2026 Predictions for Industrial AI: A Peek into Vivek’s Crystal Ball

2025 was a breakout year for the state of AI in manufacturing. Major capex investments were announced across several verticals, the productivity crisis became impossible to ignore, and COOs began seriously exploring how AI could help turn things around. At the same time, a wave of healthy skepticism emerged. Questions around trust, scalability, and vague terminology, such as the ubiquity of terms like AI Agents, have prompted many leaders to ask what’s actually real and what’s just noise. 

I believe 2026 will be a pivotal year for all of this. On one side, we’ll see continued improvements in AI model performance, especially in reasoning and efficiency. On the other hand, industrial-grade LLMs and decision intelligence systems will start being applied to real problems—not just generating insights, but driving outcomes. With persistent workforce constraints and supply chain uncertainties, Industrial AI is poised to play a central role in determining who gains productivity and who falls behind. 

Here are three trends I believe will define that shift over the next 12 months. 

Dashboard & SaaS Fatigue Finally Pushes
Industrial AI out of 2nd Gear

For many years, Industrial AI – and Advanced Analytics – have been stuck in the proverbial 2nd gear, thanks to one too many dashboards and pattern-recognition models that provide correlations that don’t always map to the correct root causes. A fair share of pilot programs and proofs of concepts were greenlit, but very few made meaningful enterprise-wide progress. As a result, we were left with a pile of SaaS subscriptions that were providing promising results but nothing that moved the needle.  

I believe 2026 will be the year we emerge from this quagmire of a situation and acquire Industrial AI solutions that provide more than just patterns and trends in a cool, sleek dashboard. The vendors who have their finger on the pulse have already begun this journey, and like most journeys, there is more than one way to arrive at the same destination. Here are a few ways I’m seeing/expecting vendors to respond to this situation: 

• • • Causal and Reasoning AI Systems:  Causal AI - originally popularized in sectors like fintech, is now gaining traction in industrial contexts. Unlike traditional pattern recognition models, causal systems aim to answer root causes. These models don’t rely on massive data volumes; instead, they combine deterministic and probabilistic methods to identify cause-and-effect relationships and enable more accurate insights.  

    • Software + Services offerings that prioritize time to value: For the longest time since Industry 4.0, Services were seen as a necessary evil that was always optimized for minimization. However, recent happenings have shown that it is the results that matter, not that it is delivered through software and/or services.  
      

    • Outcome-based business models: One of my predictions for last year was that, similar to data silos that we have come to see, the proliferation of AI assistants will cause silos of insights. It is true- as mentioned above, end users have unlimited access to insights, but often constrained by their systems and access to data. Everyone has their own insights- but what to do with the insights remains untapped. We are already seeing the shift to outcome-based models that guarantee results.

Agent Washing to Give Way to A Continuum of AI Assistants from Chatbots to Copilots to Agents  

The term “AI agent” gained significant traction in 2025, following the momentum of generative AI in 2023 and the rise of copilots in 2024. However, this rapid adoption led to confusion, as tools with vastly different capabilities, ranging from simple chatbots to autonomous systems, were grouped under the same label. This trend, now commonly referred to as agent-washing, has obscured meaningful distinctions between types of AI assistants. 

Fully autonomous agents are unlikely to be trusted with control over critical process parameters without clear boundaries, deterministic behavior, traceability, and rules-based heuristics. As a result, we are more likely to see a structured continuum of AI assistants, each serving distinct roles based on reliability, autonomy, and task complexity: 

• • • Chatbots will continue to play a role in deterministic, rule-based applications such as troubleshooting, root-cause analysis (RCA), and workflow guidance, where consistent, predictable, and reliable output is critical. 

• • • Copilots will operate at the interface layer, supporting decision-making through summarization, recommendation, and contextual awareness, without executing direct control or initiating actions autonomously.

    • Autonomous agents will emerge in specialized roles, with varying degrees of reasoning, learning, and task delegation. These agents will be embedded within existing operational systems and will differ significantly in their scope, autonomy, and risk profile. 

To coordinate this growing diversity, orchestration mechanisms will become necessary—handling agent-to-agent communication, task distribution, conflict resolution, and compliance monitoring. Some vendors have begun exploring hybrid approaches that combine deterministic and probabilistic models, allowing them to work together, but this remains the exception rather than the majority.  

As of late 2025, much of the market is still collapsing these distinctions under the umbrella of “agents.” In 2026, I expect (and hope) the industrial AI community will move toward a more practical, layered understanding, recognizing the need for a continuum of assistants that are delivering value at not just pilots in lighthouse plants but across a network of manufacturing facilities. 

Industrial AI’s Impact on DevOps, DataOps, and MLOps to Significantly Accelerate Time to Value

While much of the focus around Industrial AI has been on its analytical and generative capabilities, there is a parallel and growing effort to address the practical challenges of deployment, management, and scale. In particular, the integration of AI into infrastructure workflows—especially across DataOps, MLOps, and DevOps—is emerging as a critical enabler for accelerating time-to-value. 

Historically, these disciplines have been rooted in IT practices, often seen as complex and resource-intensive when applied in industrial environments. DataOps, for example, has long been associated with slow, multi-step processes for cleaning, shaping, and validating data before it becomes usable. MLOps presents its own set of challenges—maintaining models in production, tracking performance, and managing version control—all of which are non-trivial in industrial contexts where data is noisy, distributed, and often incomplete. 

Recent developments in Industrial AI suggest that a number of vendors are introducing features that automate parts of pipeline generation, model validation, and even deployment workflows. These tools are not just aimed at data scientists—they are being built to support engineers, analysts, and even subject matter experts as well. As a result, the infrastructure layer is becoming more accessible and less reliant on highly specialized teams. This, in turn, may address one of the core reasons many industrial digital initiatives have struggled to scale: the overhead required to operationalize and maintain them. 

In 2026, we expect to see this area gain more traction, not as a replacement for foundational practices in DataOps or MLOps, but as a way to lower the barrier to entry and reduce the time and effort required to move from proof of concept to production. I believe we can expect significant reductions in deployment timelines for industrial AI use cases. Tasks that previously took 4–6 weeks—such as setting up data pipelines, validating models, or integrating ML workflows with production systems—could, with AI-enabled automation and tooling, be completed in a matter of hours or days. 

Summary & Recommendations: 

The industrial sector is at a critical inflection point as we step into 2026. Productivity pressures, workforce shortages, geopolitical uncertainty, tariffs, and rapid advances in AI are all converging at once. Yet many manufacturers are still reacting to these forces rather than proactively shaping their response. At the same time, manufacturing remains central to long-term competitiveness, with Industrial AI positioned as a key lever in determining who will pull ahead and who will fall behind. 

Despite the challenges, I remain optimistic about the promises of Industrial AI. My predictions outlined here reflect real progress and growing maturity across industrial AI, DataOps, and Advanced Analytics over the past few months. The industry is rapidly learning, adapting, and moving beyond experimentation toward meaningful impact. Here are some recommendations for manufacturing business and technology leaders as you’re putting together Industrial AI strategy decks for 2026:  

• • • Address decision latency head-on. For years, organizations have talked about data-driven decision-making, yet data still tends to flow bottom-up while decisions flow top-down. This disconnect slows response times and limits agility. Manufacturers should focus on empowering faster, more distributed decision-making closer to operations.

    • The Future of Industrial AI is much more than Copilots: The ideal Industrial AI initiative will feature existing analytics and machine learning models with domain-specific, causal models that blend first-principles models, rule-based logic, and optimization to enable Decision Intelligence across assets, processes, energy, quality, and the workforce.

    • Industrial DataOps is the foundational enabler, but don’t stall. DataOps is the linchpin for scalable industrial AI, but many companies have made the mistake of spending years trying to perfect data foundations. Start with focused, high-impact use cases, build momentum, and scale deliberately.