How vendors help generating value with generative AI

The hype around generative AI, in particular ChatGPT is still at a fever pitch. It created thousands of start-ups and at the moment attracts lots of venture capital.

Basically, everyone – and their dog – jumps on the bandwagon, with the Gartner Group predicting that it is getting worse, before it is going to be better. According to them, generative AI is yet to cross the peak of inflated expectations.

There are a few notable exceptions, though. So far, I haven’t heard major announcements by players like SAP, Oracle, SugarCRM, Zoho, or Freshworks.

Before being accused of vendor bashing … I take this is a good sign. Why?

Because it shows that vendors like these have understood that it is worthwhile thinking about valuable scenarios before jumping the gun and coming out with announcements just to stay top of the mind of potential customers. I dare say that these vendors (as well as some unmentioned others) are doing exactly the former, as all of them are highly innovative.

Don’t get me wrong, though. It is important to announce new capabilities. It is probably just not a good style to do so too much in advance, just to potentially freeze a market. This only leads to disappointments on the customer side and ultimately does not serve a vendor’s reputation.

For business vendors, it is important to understand and articulate the value that they generate by implementing any technology. Sometimes, it is better to use existing technology instead of shifting to the shiny new toy. The potential benefits in these cases simply do not outweigh the disadvantages, starting from cost of running the new technology and extending to the added business value being marginal. Sometimes technology is a solution in search of a problem (anyone remember NFT or?Metaverse?), sometimes the new technology even turns out to be outright harmful.

Although the better is the enemy of the good, not everything new is actually better than the old. Vendors as well as buyers should keep this simple truth in mind.

Specifically looking at generative AI, it is therefore important to look at what the strengths and limitations of this technology are and to map out where business scenarios map to them. For this, I have outlined a simple framework a short while ago.

In brief, value comes out of solutions that adequately address the dimensions of fluency and accuracy. Not every business challenge needs to be addressed with equal fluency or accuracy. However, and this is important, accuracy also covers bias. Bias needs to be understood and managed.

I have outlined a few examples in that article.

In the past few weeks, vendors like Cognigy, Microsoft, Salesforce and Google did some major announcements covering enterprise use cases of generative AI. In the meantime, Open AI announced version 4 of GPT. Let’s have a look what they were about and how they fit into the fluency and accuracy categories. Notably, all vendors emphasize on a human-in-the-loop functionality being embedded in their new AI features.

How do they do and what is my analysis?