Artificial intelligence has become a pervasive force in every facet of our lives. The business world is no exception. Thanks to the digital trends of the past few years, digitization has reached a point where the data required to train an AI is readily available in most companies; you just need to flip the switch. What happens next? We are mere years, or even months, away from experts and managers being astounded by the latest developments. Who will be needed, and who will be replaced?
LET’S TAKE A GLIMPSE INTO THE FUTURE:
The questions that a manager in a data-driven company asks today to make decisions for improving operations, reducing costs, or achieving strategic goals will be answered promptly and enthusiastically by artificial intelligence in the future. For instance:
„What is the on-time delivery rate of my suppliers?” „Why are we failing to meet our deadlines?”
Experts can currently answer these and similar questions by analyzing data. However, AI has access to this data and can provide answers much more efficiently and quickly, presenting the information in easily comprehensible formats and analyzing vast amounts of data to identify correlations. But if it can effortlessly answer such questions, what can it do with broader inquiries?
„What steps should I take to enhance my company’s procurement process for greater efficiency?”
„What is the on-time delivery rate of my suppliers?” „Why are we failing to meet our deadlines?”
Experts can currently answer these and similar questions by analyzing data. However, AI has access to this data and can provide answers much more efficiently and quickly, presenting the information in easily comprehensible formats and analyzing vast amounts of data to identify correlations. But if it can effortlessly answer such questions, what can it do with broader inquiries?
„What steps should I take to enhance my company’s procurement process for greater efficiency?”
I posed this question to one of the AIs available to the public today and received a sensible response: Implement Strategic Sourcing, Embrace Digital Transformation, Enhance Supplier Relationship Management (SRM), and so on. Delving deeper, I effortlessly reached insights I would need to lead such a company, even though these were not the goals I initially had in mind. AIs trained on our company’s data can provide pertinent and concrete solution suggestions based on our data, without requiring expert or managerial experience. From there, it’s only a short step to granting the AI the control to implement these solutions. And the icing on the cake will be when the initial question is posed not by a human but by an AI.
DOES THIS SOUND EXCITING OR FRIGHTENING?
We encounter utopian scenarios like this frequently, especially when we delve into this subject. We also witness many solutions already in place. An excellent example is automated factories that efficiently produce goods without minimal human intervention. These factories can perform well-defined, unchanging tasks much more efficiently than any human has before. Moreover, solutions and opportunities continue to expand daily, thanks to the digitalization trends of recent years, which provide the ideal platform for artificial intelligence because its primary requirement is data.
Returning to the procurement example, I received a suggestion from the language model: point 5 – Implement Procurement Analytics, which entails solutions for data analysis, visualization, and measuring KPIs… Sound familiar? Many Business Intelligence (BI) solutions can perform these tasks, and we encounter them in various settings. What makes things exciting is what follows: discovering patterns and anomalies and even predicting future trends using machine learning algorithms. These are what we currently refer to as artificial intelligence in the business world, though they are still characterized by their ability to thrive in specific environments.
HOWEVER, WITH THE ADVENT OF LANGUAGE MODELS, AI IS ENTERING NEW TERRITORY: PROCESSES THAT REQUIRE HUMAN INTERACTION.
An example already in operation is sales robots that identify our customers and recommend products based on their previous purchases and browsing history. These chatbots can engage in conversations, genuinely assist customers, and only connect them to a live salesperson when necessary.
Examining our procurement process, we can implement a similar approach. Now, we can pose questions in natural language, and AI can provide relevant answers based on our data, much like what I described at the beginning of this article.
Does this work in reality?
We are very close to it. One example is the latest development from our software partner Celonis, which combines language models with their established process mining technology to address business questions using human language and a data-driven approach. All this is presented in a familiar chat interface, where the response isn’t limited to text but includes visualizations that can be refined and expanded through further interactions. With the right questions, you can quickly discover, for instance, the best practices in your company worth disseminating widely.
For this to work, of course, you need access to the necessary data. Collecting, organizing, interpreting, and analyzing the right data is a complex process, and expert assistance in these tasks is crucial for success. After data collection and analysis, the results need to be translated into actionable steps. To ensure sustainable value creation, the entire organization must transition from being data-driven to becoming a data-driven enterprise. (You can reach Celonis Blog HERE.)
Examining our procurement process, we can implement a similar approach. Now, we can pose questions in natural language, and AI can provide relevant answers based on our data, much like what I described at the beginning of this article.
Does this work in reality?
We are very close to it. One example is the latest development from our software partner Celonis, which combines language models with their established process mining technology to address business questions using human language and a data-driven approach. All this is presented in a familiar chat interface, where the response isn’t limited to text but includes visualizations that can be refined and expanded through further interactions. With the right questions, you can quickly discover, for instance, the best practices in your company worth disseminating widely.
For this to work, of course, you need access to the necessary data. Collecting, organizing, interpreting, and analyzing the right data is a complex process, and expert assistance in these tasks is crucial for success. After data collection and analysis, the results need to be translated into actionable steps. To ensure sustainable value creation, the entire organization must transition from being data-driven to becoming a data-driven enterprise. (You can reach Celonis Blog HERE.)
Finally, if all the above steps align, it becomes possible to implement the latest solutions, whether forward-looking learning algorithms or artificial intelligence supported by language models for analysis. The good news is that you don’t need to overhaul the entire company at every level; you can begin by selecting a specific process and working through the steps.
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