The Next Move of the AI Revolution

Amidst all the pictures of a monkey in an astronaut suit generated by AI, behind all the poems and essays generated by Generative AI, lies the fact that Generative AI technology has the potential to kill not only jobs but companies.

As ChatGPT translated into mass hysteria, solution providers jumped on the bandwagon to embed it in some shape or form in their products or services. To be fair to them, not doing the same would have made them look like they were lagging in the technology race. Not sure if many of them realized while leveraging the technology to “enhance” their offering, that Generative AI could allow end-user companies of their products to develop better and customized solutions much faster in the future. Not immediately for sure, but that future is not far away.

The scarier part is that customized solutions will be far more capable than many vendors currently provide. If you look at the analytics solutions landscape, the analytics boom has flooded the market with vendors of all shapes and sizes. Generative AI will be a massacre for these tools.

But behind all my predictions is an assumption. The assumption is that leading providers of Generative AI solutions, ChatGPT, BARD, Hugging Face, etc., will create ecosystems where companies can leverage these technologies but train them on their data. This assumption is the basis behind my statement that vendors of various sizes will perish. They can’t match the capabilities and resources leveraged in developing and continuously enhancing these products.

There can be arguments that Generative AI will enhance vs. replace analytics products. Give me one feature of any analytics product in the market and the underlying data used in showcasing that feature. I can develop something with the help of Generative AI, which can replicate that feature. But, the icing on the cake is that my solution will keep learning exclusively from the client data, not just when using the key. While commuting to work, returning to your home, or sleeping, the tool will keep learning. Every day it will become more intelligent and smarter.

And it is not just analytics solutions providers that are at risk. Another example is supply chain planning solutions. Recently there has been an influx of “smart” supply chain planning solutions. Behind all the jazz, these solutions essentially leveraged deep learning to learn about the client data so that the planning is intelligent and customized to the client’s data. The algorithm trained on the client’s data on the cloud, and the end-users at the client used an interface to interact with the solution.

These solutions definitely provided an advantage. But when Generative AI technologies become available as part of ecosystems, it replaces the core USP of this solutions-the deep learning algorithm. This client-owned deep learning algorithm becomes more intelligent and smarter on clients’ data. Everything else, like the user interface, etc., can be built flawlessly using self-service AI-enabled tools. Creating such a solution will only take weeks when these mature generative AI tools become available.

Then there is ERP! ERP solution providers have moved from the traditional version or vision of ERP to platforms that can ingest and integrate with many tools and solutions. , But Generative AI can even disrupt what leading ERP providers currently offer. Unlike analytics and planning, Generative AI may not wipe ERP solutions easily. But the fact is, an organization, with in-house technical prowess, in an era where Generative AI technologies have become mature and where kinks like data privacy, security, and regulations have been resolved, can build an end-to-end integrated intelligent enterprise solution in less than 180 days*.

And this is where the need to transition from platforms to ecosystems comes into play. Going digital or having digital product offerings has many advantages. But the most significant advantage will be the ability to plug into populated ecosystems into other complementary and supplementary digital products.

How do you think ERP providers can prepare for the near future? Unless they have already been working on their own Generative AI solution that matches or surpasses the capabilities of ones that currently exist, these ERP solution providers will have to devise alternative strategies. And a vital element of that strategy is creating an ecosystem that helps end-users build customized solutions easily, leveraging the solution provider’s solutions as components.

Shifting to end-users, as a leader at an end-user organization, you should be able to identify possible ecosystems that you want to be part of in the future to leverage Generative AI’s full capability. This is critical to keep in sync with the rapidly advancing technology landscape. A separate article will discuss how leaders can strategize about their ecosystem strategy.

* To insert some shameless self-promotion, this is exactly the title of a book I am working on “Build a Smart Enterprise System in 180 Days for Free“. The gist of the book is that if you have the technical prowess, in the near future, you can build an end-to-end, AI -enabled enterprise solution, entirely from open source tools and technologies, in less than 180 days. One that is customized to your unique operating nuances, and is always working to learn, while you and your employees rest. Expect to roll an initial draft out this winter.