Enterprise AI & a New Dotcom?

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dotcom 2.0

Enterprise AI & a New Dotcom?

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But, here’s the thing, a couple of years ago, everyone was talking about AI, but few were doing it. This past year things changed fast; now, there is a mad rush to embrace AI or get left behind. But its easier said than done, AI tools, models, and libraries are readily available, but skills, knowledge of specific user needs, and good data are not.

It is hard to overstate how much AI is changing the world of Enterprise Information Management, Enterprise Automation, and Business Applications in general. What seemed like a flash in the pan, with some splashy announcements, a few years ago, is now making deep and long-lasting inroads. Take the case of OCR (Optical Character Recognition), a concept that has been with us for decades, with little change. Along came Computer Vision and upended the idea altogether, lifting it to unimagined highs of accuracy and relevance. We are now not just able to capture information; we can automatically comprehend the meaning of the data we have captured. It’s not only Computer Vision; it’s Deep Learning and the universe of Neural Networks that are impacting impact our sector, leaving in their wake decades of slow and, at best, incremental improvements.

Each briefing we take is often engaging in its own right, but collectively they can be fascinating. Ultimately, all the vendors we speak to are essentially trying to do the same thing, regardless of what industry category, silo, or sector they have been placed in by analyst firms. They are trying to understand (through the use of ML &AI) and make effective and timely use of enterprise information. Even under the covers, many of these disparate vendors are surprisingly similar, using much the same technology, only configuring it slightly differently to meet slightly different use cases. Think of it this way, whether you are building an enterprise-grade chatbot, knowledge management, search, or cognitive capture system. You are pulling raw data, processing it, and making it available and understandable for use by human workers and business applications.

You can check out new research on Cognitive Capture or our recent vignettes of Splunk, Sinequa, Google Workspace, Grooper, and upcoming vignettes on Knowledge Lake, LucidWorks, and Anyline. There is no shortage of examples out there. But, here’s the thing, it was only a couple of years ago, when everyone was talking about AI, but few were doing it. Now, there is a mad rush to embrace AI or get left behind. But doing so is easier said than done; the AI tools, models, and libraries are readily available, but the skills, knowledge of specific user needs, and good data are not.

Furthermore, many vendors still face commercial challenges as they are heavily reliant on large legacy customer bases running older tech. On the one hand, they are prime markets to upsell to; on the other hand, customers often see these investments as sunk costs, and if they were to rip and replace may well look elsewhere for modern AI-driven alternatives. Indeed, they may not want to rip and replace at all; they may want to rethink how they have previously operated and go in a new direction, who knows, which can challenge technology vendors dependent on license and subscription fees.

So what does this all mean? In market terms, it means a hell of a lot of startups coming into the industry, at a scale we haven’t seen since the dotcom boom. There is more investment, both into older firms like M-Files, who just snagged another $80m, Sitecore an eye-popping $1.2B. Plus oodles of money to a slew of early-stage startups coming from a surprisingly buoyant VC sector. Add tech sector M&A activity in 2020, rocketing to a peak unseen in decades. This the face of a depressing economy and pandemic. Of course, from a buyer perspective, it also means an unrivaled choice of products and services, along with a dizzying amount of associated hype and confusion.

At times it feels like the start of another dotcom era, and maybe it is. But this time around has its differences. As a simple analogy, we may think about the dotcom era as providing us with knitting needles and wool; today, we now have a pattern to go with them. So maybe then, what distinguishes this emerging boom from the dotcom era, is the fact that the new products coming to the market work well; last time around, they often didn’t. 

The challenge still ahead of us to unravel AI’s full enterprise potential comes down to the fact that we still have a severe lack of skills and, at times, a lack of vision. But that is slowly and surely starting to change, and the future is looking brighter. Which is something we can all welcome in these otherwise dark times.

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