The Top Strategies for Implementing AI In Your Supply Chain

The 2020s have been a decade of instability, upheaval, and rising risk for supply chains. First, the COVID-19 pandemic triggered factory shutdowns, supply shortages, and container port gridlock from Shanghai to Los Angeles. In the years since, extreme weather and geopolitical conflicts have embroiled critical global shipping routes, including the Panama Canal and the Red Sea, complicating logistics and inflating transportation costs for companies worldwide. And more recently, escalating trade wars, the expansion of protectionist policies, and increased regulatory scrutiny are adding even more daunting variables to a supply chain landscape that looks nothing like it did in the comparatively halcyon days of the 2010s.

To take on this complex new terrain and bolster supply chain resilience, organizations are increasingly turning to the possibilities offered by artificial intelligence. The newest generation of AI technologies, including machine learning (ML), predictive AI, and large language models (LLMs), are capable of analyzing immense datasets, assessing myriad risks, and yielding valuable insights that shape strategy and decision-making. If wielded with a commensurate level of expertise and appropriate expectations, these AI platforms can give strategic sourcing professionals a potent, versatile tool to deftly navigate today’s volatile supply chains and the range of novel hazards embedded in them. 

In order to do that, however, organizations need to develop a multifaceted, carefully reasoned AI implementation strategy.

Why Do Companies Need an AI Strategy?

In order to fully appreciate the importance of establishing a clear strategy for AI implementation, it’s useful to consider the alternative. Let’s say an automotive manufacturer brings on a new AI tool. The corporation makes the capital investment based on a general expectation that the latest artificial intelligence technology will aid the firm in managing their supply chain, mitigating related risks, and eventually reducing overhead costs. Executives at this automaker have not, however, developed and disseminated an implementation strategy for the new tool. Management and other personnel are left to their own devices to adopt the AI solution and integrate it into their workflow.

Without top-down guidance and an effectively communicated roadmap, this automotive manufacturer is going to run into significant challenges. The organization may not have committed sufficient time to training its staff and cultivating the highly specialized skills required to utilize the AI platform. Because of this lack of proficiency, procurement professionals and strategic sourcing experts won’t be able to extract maximum value from the software, handicapping the company’s ability to recoup upfront costs and ultimately strengthen supply chain resilience. 

If the automotive firm didn’t lay out objectives for the AI play, meanwhile, management may struggle to convey clear expectations to staff regarding how to use the platform and what the six, 12, and 18-month visions for its implementation are. Professionals at the company may be able to access the software and even have received rudimentary guidance on using it, but they won’t understand how it fits into their day-to-day responsibilities. And without consistent, cohesive messaging coming from the top, staff members simply won’t have the initiative to actively integrate it into their tasks and assignments.

A lack of any overarching AI strategy can lead to the other extreme, too, one in which the solution is incorporated too quickly and aggressively. In this scenario, the technology can become a disruptive, even counterproductive force, alienating some employees and leading others to embrace the tool without fully appreciating its limitations. In either case, rushing headlong into supply chain artificial intelligence can usher in many of the risks associated with these tools while simultaneously compromising their coveted benefits. 

Conversely, developing a plan and establishing a clearly defined process for bringing on a new supply chain artificial intelligence tool can help the software realize its full value within an organization. Carefully orchestrated rollouts will facilitate mass adoption across teams and departments, incentivize team members to develop mastery on the platform, and give experts the opportunity to communicate strategic applications and use parameters.  

Essential Strategies for Supply Chain AI Implementation 

To get the most out of any technology, manufacturers and other businesses have to have a comprehensive understanding of three things: how that technology works; what specific value they want to gain from it; and what the process will be for integrating the tool into their operations. The strategies explored below can guide supply chain leaders toward success across these three pillars of effective implementation. 

Establish Objectives and Ownership

It may sound obvious, even trite, but a surprising number of businesses fail to comprehensively establish all their goals before bringing on an AI tool. “As a first step, companies need to identify and prioritize all pockets of value creation across all functions, from procurement and manufacturing to logistics and, ultimately, commercial,” McKinsey & Company explained in a 2021 article on supply chain artificial intelligence. The reality, however, was that less than a third of firms were actually carrying out this type of preliminary assessment, thus depriving themselves of critical “value-creation opportunities.”

But laying the groundwork for an AI strategy and accompanying rollout goes beyond defining a larger vision and a set of objectives that support it. It also requires organizations to identify the executives, managers, and other staff who will spearhead the AI implementation process, communicating expectations and monitoring progress. By keying in on advocates who will champion the tool and push for its integration, firms avoid succumbing to the inertia of the status quo. 

Develop Expertise to Optimize AI 

Because it's so new, AI is not yet a standardized technology that looks similar across competing companies and platforms. In the supply chain artificial intelligence landscape alone, there are now solutions that specialize in everything from demand forecasting and supply chain mapping to predictive maintenance and risk assessments. These are newfangled, niche software models, and nearly everyone within an organization—including the most tech-savvy early adopters—are going to need to learn how to use them effectively. 

As a result, businesses that invest in supply chain AI will have to cultivate the in-house expertise necessary to both extract maximum value from the platform and train other personnel to operate it effectively. In instances where the technology is especially complex and the scale of adoption substantial, companies may want to consider bringing on an outside consulting firm. Many large, established consultancies have a background in digital transformation, and these firms are beginning to serve as guiding hands for businesses looking for a sophisticated AI strategy that will help them navigate the slew of obstacles that come with integrating a nascent technology. 

Leverage Effective Change Management 

An established concept in business operations, change management refers to the tools, measures, and processes leaders deploy to manage significant transitional periods within an organization. Business management site BNET defines it as “the coordination of a structured period of transition from situation A to situation B in order to achieve lasting change within an organization.” 

While utilizing change management techniques may be necessary across an array of different scenarios, it’s particularly useful when organizations are adopting a new technology at scale. According to the Massachusetts Institute of Technology, specific change management measures often used during technology implementation include “establishing and communicating the business case for change, ongoing relationship building, communication and training for affected staff, redesigning business processes, and creating and sustaining groups to manage the project.”

Change management, in other words, is inextricable from an effective implementation strategy. Business leaders that want to steward a smooth integration of supply chain artificial intelligence should understand fundamental change management measures and weave them into the technology rollout. Because integrating an AI tool into a company’s processes and workflows isn’t just about ramping up efficiency and creating maximum value across an organization. It’s also about winning over the firm’s workforce—convincing, persuading, and even coaxing staff toward adoption without sacrificing motivation and morale in the process. Change management is keenly attuned to this variable, and leaders that understand and employ it will be, too. 

Use Clean, Reliable Data

As we’ve discussed in previous posts, the leading-edge AI technologies that have emerged over the past few years rely on massive quantities of data to execute on their range of dazzling functionalities. Because these artificial intelligence models use these inputs to train themselves and develop the specific proficiency required to forecast demand, anticipate equipment breakdowns, and even identify subtier suppliers, their performance in these tasks is only as good as the data they’re being fed. Erroneous, incomplete, or dirty data can lead these tools astray, resulting in flawed guidance or outputs that can’t be trusted or confidently acted on. 

Given these limitations, manufacturers implementing supply chain artificial intelligence should be vetting all the statistics, documentation, and other information serving as inputs for their AI tools. This screening process is especially important in the early stages of adoption, when initial impressions may go a long way in shaping staff attitudes toward the technology over the longer term. 

Combine Human and Artificial Intelligence 

In the early days of ChatGPT’s rapid ascendance in 2022 and 2023, a frequent talking point was the degree to which the AI platform and comparable tools were going to supplant the human workforce in a range of fields. And it’s true that these technologies are already impacting the labor market, replacing professionals in data entry and administrative roles and even threatening more highly-skilled positions like computer programming and video game development. But just because the AI revolution is starting to reshape the job landscape doesn’t mean that these platforms are functioning exactly like independent human workers. 

In fact, it’s very much the opposite. To carry out the more sophisticated, higher-level tasks that supply chain professionals are turning to them for, AI tools require human management and oversight. While supply chain artificial intelligence can process and analyze far more data than their human counterparts, they also lack the seasoned expertise, contextual awareness, and cross-referencing skills that longstanding professionals possess. Due to these current limitations, AI’s emerging role in supply chain management should be seen as that of a collaborator—a tool that can expand a company’s efficiency and optimization, but that also requires human judgment to shore up its blindspots and deficiencies.

Achieving Supply Chain Resilience in a Turbulent Era 

Just a few years into the putative AI revolution, U.S. businesses are adopting supply chain artificial intelligence solutions in sweeping numbers. A recent study carried out by IT company Capgemini found that over two-thirds of supply chain organizations are utilizing AI for traceability and visibility. Seventy percent of manufacturers, meanwhile, are drawing on the solutions to carry out predictive maintenance. The rationale behind this rush to develop an AI strategy for supply chain management goes beyond just the mesmerizing effects of a shiny new technology or the creeping dread of falling behind while your industry takes a transformational leap forward. Today’s sourcing and procurement professionals are navigating a layered, dynamic field at the mercy of any number of hazardous variables, and they can use as much actionable data and intelligence as they can find. 

Manufacturers and other businesses looking for high-utility technology uniquely suited to the rigors of the supply chain landscape can find a great deal of value in supply chain resilience tools. Industry-leading SCRM platform Z2Data, for example, offers a suite of interrelated features that helps businesses bolster their visibility, sustainability, compliance, and risk management measures. Drawing on a robust combination of human expertise, artificial intelligence, and large proprietary databases, Z2Data provides customers with substantive insights that help them minimize threats, maximize transparency, and gain greater control over their supply chains. 

To learn more about Z2Data and the array of supply chain functionalities it offers customers, schedule a free demo with one of our product experts.