Harnessing AI to redefine operational agility and drive growth could be a key differentiator in the near term.
Technology is no longer optional—it is a fundamental driver of business success. This does not mean it always takes center stage, but without it, businesses risk falling behind. Small and medium manufacturers now have a unique opportunity to learn from the transformation journeys of larger enterprises—including to consider alternate paths. By adapting digital strategies to their scale and needs, they can accelerate innovation, improve efficiency, and compete on a broader stage. The convergence of artificial intelligence (AI), cloud computing, IoT, and enterprise platforms provides a roadmap to rethink traditional operations while fostering resilience and agility.
Deloitte’s recent report, The Intelligent Core: AI Changes Everything for Core Modernization, highlights a critical shift in the role of core systems due to the rise of AI: “For years, core and enterprise resource planning systems have been the single source of truth for enterprises’ systems of records. AI is fundamentally challenging that model.” AI is moving core systems away from static, rigid structures, offering systems that are adaptive and predictive, transforming how businesses operate.
For smaller manufacturers, this shift underscores the importance of moving beyond static systems. By adopting modular, cloud-based ERP solutions, they can introduce intelligence incrementally without overhauling their entire infrastructure. Scalable platforms allow small and medium manufacturers to integrate AI gradually, starting with targeted applications like inventory management or demand forecasting.
Converging technologies for strategic growth
Deloitte emphasizes the convergence of AI with technologies like IoT and robotics as key drivers of transformation: “In an increasingly convergent world, enterprises would do well to explore intentional industry and technology intersections that propel innovation across boundaries.” While core technologies and enterprise systems may seem exclusive to large enterprises, smaller manufacturers can strategically adopt them to address their unique challenges.
Referring to “core transformation” implies more than digital transformation; AI is poised to disrupt what is, or should be, in the core because it drives new, accessible, capabilities. This is certainly the beginning of some sort of “data democratization” across functions, leveraging both structured and unstructured data sets. The notion of digital core is perhaps more than a merely data repository or functional vault. It is about intellectual property and pan-enterprise dynamic insights—while maintaining appropriate levels of consistency, traceability, and security of the relevant data assets.
Collaborations with technology providers or local academic institutions can help small and medium manufacturers access cutting-edge solutions tailored to their needs without heavy upfront investments. Intentional adoption of converging technologies ensures immediate and sustained value. Among other things, AI can elevate IT from a support function to a strategic enabler, allowing smaller manufacturers to use AI selectively to drive measurable outcomes.
AI-powered tools, such as shop-floor predictive maintenance, can analyze machine data to predict failures, reducing downtime and costs. Similarly, AI-driven production scheduling can optimize workflows, helping manufacturers meet tight deadlines. These high-impact, low-barrier applications of AI can deliver substantial value for small and medium businesses.
Sustainability and scalability as core principles
Deloitte also highlights the importance of balancing sustainability with technological modernization: “The AI revolution will demand heavy energy and hardware resources—making enterprise infrastructure a strategic differentiator once again.” For smaller manufacturers, this presents an opportunity to make strategic decisions that combine scalability with environmental responsibility.
Furthermore, a cloud-first strategy can help small and medium manufacturers reduce costs while enhancing scalability. Cloud services allow businesses to pay for only what they use, easing the financial burden of infrastructure investment. By investing into energy-efficient hardware and renewable energy sources, businesses can align their modernization efforts with sustainability goals.
This intersection of scalability and sustainability also extends to supply chain practices. For instance, AI-powered just-in-time inventory management can contribute to minimize waste and the environmental impact of overproduction. IoT-enabled sensors can track goods in real time, improving logistics efficiency and reducing emissions. These innovations provide operational savings and enhance a manufacturer’s environmental credentials, strengthening their position in the marketplace.
AI-enabled ways of working
The convergence of AI and enterprise digital technologies offers smaller manufacturers the ability to rethink their entire system of operations. By adopting AI-enabled ways of working, businesses can unlock new levels of scalability and agility. AI maximizes resource utilization, reduces inefficiencies, and enables faster, more accurate decision-making. As such, AI-powered analytics uncover hidden patterns, driving innovation in product design and service delivery, which gives manufacturers a competitive edge.
AI also shifts operations from reactive to proactive. For example, integrating AI into CRM systems allows manufacturers to anticipate customer needs and adjust production schedules dynamically. AI-powered chatbots and virtual assistants enhance customer interactions, providing instant support and fostering stronger relationships. This can drive significant value to end-users, such as:
- Improving knowledge management, and in turn, reducing errors and duplication.
- Minimizing essential non-value-added activities, without complex data and digital transformation investment.
- Learning from new insights (and enabling new technologies), embedding lessons into continuous improvement opportunities.
- Driving continuous efficiencies and time-to-market optimization.
The vision described by Deloitte is about an AI-enabled core aligning with what the business is doing, rather than the reverse: “In the truly agentic future, we expect to see more of these kinds of bots that work autonomously and across various systems. Then, maintaining core systems becomes about overseeing a fleet of AI agents.” McKinsey reinforces this perspective in its latest quarterly insights publication, stating: “Companies are rethinking their digital strategies, moving away from massive transformations to more modular approaches that focus on areas of greatest impact.” This modularity ensures that smaller manufacturers can scale AI capabilities incrementally, avoid the risks of large-scale overhauls, and achieve meaningful progress.
Strategic growth through AI
Smaller manufacturers can achieve long-term scalability by focusing on creating ecosystems that support seamless data exchange and collaboration. AI-driven simulations, such as digital twins, can refine processes before implementation, reducing risks and maximizing efficiency. These ecosystems improve productivity while preparing businesses for future technological advancements. Starting with high-impact, low-barrier AI initiatives like predictive maintenance and optimized production scheduling allows manufacturers to achieve immediate benefits. These small-scale efforts can pave the way for broader digital transformation, leading to sustained growth.
As ERP systems and other core technologies transform into intelligent platforms, leveraging AI to provide dynamic, real-time insights instead of relying on static records, PDM and wider PLM systems are poised to embrace similar advancements. The adoption of AI-driven PLM systems is already underway in some forward-thinking organizations, and the wider industry is quickly following suit. While transitioning from legacy systems can be complex, the promise of intelligent, predictive PLM systems is worth the effort. As AI technology matures and platforms become increasingly interconnected, enterprise platforms will evolve into dynamic, proactive solutions that enable manufacturers to make smarter, data-driven decisions and unlock new opportunities for growth and innovation.
Digital transformation and AI certainly offer smaller manufacturers a clear path toward scalability and competitiveness—pending they are not afraid of experimenting. By strategically adopting converging technologies, prioritizing sustainability, and gradually integrating AI into operations, small and medium manufacturers can modernize their processes without overstretching resources. This incremental approach might foster resilience and agility, ensuring that businesses can evolve alongside the technological advancements that will define the future of manufacturing.
