Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.
The promise of agentic AI in manufacturing, energy, utilities, and logistics is real. Autonomous agents can monitor complex assets, predict failures, optimize workflows, and adapt in real time. However, a recent MIT study found that while generative tools are widely adopted and increasing personal productivity, the majority of enterprise AI pilot efforts (95%) show zero financial return.
For industrial organizations, where downtime, safety, and efficiency directly impact the bottom line, these numbers should serve as a flashing red warning. Without a disciplined approach to scaling, AI programs risk stalling in the same ways outlined by MIT’s research.
The question becomes: How can industrial leaders avoid becoming part of that 95% failure rate? The key lies in strategic scaling, careful integration, and a focus on long-term value creation. Let’s break down the lessons and best practices.
See also: Agentic AI in Industry: The Technologies That Will Deliver Results
Lesson 1: Align AI with Business Outcomes
Perhaps the most important takeaway: AI must deliver measurable business impact. Too often, pilots focus on technical novelty (“Can the agent predict this?”) rather than economic value (“Will this reduce downtime or increase throughput?”).
To ensure impact:
- Set ROI metrics early: Define how AI success will be measured, whether through reduced unplanned downtime, energy savings, or safety incident reduction.
- Involve business leaders: Scaling efforts should not be confined to IT or R&D. Operations, finance, and strategy leaders must be aligned on objectives to ensure a cohesive approach.
- Track continuous improvement: AI agents are not “set-and-forget.” Their performance should be monitored and tuned against evolving business needs.
When AI is aligned with outcomes, it earns executive support, unlocks budgets, and scales across the enterprise.
Lesson 2: Focus on Workflow Integration, Not Just Algorithms
The MIT study highlights a common pitfall: enterprises over-invest in model performance while under-investing in workflow integration. A sophisticated agent that produces insights in isolation will fail if operators cannot easily use it.
In industrial settings, integration is everything. For example:
- Maintenance teams don’t want a standalone dashboard. They need AI-driven alerts integrated directly into their existing work-order systems.
- Operations managers need agent recommendations linked to KPIs they already track, such as uptime percentages or energy costs.
- Field workers require AI delivered through mobile interfaces that function effectively in harsh environments, with offline functionality in place when connectivity is unavailable.
True ROI emerges when AI agents are woven seamlessly into the day-to-day flow of work, augmenting humans rather than adding friction.
Lesson 3: Go Beyond Pilots—Invest in Production-Grade AI
Too often, AI in industry remains stuck in “science experiment” mode. Teams run pilots in controlled settings, but these systems rarely move into the harsh reality of daily operations. Equipment sensors produce noisy data, operators juggle competing tasks, and legacy systems resist change.
Scaling AI means planning for production from day one. That requires:
- Data readiness: Industrial AI thrives on high-quality, contextualized data. Simply pulling in raw IoT sensor feeds isn’t enough. Organizations must establish pipelines that clean, label, and contextualize data for AI agents.
- Robust infrastructure: Cloud-native platforms and scalable data architectures ensure agents can operate reliably across fleets of equipment and multiple plants.
- Operational testing: Pilots should be designed with “real-world messiness” in mind. Test how AI agents handle conflicting signals, downtime scenarios, or unexpected operator inputs.
The organizations that succeed treat AI not as a lab experiment, but as a production technology, applying the same rigor used with safety systems, control software, or industrial automation platforms.
Lesson 4: Build for Adaptability and Learning
Industrial environments are dynamic. Equipment ages, processes evolve, and external shocks from supply chain disruptions to energy price fluctuations are inevitable. Static AI systems quickly become obsolete.
Agent-based AI must be designed to learn continuously. That means:
- Leveraging feedback loops where human operators validate or override agent decisions, strengthening the model.
- Designing adaptive systems that retrain or fine-tune with minimal downtime.
- Ensuring AI solutions evolve alongside regulatory and safety standards.
Scalable AI must improve over time rather than staying stagnant or worse, degrading over time.
Scaling Agentic AI with an Experienced Technology Partner
Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure. Few organizations possess all of these capabilities, which is why many are teaming with experienced partners. Companies like Cognite bring proven technology and services that go far beyond generic AI offerings. Additionally, Cognite has a track record of successful agentic AI scaling.
In particular, Cognite’s industrial AI and data platform is built to handle the messy, contextual data that industrial systems generate, turning raw sensor streams into meaningful information layers that AI agents can actually use.
Equally important, vendors with domain expertise deliver systems that are customized to industrial environments and capable of continuous learning. This reduces the time it takes for organizations to see tangible value, while ensuring solutions remain relevant as processes and conditions change. Beyond the technology itself, a suitable partner also brings an understanding of governance, compliance, and enterprise IT requirements. Such elements are essential when scaling across multiple plants or regions.
By working with vendors who have already solved these challenges in similar contexts, industrial firms can accelerate deployment, reduce risk, and avoid reinventing the wheel. In doing so, they position themselves to realize the benefits of AI at scale, rather than getting stuck in the cycle of pilots that never reach production.
The Agentic AI Path Forward
Industrial organizations that approach AI with rigor and foresight can avoid the fate of the 95% who fail. Success depends on treating AI as a production technology rather than a series of experiments, ensuring systems are tested, hardened, and ready for real-world use. It also requires moving beyond isolated tools and embedding AI into the natural flow of work, so that operators, engineers, and managers benefit without added friction.
The main point to keep in mind is that scaling agent-based AI in industry is not about building static systems. The environments in which these technologies operate are constantly shifting, and the AI must be capable of learning, adapting, and improving over time.
