If 2024 was the year enterprises discovered AI assistants and 2025 was the year they deployed copilots, 2026 is shaping up to be the year the chemical industry encounters agentic AI—systems that don't just analyze data and suggest actions but autonomously coordinate multi-step workflows across an organization's tools and processes. The shift from copilot to agent isn't incremental. It represents a fundamental change in what AI does within your innovation operation.
For innovation leaders at specialty chemicals and materials companies, understanding agentic AI isn't an academic exercise. IBM's research shows that agentic AI is already being piloted in predictive maintenance (39% of chemical companies), customer recommendations (34%), and demand forecasting (33%). The question isn't whether these capabilities will reach your R&D and innovation workflows—it's how soon and how prepared your organization will be when they arrive.
What Exactly Is Agentic AI and How Does It Differ From Current AI Tools?
Agentic AI describes systems that can independently plan, execute, and adapt multi-step tasks to achieve defined goals—distinct from today's AI assistants that respond to individual prompts with individual outputs.
The distinction is clearest through example. Today's AI assistant in an innovation context works like this: you ask it to analyze a set of market research documents and identify competitive threats. It processes the documents and returns an analysis. Then you ask it to evaluate how those threats affect three specific projects in your portfolio. It produces that evaluation. Then you ask it to suggest risk mitigation strategies. Each step requires a human prompt. The AI is reactive—powerful, but dependent on human direction for every task transition.
An agentic system receives a goal: "Evaluate the competitive landscape for our polymer additives portfolio and flag projects that need strategic adjustment." It then independently determines the steps required—gathering market data, cross-referencing with active project parameters, assessing competitive timing, evaluating risk factors, and producing recommendations—executing each step and adapting its approach based on what it discovers along the way. The human defines the objective and reviews the output. The AI handles the workflow between those two points.
This isn't science fiction. Microsoft's Copilot Studio already enables organizations to build agents that orchestrate actions across M365 tools. The architectural foundations are being laid in enterprise platforms right now.
Where Are Chemical Companies Already Deploying Agentic AI?
IBM's data reveals that chemical industry adoption of agentic AI is concentrated in three areas that will predictably expand into innovation management.
Predictive maintenance (39% piloting): Agents that monitor equipment sensor data, predict failure probabilities, schedule maintenance windows, and coordinate with supply chain systems to ensure replacement parts are available—all without human orchestration of each step. The innovation management parallel: agents that monitor project milestones, predict schedule risks, and proactively suggest resource reallocation before delays cascade through the portfolio.
Customer recommendations (34% piloting): Agents that analyze customer usage patterns, market trends, and product performance data to generate targeted product recommendations and identify cross-selling opportunities. The innovation parallel: agents that scan customer feedback channels, identify unmet needs, and generate product development opportunities aligned with strategic priorities.
Demand forecasting (33% piloting): Agents that continuously monitor market indicators, adjust demand models, and flag significant deviations that require supply chain adjustment. The innovation parallel: agents that track market development against innovation timelines, adjusting project priorities when market conditions shift rather than waiting for quarterly review cycles to surface misalignments.
The pattern is clear: agentic AI is entering chemical companies through operational functions and will inevitably extend to innovation management as the technology matures and organizations build confidence.
What Does Agentic AI Mean for Innovation Management Specifically?
The transition from assistive to agentic AI in innovation management will reshape three fundamental processes.
Continuous opportunity scanning: Today, market intelligence gathering is periodic—teams conduct research sprints, attend conferences, or review industry reports on a cycle. An agentic system could continuously monitor patent filings, regulatory changes, competitor announcements, academic publications, and market data, then correlate findings against your innovation strategy and surface relevant opportunities proactively. Instead of discovering that a competitor filed a patent in your target space during a quarterly review, the system flags it within hours and automatically assesses the implications for affected projects.
Dynamic portfolio optimization: Current portfolio management is inherently retrospective—teams assess project status, compare to plans, and make adjustments based on what's already happened. Agentic systems could maintain a continuously updated portfolio model, running scenario analyses as conditions change and recommending rebalancing before problems materialize. When raw material costs spike for three projects simultaneously, the agent doesn't wait for someone to notice—it evaluates alternatives, assesses impact on timelines and budgets, and presents options with supporting analysis.
Automated stage-gate preparation: The most time-consuming aspect of structured innovation processes is preparing for gate reviews—assembling data from multiple sources, creating presentations, drafting risk assessments, updating financial models. Agentic systems could handle this preparation autonomously, generating comprehensive gate packages that compile the latest project data, AI-generated risk analysis, competitive context, and financial projections. The innovation team's role shifts from data assembly to strategic evaluation of well-prepared recommendations.
What Should Innovation Leaders Do Now to Prepare?
The gap between "agentic AI is coming" and "agentic AI is deployed in our innovation process" will be determined by foundational decisions organizations make in the next 12 to 18 months. Three preparations matter most.
Unify your innovation data. Agentic AI can only orchestrate workflows across data it can access. If your innovation data is scattered across disconnected tools—spreadsheets, standalone databases, email threads, and separate SaaS platforms—no agent can stitch it together into coherent action. The single most important preparation for agentic AI is consolidating your innovation data onto a unified platform where an agent can access project information, market research, evaluation criteria, and portfolio context through a single interface.
Build on agent-ready infrastructure. Microsoft's investment in agentic capabilities within the M365 ecosystem—Copilot Studio, SharePoint Agents, Teams-based workflow orchestration—means organizations running innovation management natively on M365 will have the most direct path to agentic innovation workflows. When Microsoft releases new agent capabilities (and the pace of releases is accelerating), platforms built on M365 can adopt them without re-architecture. Platforms built on separate SaaS infrastructure will need to build bridge integrations for each new capability.
Start with assistive AI now. Organizations that deploy AI assistants today are building three assets that agentic deployment will require: structured innovation data that AI can access, team workflows adapted to AI-augmented decision-making, and organizational trust in AI-generated analysis. The companies that skip the assistive phase and wait for agentic AI will find themselves trying to build all three simultaneously—a recipe for the stalled transformations that McKinsey reports affect 72% of chemical industry digital initiatives.
The evolution from reactive tools to proactive assistants to autonomous agents isn't a technology curve to observe from the sideline. It's an organizational capability that compounds. Companies building AI fluency into their innovation processes today will transition to agentic workflows far faster than those starting from scratch when the technology arrives.
The timeline for "arrival" is shorter than most expect. IBM data shows 63% of chemical executives expect AI to contribute meaningfully to revenue within three years. The companies that positioned themselves early won't just have better tools—they'll have organizations that know how to work alongside AI systems that think, plan, and act on their behalf.