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 exactly new — it’s been around for quite some time. Even in the early days of Google’s widely-used search engine, automation was at the heart of the results. Now, the world is starting to wake up and realize how much AI is already ingrained in our daily lives and how much untapped potential it still has.
The pace of AI adoption and innovation is moving so fast – hitting around $1 trillion in expenditures – that many wonder if we can accurately anticipate the expansion of future models even two years from now. This is fueled even more so as tech companies like Meta, Alphabet, Microsoft, Oracle, and OpenAI unveil round after round of new AI advancements and models to try and keep up with industry demand. AI chip manufacturer Nvidia is growing so quickly, its business can’t even be properly valued.
What we do know about the pace of AI is that as the quantity of data increases and the quality of data continues to improve, so will AI’s ability to drive innovation for business activities, applications, and processes across every industry. In order to estimate where AI will be in just a few years, we first must understand that the use cases for AI are two-fold. The first is that it’s a technology enabler, improving existing solutions to make them more efficient, accurate, and impactful. The second is that AI has the potential to be a technology innovator by making unimaginable advancements or solutions tangible.
Rethinking AI’s Pace Throughout History
Although it feels like the buzz behind AI began when OpenAI launched ChatGPT in 2022, the origin of artificial intelligence and natural language processing (NLPs) dates back decades. Algorithms, which are the foundation for AI, were first developed in the 1940s, laying the groundwork for machine learning and data analysis. Early uses of AI in industries like supply chain management (SCM) trace back to the 1950s, using automation to solve problems in logistics and inventory management. In the 1990s, data-driven approaches and machine learning were already commonplace in business. As the 2000s progressed, technologies like robotic process automation (RPA) streamlined menial tasks across many complex and administrative business functions.
Then came ChatGPT. It’s very clear that the perception of AI has changed because of generative AI. Before the inception of GenAI, consumers didn’t understand the mechanics of automation, let alone the power of automation for businesses. AI underlies a lot of our modern technology, like the Google Search Engine. Most consumers trust Google to deliver accurate answers to countless questions, 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 started to see real-life use cases. Still, there’s a misconception of how integrated AI is in our daily lives — even in the business world. As mentioned above, AI enables existing technology to be better and, just like Intel’s microchips, AI sits in the background of the technologies we use every day.
If leaders can’t comprehend the magnitude of AI, how can they be expected to successfully adopt AI into their day-to-day business operations? That’s exactly the problem.
Adoption and Growth Challenges
If someone were to ask a GPT tool, ‘what procurement and supply chain professionals are likely to say about AI’ it will probably highlight the knowledge gaps related to AI adoption. Globally, AI adoption increased exponentially in the past year after limited growth in years prior. For the past six years, only 50% of business leaders said they were investing in AI technology across their operations. In 2024, the adoption rate jumped to 72%, showing that business leaders are just waking up to the potential of AI to enhance their organization across all lines of business.
However, realizing AI’s full value requires more than just deploying cutting-edge solutions. It necessitates having access to the right data — data that provides rich context on actual business spend patterns, supplier performance, market dynamics, and real-world constraints. Inadequate access to data means life or death for AI innovation within the enterprise. At least 30% of all GenAI projects are expected to be abandoned due to poor data quality, among other challenges such as inadequate risk controls, escalating costs or unclear business value. But there are many other challenges businesses face when adopting AI and bringing it to scale.
In large organizations, it’s unfortunately common to have silos which can expose businesses to major risks. Take, for example, the supply chain industry. The supply chain plays a critical role within business strategy and for large, global organizations, the interconnected scale of the sector 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 are not communicating changes in demand to their suppliers, how can leaders be expected to then create accurate forecasts? If the sales team isn’t communicating updated forecasts to procurement, they might secure long-term contracts based on outdated information, locking into agreements that may not align with current customer demand.
Whether it’s an organizational or informational silo, the lack of communication can lead to a breakdown in customer service, create inefficiencies, and an overall halt in innovation. AI can prove its value in addressing these silos: if their technology is efficiently 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 find success with these AI capabilities and achieve their total spend management goals, companies must work together to foster transparency and work towards a common goal.
The Next Evolution for AI
Right now, the best use case for AI that actually drives business efficiency and growth is automating simple, administrative tasks. Whether it’s workflow efficiencies, data extraction and analysis, inventory management, or predictive maintenance, leaders are realizing that AI can speed up monotonous, time-consuming tasks at unprecedented rates and with extreme precision. Although it seems simple, when leveraged in industries like the supply chain or procurement, use cases like these can save businesses countless hours and billions of dollars.
We’ve discussed AI as a technology enabler — but there is still untapped potential for AI to become a technology innovator. As we’re on the brink of a new year, there are many AI advancements that business leaders should be on the lookout for just over the horizon.
For supply chain management and procurement specifically, one of these advancements will be enhancements in autonomous sourcing. By leveraging AI and other advanced technologies, businesses can automate tasks that were traditionally relied upon by humans, such as sourcing and contracting, in order to drive efficiencies and free up resources by allowing AI to analyze vast amounts of data, identify trends, and make informed sourcing decisions in real-time. Fully autonomous sourcing not only offers unmatched cost savings by saving employee time, promoting efficiency, and reducing errors, but it can mitigate the risk of fraud and counterfeiting by continuously ensuring compliance with ethical and sustainability standards.
However, even before introducing autonomous sourcing, companies should focus on delivering 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 cohesively implement autonomous solutions.
The result of AI is not just improving businesses’ ROI, but improving decision-making, predicting future patterns, and building resiliency. C-level executives across sectors increasingly view the adoption of AI technologies as essential for transforming and future-proofing their operations through automation. Over time, like every other technology before it, AI will become increasingly inexpensive while the value of its output will continue to rise. This gives us ample reasons to be optimistic about the future of AI and the balanced role it will play in our lives — both business and personal.
