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Artificial intelligence is reshaping industrials in practical, measurable ways. Companies are using it to cut waste, accelerate innovation, and strengthen operations. At Amazon, AI-driven systems reduced overstock by 30%. Pfizer has shortened drug development timelines by 20%. Siemens achieved a 42% cut in energy use at its Erlangen factory while raising productivity by nearly 70%.

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Investment signals show the same shift. More than one-third of manufacturers now allocate over 10% of their IT budgets to AI, making it a central pillar of transformation strategies. Traditional leaders are applying it too. Caterpillar uses generative AI to reduce unplanned downtime for customers. Rockwell Automation deploys AI-driven design tools that speed up automation projects.

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AI in industrials has advanced past pilots. The defining question is how quickly leaders can turn it into growth and ROI.

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Why Industrials Can’t Afford to Wait on AI

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AI adoption is advancing across industries, yet industrial companies remain behind the curve. Only 59% of firms in this sector have deployed AI, compared with 71% across the wider economy. Smaller firms are even further behind, with few able to position AI as a core driver of revenue.

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The hesitation stems from real barriers: limited AI-ready talent, fragmented data systems, and organizational silos that slow progress. These challenges extend the journey from pilot programs to scaled transformation, leaving industrials trailing faster-moving sectors.

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At the same time, the environment around them is shifting in ways that demand faster action.

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Four headwinds are converging:

1. Aging workforce: Retirements are eroding institutional knowledge, intensifying pressure on automation and upskilling.

2. Rising customer expectations: Buyers increasingly seek integrated, intelligent offerings, pushing traditional players to rethink product and service models.

3. Legacy systems and complexity: Outdated infrastructure continues to slow efficiency and delay returns on digital investments.

4. Organizational misalignment: Data leadership often sits in finance functions, leading to reactive approaches that can double the time needed to move from pilot to scale.

The cumulative effect of these pressures is severe. In year one, companies lose insights. In year two, they lose talent. By year three, they begin losing market share. The clock is ticking, and the question for industrial leaders is how quickly they can mobilize it to defend and expand their competitive position.

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The New Math of AI ROI

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For decades, digital investments in industrials faced a steep hurdle: costs were high, implementation was slow and returns often took years to materialize. A scheduling tool might improve uptime, but only after significant training, process redesign, and months of integration. ��

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AI has changed that equation. With inference costs measured in pennies and the ability to automate previously manual workflows, payback periods are shorter, and solutions can scale quickly. Instead of training entire teams to spot defects or optimize inventory, AI models can flag issues instantly, surface insights across systems, and adapt in real time. ��

The opportunity is broad. AI is already unlocking value across at least 46 high-impact use cases, spanning every major function:

• Sales and Marketing: Smarter lead scoring, automated proposal generation, real-time pricing optimization

• Supply Chain: Predictive logistics, inventory balancing, demand forecasting

• R&D: Generative product design, accelerated prototyping, materials discovery

• Operations: Predictive maintenance, digital twins, automated quality inspection

• Support Functions: Streamlined receivables, AI copilots in HR and IT, intelligent finance workflows

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What sets AI apart is its ability to scale and compound. A defect detection model deployed in one facility can be replicated across dozens of plants. A digital twin used for one product line can inform new ones. Over time, these scaled applications create a multiplier effect.

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Industrial leaders must embrace this new ROI dynamic. Those who act decisively will redefine productivity and resilience in the sector.

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Leaders Already Showing the Way

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Even as many industrial firms weigh their options, a set of pioneers are already embedding AI into their products, services, and operations. These companies show that the path to impact is real and underway today:

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• is advancing AI-driven unmanned systems, improving mission effectiveness in defense and industrial applications.

• has integrated AI into machine vision, enhancing automation and quality control on production lines.

• uses AI to optimize smart energy and storage systems, enabling more resilient and efficient power management.

• applies AI in fluid power and motion control, pushing the boundaries of industrial automation.

• leverages robotics and AI to reinvent warehouse logistics, reducing costs and improving speed.

• delivers real-time sensing and tracking solutions, helping companies gain sharper visibility into assets and workflows.

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These examples reveal talented teams driving AI strategy, strong data infrastructure that supports deployment, and a focus on meaningful problems where early wins can be scaled. ��

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offers a compelling example of how this transformation looks in practice. Under CIO , the company began its AI journey with a single data scientist solving shop-floor problems such as tool tracking and predictive inspection. Those early wins built trust and scaled into a 65-person digital team, fully digitized greenfield plants, and supply chain systems that dynamically optimize stock levels and production schedules. As Khare explained in our conversation with him, “We don’t pitch projects, we pitch possibilities. People adopt technology faster when they feel it is built for their problems.” The emphasis on co-creation and human-centered adoption has been central to Oshkosh’s success. ��

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This people-first approach echoes the perspective of , Group President of Digital Intelligence at , , “Technology is not the biggest barrier. Change management is.” ��

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Together, these insights highlight a consistent truth that scaling AI in industrials requires as much focus on culture and trust as it does on algorithms and infrastructure. The lesson is clear. Industrial AI leadership is about execution, and those who act decisively are already creating a competitive gap.

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‍Why Many AI Efforts in Industrials Fall Short

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The promise of AI is real, yet many industrial firms struggle to capture measurable returns. The issue is rarely the technology itself. Failures arise when AI is deployed on the margins of the business. Too often initiatives sit outside core processes, disconnected from strategy, operations, and outcomes.

This creates what is called the “pilot trap.” Companies run isolated pilots that never scale, giving leaders a temporary sense of progress while draining resources and attention. GE’s Predix platform Launched with great ambition as the centerpiece of the company’s industrial digital strategy, it faltered due to over-customization, weak alignment with user needs, and unclear ownership of use cases. Despite billions invested, the system could not integrate effectively across customer environments and eventually had to be restructured.

From our work, we see four recurring pitfalls that consistently derail industrial AI initiatives.

1. The hammer looking for a nail: deploying AI for the sake of novelty rather than to solve a defined business problem.

2. Archaic data infrastructure: legacy systems and unstructured data create a “garbage in, garbage out” cycle that undermines even the strongest models.

3. Siloed pilots: projects confined within single functions without cross-functional alignment or shared architecture, which blocks scale.

4. Skill gaps and change fatigue: organizations often lack AI fluency across teams, and weak change management leaves initiatives underpowered.

These pitfalls carry heavy costs. In a sector where capital is finite and margins are tight, repeated failures delay transformation and erode competitiveness. ��

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Three Essentials for Scaling AI ROI

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Industrial leaders that succeed with AI approach it with the same discipline they bring to capital investment. They focus

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1. Treat AI as an Asset
   AI should be managed like . Just as factories or equipment require upfront investment and integration before delivering consistent returns, AI demands patience and scale. The ROI curve starts with higher early costs, but as systems are reused and scaled across the enterprise, returns compound. ��

2. Targeted Data Transformation
   Many industrials wrestle with fragmented systems, legacy ERPs, and inconsistent data practices. The answer is cleaning, consolidating, and organizing the data that feeds AI. Building data lakes that unify inputs across functions enables real-time insights in areas such as revenue, materials, and headcount. This targeted approach creates a foundation for AI without the disruption of large-scale IT overhauls, and it opens the door to dozens of high-impact use cases across R&D, operations, sales, and support.

3. Give AI a Seat at the Top
   AI cannot remain buried in IT or finance. Leadership roles such as Chief Digital Officer or Chief Data Officer must report directly to the CEO and act as peers to other senior executives. With this mandate, . ��

These three essentials create the conditions for AI to move beyond pilots and deliver real ROI. They turn technology into a business asset, data into fuel, and leadership into a catalyst for change.

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Opportunistic or Holistic: Finding the Strategy That Fits

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For companies preparing to embark on their AI journey, the key question is which strategy matches the organization’s readiness. ��

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Two adoption:

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• Opportunistic path
  This approach is tactical and ROI-driven. It focuses on high-impact use cases such as defect detection, inventory counting, or automated payment collection. It works best for organizations in early stages of digital maturity that need visible wins to build momentum. , CEO of HappyRobot, : “Start simple. The longer you take to make the decision, the more of a disadvantaged position you are in.” His company delivered 100x ROI by launching with an automated payment collection before expanding into other use cases. The lesson is that value emerges in deployment, not in prolonged planning.

• Holistic path
  This approach treats AI as an enterprise-wide capability. It requires governance, a modern data foundation, and alignment across functions. It suits firms with complex operations and established digital infrastructure. , Solutions Manager and Technical Fellow at , explains the discipline required: “In manufacturing, every greenfield still carries its own legacy. OT and IT convergence and data readiness are non-negotiable if you want AI to work at runtime.” His advice is to combine near-term wins with long-term foundations. “Balance the portfolio. Prove value quickly with opportunistic wins, but in parallel build the enterprise foundations—data, edge, cloud—so you can actually scale.”

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There is no single right way to begin. Success depends on aligning ambition with readiness. Start with the path that fits today, then expand as capabilities grow.