Since the emergence of ChatGPT, the world has entered an AI boom cycle. But what most people don’t realize is that AI isn’t really new: it’s been around for a while. Even in the early days of Google’s widely used search engine, automation was central to results. Today, the world is starting to wake up and realize how ingrained AI is already in our daily lives and what untapped potential it still holds.
The pace of AI adoption and innovation is changing so quickly – reaching approximately $1 trillion in spending – that many wonder if we can accurately anticipate the expansion of future models, even two years from now. This is further fueled as technology companies like Meta, Alphabet, Microsoft, Oracle and OpenAI each unveil new AI advances and models in an attempt to meet industry demand. AI chip maker Nvidia is growing so quickly that its business can’t even be properly evaluated.
What we know about the pace of AI is that as the quantity of data increases and data quality continues to improve, AI’s ability to drive business innovation , applications and business processes across all industries are also increasing. In order to estimate where AI will be in just a few years, we must first understand that the use cases for AI are two-fold. The first is that it is a technology facilitatorimproving existing solutions to make them more efficient, precise and impactful. The second is that AI has the potential to become a technology innovative by making unimaginable advances or solutions tangible.
Rethinking the pace of AI throughout history
Although it seems that the buzz behind AI started when OpenAI launched ChatGPT in 2022, the origin of artificial intelligence and natural language processing (NLP) dates back several decades. Algorithms, which form the foundation of AI, were first developed in the 1940s, laying the foundation for machine learning and data analysis. The first uses of AI in industries such as supply chain management (SCM) date back to the 1950s, using automation to solve logistics and inventory management problems. By the 1990s, data-driven approaches and machine learning were already commonplace in businesses. As the 2000s progressed, technologies like robotic process automation (RPA) has streamlined menial tasks in many complex business and administrative functions.
Then came ChatGPT. It is very clear that the perception of AI has changed thanks to generative AI. Before GenAI was created, consumers didn’t understand the mechanics of automation, much less the power of automation for businesses. AI is the basis of much of our modern technology, such as the Google search engine. Most consumers trust Google to provide accurate answers to countless questions, but they rarely consider the complex processes and algorithms behind how those results appear on their computer screen. But seeing is believing: with ChatGPT, the world has started to see real-world use cases. Yet there is a misconception about integrating AI into our daily lives, even in the business world. As mentioned above, AI helps improve existing technology, and just like Intel’s microchips, AI sits in the background of the technologies we use every day.
If executives cannot understand the magnitude of AI, how can they successfully adopt it in their daily business operations? That’s exactly the problem.
Adoption and growth challenges
If someone asked a GPT tool “what procurement and supply chain professionals are likely to say about AI,” it would likely highlight the knowledge gaps related to adoption of AI. Globally, AI adoption has increased exponentially over the past year, following limited growth in previous years. Over the past six years, only 50% of business leaders said they are investing in AI technology across their operations. By 2024, the adoption rate has climbed to 72%, showing that business leaders are just beginning to realize the potential of AI to improve their organizations across all industries.
However, realizing the full value of AI requires more than deploying cutting-edge solutions. This requires having access to the right data, that is, data that provides rich context on real business spending patterns, supplier performance, market dynamics and real-world constraints . Insufficient access to data is a matter of life and death for AI innovation within the enterprise. At least 30% of all GenAI projects should be abandoned due to poor data quality, among other challenges such as inadequate risk controls, increasing costs or unclear business value. But businesses face many other challenges when adopting AI and deploying it at scale.
In large organizations, it is unfortunately common to have silos which can expose companies to major risks. Take, for example, the supply chain industry. The supply chain plays a vital role in business strategy and for large global organizations, the interconnected scale of the industry is almost unimaginable. If one facet of the business operates in a silo, it can put the entire organization at great risk. If supply chain teams don’t communicate changes in demand to their suppliers, how can executives be expected to make accurate forecasts? If the sales team does not communicate updated forecasts to purchasing, they may enter into long-term contracts based on outdated information, thereby committing to agreements that may not align with current customer demand.
Whether it’s an organizational or informational silo, lack of communication can lead to a breakdown in customer service, create inefficiencies and a general shutdown of innovation. AI can prove its value in overcoming these silos: if their technology is effectively connected, then their employees and suppliers can be too.
Business leaders are actively investing in AI-powered solutions to drive process automation, strategic sourcing capabilities, spend visibility and control, and overall profitability. To succeed with these AI capabilities and achieve their total spend management goals, companies must work together to drive transparency and work toward a common goal.
The next evolution of AI
Right now, the best use case for AI that actually drives business efficiency and growth is the automation of simple administrative tasks. Whether it’s workflow efficiency, data extraction and analysis, inventory management or predictive maintenance, executives are realizing that AI can accelerate monotonous and time-consuming tasks to unprecedented rhythms and with extreme precision. While it seems simple, when leveraged in industries like supply chain or procurement, such use cases can save businesses countless hours and billions of dollars.
We have discussed AI as a technology enabler, but there is still untapped potential for AI to become a technology. innovative. As we head into a new year, business leaders should be on the lookout for many AI advancements on the horizon.
For supply chain management and procurement in particular, one of these advancements will be in improving autonomous sourcing. By leveraging AI and other advanced technologies, businesses can automate tasks that humans traditionally rely on, such as procurement and contracting, to gain efficiencies and free up resources by enabling AI to analyze large amounts of data, identify trends and make decisions. informed sourcing decisions in real time. Fully autonomous procurement not only provides unparalleled cost savings by saving employees time, driving efficiency and reducing errors, but it can also mitigate the risk of fraud and counterfeiting by continuously ensuring the respect for ethical and sustainable standards.
However, before even introducing autonomous sourcing, companies must focus on providing a user experience (UX) that is intuitive, efficient and easy to navigate for both procurement teams and suppliers. Once a hyper-personalized UX is created, businesses can consistently implement standalone solutions.
The result of AI is not only to improve business ROI, but also to improve decision-making, predict future trends and build resilience. C-suite executives across industries increasingly view the adoption of AI technologies as essential to transforming and future-proofing their operations through automation. Over time, like every other technology before it, AI will become cheaper and cheaper while the value of its production continues to rise. This gives us plenty of reasons to be optimistic about the future of AI and the balanced role it will play in our lives, both professional and personal.
