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It’s stunning how much can change in a matter of months. Over the past couple of decades, Artificial Intelligence (AI) has already revolutionized industries, technologies and experiences. Now, within just the past six months, generative AI has started proliferating nearly every facet of our lives – even if we don’t recognize it (yet). With it, distributors can do everything from sharpening up their sales correspondence to task automation.
According to McKinsey, generative AI involves applications that apply deep learning at a whole new level. McKinsey points out, “Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.”
In a nutshell, generative AI is applied AI for everyone. It involves general-purpose large language models (LLM) and is cross-discipline, cross-domain and cross-application.
Generative AI came to the forefront with the introduction of OpenAI’s ChatGPT and GPT-4. These were immediately followed by the release of similar large language model (LLM) applications from other well-established technology companies. Perhaps even more critically, companies such as Microsoft, Google, Adobe, Meta and more started integrating generative AI into their products as fast as they could. The technology is being used to power new features that greatly enhance productivity across industries – for everyday users, no training required.
This means, while you may not be directly interacting with ChatGPT or another LLM, you may be using technology fueled by generative AI. It is rapidly becoming central to nearly every technology we interact with.
With ChatGPT, OpenAI was the first company to make general-purpose LLMs accessible to anyone and everyone. That is one of the most significant changes. The next most significant factor is that generative AI can understand plainspoken language, as if speaking directly from one person to another. This doesn’t only have implications as far as usability, but also as far as applicability. For example, a chatbot powered by generative AI has an improved range of understanding over previous chatbot capabilities.
Its strengths of usability and applicability have motivated technology companies to build application programming interfaces (APIs) around generative AI applications and integrated them with other tools. The transformation goes beyond writing, allowing individuals to near-instantly generate art, video and code, and to even have the technology to perform tasks within your systems.
Generally, McKinsey data shows that a majority of use cases for generative AI fall into four categories:
Distributors can use generative AI to solve a range of concerns and seize new opportunities for their businesses.
In the most basic use of generative AI, distributors can use the technology to improve their email correspondence and sales-enablement materials. They can also use it to develop targeted sales literature for targeted marketing initiatives.
However, that’s only the beginning. Generative AI can:
With generative AI, distributors can optimize customer service from many angles. Firstly, with data analysis powered by this technology, they can better understand how their customer service reps perform and identify improvements. They can also use analysis to better understand their customers, learning invaluable information such as how customers prefer to interact, at what point they make certain decisions and what might signal that they’re unsatisfied.
Similarly to sales, generative AI can also help with training and task and process automation, such as ensuring prompt communications over customer service matters. Further, chatbots fueled by generative AI are much more intuitive with natural language than previous technologies.
Generative AI has the capacity to analyze data and deliver not only the facts – but the distinct possibilities. It can make intelligent predictions based on a wide swath of data. The use cases for this apply across the business. Distributors can make more informed decisions about their operations, from inventory to finances and logistics. They can also use generative AI to better understand business risks and analyze supplier and customer performance (Medium).
McKinsey estimates that generative AI, paired with the appropriate technologies, can achieve a level of task automation that relieves 60 to 70 percent of employees’ workload. Such task automation can be applied across a range of departments, allowing employees to take on new, higher-level work that fuels business value. Distributors should plan to optimize the opportunities that arise from this, planning role transitions and the appropriate training to keep employees on an upward trajectory, maintain their workforce culture and continue growing their business.
Distributors can use data analysis to better understand how their inventory performs and to determine the best next steps to optimize it. This includes everything from identifying and eradicating dead stock to increasing stock based on expected demand.
Microsoft is embracing generative AI across its entire product lines and even more features are sure to come. For distributors running on Microsoft products or looking to do so, the impact will be tremendous. Here are just a few recent examples of releases and collaborations:
Transforming how we work. In March, Microsoft released Microsoft 365 Copilot, which uses generative AI and your business data to work side-by-side with employees in Microsoft platforms. According to their data at the time, 88 percent of developers said they were more productive using the GitHub Copilot (Microsoft).
Savings sales teams time. In February, Microsoft announced a new GPT seller experience in Viva Sales that uses generative Ai to make relevant and comprehensive email content suggestions, remind sellers of certain tasks and more (Microsoft).
Enhancing factory operations. Microsoft and Siemens announced a collaboration specifically for the industrial sector that leverages generative AI to improve factory automation and operations (Siemens).
Fighting cybercrime. Microsoft and Rubrik announced an integration that uses generative AI and Natural Language Processing (NPL) to reduce the time it takes to recover from a cyber incident (Rubrik).
This list is by no means exhaustive. The possibilities with generative AI are innumerable, and they will only increase over the next week, month and year. If you’re interested in how generative AI can drive value for your distribution business, start a conversation with our experts today.
Also, you can learn more from our session, 3 Ways AI Impacts the Bottom Line for Distributors and Manufacturers, featured in MDM’s Profitability Summit, which is now available on-demand.
As the Global Microsoft Solutions Evangelist, Robert is responsible for helping our clients with their end-to-end digital transformation journey within the Microsoft solutions and Cloud ecosystems. With over 20 years’ experience in the Microsoft channel – he helps our clients with Hybrid Cloud architectures, strategic and technical road-mapping, DevOps automation, Packaging, and deployment, navigating Microsoft App Source, partner relationships and more.