No Excuses: Manufacturers Must Digitize Now. Here’s How.

Manufacturing stands at a pivotal point. Despite the groundswell of public awareness around AI, many manufacturers—who represent a substantial portion of global GDP—have been slow to transition from analog to digital operations, even though the overwhelming majority have long known they need to do so. This hesitancy now threatens their very existence. As innovators and disruptors leverage AI to optimize every aspect of manufacturing, from suppliers to end consumers, those who fail to transform risk becoming obsolete, potentially at a pace far faster than they anticipate.

The opportunity is immense. Manufacturing collectively represents three-quarters of the global GDP, or $75 trillion. And the infrastructure necessary for this digital transformation is being rapidly built. According to a recent report from the Financial Times, Big Tech companies including Microsoft, Alphabet, Amazon and Meta have boosted their capital spending by 50 percent to more than $100 billion in the first half of 2024 alone, as they race to build the infrastructure supporting AI. Despite growing skepticism from Wall Street about the returns on this unprecedented investment, these tech giants are brushing off stock market jitters and pledging further investment hikes over the next 18 months. Analysts at Dell’Oro Group now expect as much as $1 trillion could be channeled into infrastructure such as data centers within five years, setting the stage for a manufacturing revolution.

In the pursuit of growth and efficiency, manufacturers face massive disruption in the age of AI. Multiple competencies will be democratized, dematerialized, demonetized and digitized, leading to a significant shift in the role of humans. Historically, humans contributed skills, generated data, designed processes and made decisions, while systems provided data access. In the world that is now emerging, the human role will shift towards fine-tuning or validating decisions and outcomes, while machines and algorithms handle the rest.

The potential for transformation in manufacturing is huge. Just as digitization revolutionized consumer-facing industries like music, movies and retail over the past two decades, manufacturing now has the opportunity to undergo a similar metamorphosis. The conversion this far, though, has been sluggish at best.

It’s time that manufacturers embrace end-to-end digitization across the entire value chain, not just in consumer-facing functions. This transformation remains elusive for most, and it leaves them vulnerable. The stakes couldn’t be higher – AI-powered manufacturing is poised to unleash unprecedented efficiency, agility, customization and innovation. Early adopters will seize market share while laggards may fall behind. They might even fail to survive.

Navigating the Roadblocks

So what’s holding manufacturers back from this essential evolution? Several roadblocks stand in the way of progress:

• Lack of urgency from boards and investors to drive long-term transformation;
• CEOs nearing retirement who are unwilling to embark on multi-year change efforts;
• scarcity of digital talent and expertise within manufacturing organizations;
• high perceived costs and unclear ROI of large scale digitization initiatives.
• And perhaps most important of all, manufacturers are reluctant to disrupt or cannibalize existing profitable analog businesses.

Coupled with the inherent complexities of manufacturing itself—capital intensive factories, intricate multi-tier supply chains, legacy equipment—these barriers have led to sluggish digital adoption in many organizations. But as competitive pressures intensify, standing still is no longer tenable. Change must happen.

To overcome these barriers and accelerate their digital journey, CEOs and boards must take concrete steps, and begin with at least one or more pilot projects. Here’s a potential framework to help you get going:

1. Scope. Set up a high-level team with the right people and advisors to assess the scope of the AI opportunity in your business. Develop an action plan that outlines required skills, capital, timeline, and desired benefits and outcomes.
   
   
2. Leader. Appoint a Chief Digital Officer to lead the transformation. This should not be delegated to business units alone—establish a cross-functional team to execute the plan.
   
   
3. Pilot. Select a pilot business unit or product line where customers, suppliers and your own infrastructure are best positioned to leverage AI. Engage all partners in this pilot.
   
   
4. Plan. For the pilot, lay out a detailed plan with milestones, costs and expected benefits. Communicate this plan internally and externally to gain investor confidence.
   
   
5. Learn. Use lessons from the pilot to scale AI across the organization. Focus on reengineering work processes and redeploying talent for more productive tasks.

Coordinated Action

As you develop your strategy, investing in digital talent and upskilling the current workforce is critical, as is modernizing IT infrastructure and data architecture. Adopting agile and DevOps methodologies can accelerate the delivery of digital solutions, while launching pilot projects and quick wins can demonstrate value and build momentum.

Fostering a culture of innovation and continuous learning is essential to sustaining digital transformation over the long term. This includes encouraging experimentation, celebrating both successes and failure and promoting data-driven decision making. Engaging with industry consortia and standards bodies can ensure alignment with industry direction and interoperability with partners and customers.

Two key areas demand attention in this transformation: process redefinition and unlocking growth. Historically, companies organized people by skills and activities due to the differentiated knowledge and priorities of each function. In the AI era, tremendous productivity can be unlocked by having systems take on the knowledge aggregation role and suggest actions, while humans fine-tune or validate the suggestions. This eliminates the coordination of cross-functional knowledge dependencies. However, this transition requires significant investment in people and change management. Many startups are already organizing themselves this way.

In addition, AI can substantially democratize domain knowledge in any field. This shifts pricing power to players closer to the end consumer or customer. For example, in the materials value chain, feedstock providers are using AI to build downstream capabilities, while material users innovate to create materials that solve their product problems. Historically, players depended on those upstream or downstream to drive innovation. AI can shift that power structure, favoring those with an appetite for investment and change, regardless of their position in the value chain. Adaptability and agility will be the key determinants of success.

The pharmaceutical and biotech industries provide compelling examples of how consumer data analytics and AI are enabling personalized medicine. Genentech, a pioneer in targeted therapies, leverages vast amounts of genomic and clinical data to develop drugs tailored to specific patient populations. Similarly, GlaxoSmithKline is using AI to identify patient subgroups that are more likely to respond to certain treatments, allowing for more targeted and effective clinical trials. These data-driven approaches not only improve patient outcomes but also expand the market for these therapies.

For customers to share information with their suppliers, they must see clear benefits in terms of improved cash flow and increased value, while being assured of data security. Manufacturers that show how data sharing can optimize inventory, reduce lead times and enhance product quality will gain the trust and cooperation of their customers. Blockchain technology can play a key role here, providing a secure and transparent platform for data exchange across the supply chain.

Pioneers Leading Transformation

Fortunately, a vanguard of digital manufacturing pioneers is leading the way, offering useful case studies for others. Companies such as Tesla in automotive, Honeywell in appliances, and John Deere in agriculture are showing what is possible through their data-centric transformations. Tesla treats vehicles as “computers on wheels,” continuously analyzing real-world driving data to refine designs and features via over-the-air updates. This approach has not only improved vehicle performance but also opened up new revenue streams. For example, Tesla’s entry into the insurance market, where premiums are adjusted based on individual driving habits, illustrates how data can be monetized in innovative ways.

Honeywell bakes sensors and AI into its products to enable predictive maintenance, usage optimization and new business models. John Deere harnesses sensor data, computer vision and machine learning to automate precision agriculture at scale. These pioneers demonstrate how embracing digital can expand markets and create opportunities for growth, not just efficiency gains.

The digital transformation of manufacturing extends beyond just AI. It’s a combination of technologies including the Internet of Things (IoT), cloud computing and big data analytics, working together to reshape the industry. IoT devices stream real-time data from machines and products, while cloud platforms enable the storage, processing and sharing of this data across global operations and partners. Advanced analytics can turn data into insights that drive better decisions, from optimizing equipment performance to predicting market demand.

This gradual digital transformation is evident all over the world, not just in the U.S. In Europe, Siemens is leveraging IoT and cloud technologies to build a “Digital Enterprise” platform that connects its entire value chain, from design to production to service. This has enabled Siemens to offer new data-driven services, such as predictive maintenance for industrial equipment.

In Asia, Foxconn, the world’s largest contract electronics manufacturer, is investing heavily in industrial IoT and AI to create “lighthouse” factories. These showcase the future of manufacturing, with connected robots, real-time production optimization and digital supply chain integration. Though some of Foxconn’s efforts have been marred by controversy, the company’s goal is to replicate this digital model across its global network of factories.

The adoption of AI-driven technologies in the manufacturing sector is not limited to large multinational corporations. Mid-sized companies are also embracing these tools to enhance their competitiveness. For instance, Fictiv, a San Francisco-based startup, operates a digitally-enabled manufacturing platform that allows businesses to rapidly prototype and produce custom parts on-demand. By leveraging a network of vetted suppliers and AI-powered project management, Fictiv helps customers bring products to market faster and more cost-effectively. Such platforms are democratizing access to advanced manufacturing capabilities, allowing smaller players to innovate and scale like never before.

Marathon, Not a Sprint

However, the path to digital transformation is more of a marathon than a sprint for most manufacturers. Legacy equipment, complex supply chains and regulatory requirements can make the journey longer and more challenging than in other industries.

Many manufacturers are taking a phased approach, starting with pilot projects in specific areas before scaling up. They are also collaborating with technology partners and industry consortia to navigate complex technical and organizational challenges. For example, the Boston-based Industry IoT Consortium (which is now part of the Digital Twin Consortium) brings together manufacturers, technology providers and academics to develop interoperability standards and best practices for IoT in manufacturing.

So while the imperative for digital transformation is undeniable, the timeline may be longer than the “adapt now or die” rhetoric suggests. Manufacturers who start the journey today, with a clear vision and a step-by-step approach, can position themselves for success in the digital age. Those who wait too long, however, may be out of luck.

While their domains differ, these leaders share common principles in their successful digital journeys. Fundamentally, it starts with an ambitious vision and firm commitment from the CEO and board. Digitization is an all-encompassing, multi-year endeavor, not a side project.

Organizationally, these pioneers reorganize around key processes and data flows rather than traditional functions. Silos are broken down as IT, engineering, manufacturing and supply chain collaborate deeply. Digital talent is acquired to augment existing teams.

Strategically, they sequence the transformation in phases, steadily building capabilities and iterating based on early wins. They often launch in customer-facing areas first to capture direct ROI, then extend step-by-step into manufacturing, supply chain and finally to suppliers. Each success generates know-how—just as each setback generates lessons—for the next phase.

The days of debating if or when to digitize manufacturing are over. It’s now or never. Manufacturers must act urgently to transform, or risk irrelevance as innovators disrupt the industry. The threat will emerge from both incumbents who adapt fastest, as well as AI-native startups that are unencumbered by legacy systems and mindsets.

For manufacturers determined to lead in this new era, the proven keys to success are clear:

• CEO-driven, board-supported commitment to long-term transformation;
• re-organizing around key digital processes and data flows, not traditional silos;
• a phased approach balancing quick wins and foundational investments;
• acquiring and empowering digital talent to catalyze change

Those who embrace this challenging journey will not just survive; they will dominate the emerging digital manufacturing landscape. They will outperform others in speed, efficiency, customization and resilience that redefine the competitive frontier. For manufacturers, the message is as unequivocal as it is clear: Adapt or prepare for a slow but inevitable death.