the keys will be choosing the right model (often DSLMs), taking an agentic approach, and defining clear measurements tied to business outcomes.
The right time to explore, build guardrails, and prepare is now. AI success in 2026 won’t come from more pilots. It will come from readiness, orchestration, and measurable outcomes. There are 3 key items to keep in mind as you navigate AI initiatives:
- Validate vendor claims with proof, governance, and outcomes
- Invest in data readiness before scaling AI—architecture, access, retention, security
- Anchor every use case to ROI with clear success metrics and executive alignment
