- 92% of companies plan to increase AI investments, but only 1% consider themselves mature in its implementation.
- Successful AI integration requires a clear understanding of existing business processes and sufficient, quality data.
- Real-time IoT data, such as from RFID and BLE, is essential for AI to deliver operational improvements in supply chains.
- Tracking specific KPIs and aligning AI applications with measurable business outcomes drives economic benefits.
The Problem Isn’t AI, but a Lack of Understanding of One’s Own Processes
Artificial intelligence, when combined with real-time data, is no longer purely a technological issue but a matter of strategic corporate management. The pressure to act is high. According to McKinsey, 92 percent of companies plan to increase their AI investments over the next three years.
At the same time, only 1 percent of the companies surveyed describe themselves as truly “mature” in their implementation, meaning organizations where AI is already firmly integrated into processes and delivers tangible business results.
Much of the conversation around AI in the supply chain focuses on algorithms and applications. In practice, however, performance is constrained by the data layer. Ashley Burkle’s perspective is grounded in IoT, where technologies such as RFID and BLE generate the real-time, accurate data that AI depends on to function in operational environments.
This gap between investment and maturity often comes down to a more fundamental issue: understanding where AI can deliver measurable value within existing operations – and whether the underlying data is sufficient to support it.
Historically, supply chain data has been periodic and delayed – arriving after events have already occurred. Real-time IoT data changes that dynamic by reflecting what is happening as it unfolds.
Only on this basis can the value of AI be assessed, relevant metrics identified, and investments prioritized and justified in a meaningful way.
Ashley Burkle, Director of Sales and Business Development at Identiv, makes it clear that the key lies not in the technology itself, but in companies’ ability to make impact measurable and to align decisions consistently with operational results. McKinsey also shows that tracking clearly defined KPIs for AI solutions is among the practices with the strongest influence on economic benefits.
About Ashley Burkle
Ashley Burkle is Director of Sales and Business Development at Identiv, with a strong focus on the role of IoT and identification technologies in modern supply chains. Based on her experience and insights from recent industry developments, she identifies AI as one of the most critical topics for organizations today. Her work centers on how technologies such as RFID and BLE serve as foundational data sources for AI-driven decision-making.
She highlights both the strengths and deployment considerations of these technologies, as well as the practical challenges organizations face during implementation. A key focus of her expertise is the transition from historical and predictive models to real-time intelligence in the physical supply chain and the tangible operational impact this shift enables.
AI Has Reached the Executive Level
Ashley Burkle makes it clear that the topic of AI has now reached the highest levels. “I think there is an understanding among board members and decision-makers that they need to take action regarding AI.” Those who hesitate too long risk falling behind the competition. “If you don’t act quickly and stay at the forefront of innovation, you’re already lagging behind your competitors.”
Between Potential and Uncertainty
At the same time, Burkle observes significant uncertainty in many companies when it comes to the concrete integration of AI into operational processes. While the potential is enormous, that is what makes getting started difficult. “I believe there’s almost a certain uncertainty about how we can use AI and apply it in our operational processes to improve operational efficiency, reduce costs, and, of course, offer our customers a better experience,” she explains.
This multitude of possibilities gives rise to a central question that many companies are currently grappling with: “Where do we start? What do we implement first? How do we invest first?” In Burkle’s assessment, most companies are still working through this very prioritization.
No Meaningful AI Deployment Without Process Understanding
She believes that in most cases, the constraint isn’t the AI itself – it’s whether organizations have the data, systems, and alignment required to apply it specifically where it can achieve concrete improvements.
“The companies that are successful in the short and long term focus on deploying AI where it actually improves their processes, based on the existing supply chain and available data,” says Burkle.
The quality of the data is crucial here. AI can only be as good as the information it works with. If there is a lack of clean data collection, processing, and quality, the added value remains limited.
This leads to a clear prerequisite: companies must understand their own processes in detail. Only when workflows, weak points, and data flows throughout the entire product lifecycle are transparent can it be meaningfully determined where AI can provide real value.
AI Is No Longer a Niche Topic
AI opens up an enormous range of possibilities, but without a clear understanding of one’s own operational reality, it becomes difficult to decide where to start and which applications should be prioritized. At the same time, the pressure continues to mount. AI is no longer a marginal topic but has become a strategic factor in nearly every industry.
“Governments, companies, and capital markets are talking about it, and major market players like Amazon are demonstrating very clearly just how central AI has become to the further development of business models,” says Burkle. This is why the foundation is so crucial: “Anyone who wants to successfully integrate AI must first understand their own processes. Only then can data be used correctly, meaningful use cases be defined, and investments be implemented in a targeted manner.”
To explore these challenges in greater depth, Ashley Burkle discusses the strategic role of RTLS, AI, and data in the future of supply chain management.
How are RTLS and AI changing strategic decisions? How should companies approach these future opportunities?
Ashley Burkle: RTLS and AI are fundamentally changing how strategic decisions are made in supply chains. For many years, supply chains operated in relatively similar ways, but that changed drastically within a short period of time. This is exactly where the importance of RTLS and AI becomes visible.
Companies are moving away from a more report-driven management style toward real-time operational intelligence. This means that decisions are no longer based solely on quarterly planning or static forecasts, but much more on current data and what is actually happening in the business right now. This allows companies to react more flexibly and make better decisions on an ongoing basis.
This, of course, also changes logistics. Instead of merely reacting to problems, companies can act more proactively, adjust processes earlier, and manage supply chains more effectively overall. When AI is added to the mix, this effect becomes even stronger. AI can analyze large amounts of data faster, identify patterns, and provide additional insights that serve as the basis for human decisions.
Ultimately, therefore, it is not just about technology, but also about a shift in mindset within companies, moving away from old, established patterns toward a culture in which data-driven decisions, transparency, and adaptability play a much greater role.
Who will bear strategic responsibility for data as an operational production factor in the future, and how must companies prepare for this?
Ashley Burkle: We all remember the time when the term “big data” emerged and it became clear that data was increasingly taking on the status of a key production factor alongside labor, capital, and materials. Its value has risen sharply in recent years, particularly in supply chains.
Many large companies have already responded by creating roles such as Chief Data Officer or Chief Digital Officer and establishing their own cross-functional data departments. Depending on the organizational structure, these teams are anchored in different places, but their core task is the same: to ensure that data is strategically captured, managed, and utilized.
The key point here is that data can no longer be viewed in isolation within individual departments. Today, it flows through virtually every function of a company. Anyone who wants to work more agilely and in a more data-driven manner therefore needs a central structure that assumes responsibility for data usage, data quality, and, increasingly, the sensible use of AI.
I think we’ll see even more new roles, responsibilities, and organizational models in the future. But regardless of the title, the bottom line is this: companies must prepare for an operating model based on data, real-time feedback loops, and cross-functional collaboration.
How can companies quantify the value of real-time AI and thereby justify investments?
Ashley Burkle: Companies can assess the value of real-time AI using metrics they are already familiar with. These include, for example, higher inventory accuracy, lower shrinkage, improved asset turnover, higher labor productivity, and enhanced service levels.
The advantage is that these effects do not need to be considered in isolation. They can be measured using the very same operational KPIs that companies have traditionally used to assess performance and efficiency, for example through year-over-year comparisons or by tracking specific process metrics.
Ultimately, companies should view such investments in the same way as other innovation projects: with clearly defined goals, measurable results, and a structured evaluation process. Once this connection is clearly established, the strategic value of real-time AI can be justified very well.
How robust and independent will the business model of the future be if AI and real-time capabilities are decisive factors?
Ashley Burkle: I believe that the companies that will succeed in the future will consistently align their operating models with real-time data intelligence. To do this, they use IoT, sensor technology, and AI to make their processes more transparent and easier to control.
Supply chains are currently evolving toward autonomous models, with digital twins, predictive logistics, and a seamless, sensor-based infrastructure. In this environment, real-time intelligence becomes the decisive factor for operational decisions.
This makes them less dependent on individual events or rigid planning.
At the same time, we see that technologies such as IoT, digital identity, and AI are increasingly converging and fundamentally transforming how global supply chains operate.
The COVID era was a good example of just how important adaptability is. Companies that were able to react quickly, for example through flexible delivery models or alternative fulfillment structures, were significantly more successful. This shift has shown just how crucial speed and transparency are in the supply chain.
I believe that AI will be the next major development in this area. Companies that strategically deploy these technologies and integrate them into their processes will build the most robust and independent business models in the long term.
