Is your organization disciplined enough to extract real value from AI?
Until recently, the story of artificial intelligence (AI) has been one of disruption, as these groundbreaking new technologies promised to transform supply chain operations. Supply chain leaders have responded with a mixture of enthusiasm and caution, looking to implement AI tools where possible, while limiting risk.
In this context, it has been natural for organizations to rely on the expertise of early adopters—enthusiasts who were perceived as having the right skill set to maximize the value of AI.
But now, the page has turned.
AI tools have matured to the point that they no longer require savant-level expertise to implement and use successfully.
Going forward, the main ingredient for AI success will be organizational discipline. Companies that deliver diligent, methodical, and consistent execution will be the ones to extract the most value from their AI investments.
This is exciting news because it means that supply chain organizations are finally in the driver’s seat when it comes to AI. Global logistics stands on the precipice of transformation, and companies that commit to AI discipline will be well-positioned in the world that comes next.
In this blog, we’ve outlined three core competencies where a disciplined approach will enable AI success: data, process, and strategy.
Data discipline
One of the major advantages of AI is its ability to manage large amounts of data, identifying patterns that allow for better forecasting, and finding solutions to problems and challenges much more quickly than a human could accomplish alone.
In the context of a supply chain, these AI models use data to forecast demand, anticipate equipment breakdowns, or reroute shipping to avoid delays. But their performance in these tasks is only as good as the data they’re being fed—the quality of AI outputs relies on the quality of the data inputs. And when dealing with supply chain data, that can be a challenge.
Due to the breadth and complexity of modern enterprise supply chains, these networks generate enormous amounts of data. That data is often sourced from disparate, non-standardized systems, which often leads to gaps, errors, and redundancies that slow down the AI and make its outputs much less reliable.
In fact, data infrastructure is often the biggest barrier to successful AI implementation. For example, a recent PwC survey found that 44% of tech investments didn’t meet expectations because of data issues.
To prepare their data infrastructure for AI, supply chains should start with a data audit to uncover anomalies, duplicates, missing values, and inconsistent formatting. Because supply chain data is often spread across on-premise systems, multiple clouds, and edge locations, adopting a single integration platform for all data can help clear up many of these issues.
Once data is assembled in a central hub, good data governance can help maintain data quality over time. Clear ownership of data sources will encourage accountability and better data maintenance and contribute to better data security.
Once data discipline is in place, the organization will have confidence in the core functionality of the AI tools, and be ready to move toward process execution.
Process discipline
Change management is important with the adoption of any new technology, but AI can be particularly challenging. The conversation around AI in the media over the past few years has focused on the idea that it will require extensive retraining or eliminate many jobs altogether. Not surprisingly, this has created an atmosphere of fear, mistrust, and resistance to change among many workers.
Even without these psychological challenges, successful adoption of AI into daily operations presents difficulties. AI is not a technology that operates quietly in the background, nor does it automate human tasks wholesale (in most cases). Rather, AI integrates deeply into human-centered workflows, requiring workers to proactively engage with the technology.
This human engagement with AI is necessary both to maximize its potential value and to define its limitations.
To overcome these challenges, supply chains need to develop processes that allow workers to fully engage with AI while simultaneously creating a feedback loop to continuously manage AI adoption.
Continuous training and mentoring programs can help both new and existing employees learn from one another and keep abreast of these rapidly changing technologies. These initiatives should be coupled with an open channel of communication with leadership, as well as other stakeholders, which will encourage a sense of “ownership” of the AI tools among the workforce.
Putting in place a disciplined, day-to-day approach to these processes will go a long way toward both keeping morale high while also maximizing the value of AI investments.
Strategic discipline
AI works best when it is employed to meet company-specific objectives. In other words, AI should not be a solution in search of a problem. Without strategic discipline, AI can become an expensive distraction.
This is especially relevant to supply chain organizations, many of which have become (understandably) trapped in crisis mode, jumping from one firestorm to the next. Supply chain leaders could be forgiven for looking at AI as a means to simply make the operation more nimble. The problem is that because AI is such a uniquely customizable tool, it works much better when it is designed with specific strategic objectives in mind.
To effectively use AI, supply chain leaders should identify their strategic North Star before they begin using it. The AI implementation process consists of several pivotal decision points; handling those becomes much more straightforward when they are made against a defined strategy.
Is your supply chain trying to solve for labor shortages? Gain better visibility into supplier relationships? Improve intelligence and decision-making speed? These are all tasks AI can tackle, but the final solution will differ somewhat depending on the use case.
By applying a consistent, disciplined approach to see AI-driven strategies, supply chains can make sure that AI actually has a measurable impact on business goals and truly delivers ROI.
Fast and steady wins the race
AI is set to revolutionize global logistics, amplifying the powers of supply chain professionals so they can react more quickly to changing conditions and develop deeper knowledge of their operations with more data. None of this requires a PhD in computer science or the genius of a tech founder. It simply requires discipline and a clear plan for data, process, and strategy, centered around your business objectives. This is a case where being steady actually accelerates success—thanks to AI!
