Is low- or no-code the secret to AI success? IBM thinks it might be, and so is launching another of its Cloud Paks, this one called “Palantir Cloud Pak for Data”, that combines them. Obviously, Palantir, and apparently primarily Palantir Foundry, is a key element in this, but the Cloud Pak still involves IBM products and professionals too. Then, of course, there’s the question of whether the new Cloud Pak could help IBM realize the gains it expected from AI, in terms of revenue and customer traction.
When I’ve talked with enterprises about AI, they’ve been optimistically pessimistic. They’re confident that AI (Artificial Intelligence and ML, or Machine Learning) could do a lot for their company. About a third say they have some experience with AL or ML in what are essentially limited and packaged solutions. While none of them said this experience was life- (or business-) changing, almost three-quarters said it was valuable.
They’re pessimistic that making that goodness happen is going to be easy for them, and some who have tried it out have been disappointed. Even enterprises with local AI/ML success admit that they aren’t confident that they could combine a bunch of limited and local AI/ML applications to create something that addresses their business overall. That, they believe, would require customization, which they associate with software development. One CIO said that “AI and ML are kind of like programming. You know programming can be great for your business, but just knowing that doesn’t move the ball much.”
That’s a fairly insightful view, actually, because using AI and ML really is a lot like programming unless you get a package that digests one or both into the specifics of the mission you’re targeting. There are AI/ML tools just like there are software development tools, but many organizations have absolutely no knowledge of AI/ML and no internal skills to use the tools available. That’s likely what IBM has in mind to address with its Palantir Cloud Pak for Data.
Cloud Pak for Data is IBM’s original offering, and it’s the foundation of the new solution. It provides for data collection and organization, as well as Watson Knowledge Catalog for unification of all data assets, as well as for compliance and security policy enforcement. Palantir lays a new layer on the result, apparently via the Catalog. Watson Studio is also generating AI models for use in various what-if scenarios, and Palantir builds an object-oriented framework on top, as well as a set of semantics for accessing and referencing both data and AI models. This, says IBM, builds a “digital twin” of the real-world operation.
What IBM and Palantir seem to be doing here is creating a kind of generalized data-analytics-centric AI framework that can be mapped to all or part of a business operation. In other words, there’s a “create-the-digital-twin” task and then a “draw-on-it-for-insight” task. I don’t have any direct enterprise feedback on the approach, which is obviously fairly new, but it does seem to at least attempt to address the problems of AI/ML use I’ve noted above.
What I think IBM intends here is to provide professional services (at least optionally) to assist businesses in building their digital twins with the Cloud Pak. With that done, and with the staff perhaps indoctrinated in the way the digital twin works, the semantic layer of Palantir would be tapped to provide ways of pulling useful results from their data, via those AI/ML models and using vertical-market specialties and special tools created by Palantir.
I think the digital-twin notion is a strong one. If you could create an AI/ML-empowered model of a business, with important elements mapped as objects and assembled into a model of the processes of the company overall, there are a lot of good things that could fall out of the organization alone, and AI/ML could be guided better to consider the important areas in the context of their real-world relationships. Every butterfly’s wings flapping in Japan don’t start a hurricane off the East Coast, after all. Some limits on analytic correlation could be important in weeding out the chaff.
The enterprises I’ve talked with who use Palantir do like it, and find it valuable, but the majority of my contacts have never used it. Neither IBM’s nor Palantir’s websites offer a really strong view of just what’s going on here, or on how the new Cloud Pak could be used and extended. That could indicate that the target customers are those companies with strong IBM account positions and an appetite for consuming detailed demonstrations and sales material.
Is that bad, you could ask? It depends on just how much impact you’re hoping for. IBM Cloud Paks aren’t small business offerings; even most mid-sized businesses wouldn’t be candidates, so loss of a bit of populist appeal might not be a big deal. However, almost half of all enterprises I talk with are not major IBM accounts, and they might be interested in something like this if it were a bit more accessible.
I thought Red Hat was a compelling acquisition for IBM because it extended its total addressable market. I’m not sure that IBM can succeed with AI/ML unless it harnesses a lot of that incremental, Red-Hat-created, breadth. I’m not sure that this offering can do that.
What it might do is serve as a blueprint for others, though. I could see a Dell or an HPE frame a broader offering with similar elements and offer it to all the enterprises and many of the SMBs as well. That would have a major positive impact on AI/ML adoption, but it would have a negative impact on IBM.
Most companies are not going to do their own AI programming. A low-code/no-code tool would be useful, but given the nature of AI/ML I’m not sure that traditional programming models for low-code/no-code would serve usefully. The digital twin concept seems to me to be a really good way of doing things. You create a model of a company or process and then build AI/ML enhancements around it, then create a front-end that lets you harness the value it creates. That’s what Palantir Cloud Pak for Data really does in the end, and the idea is great.
Not so much the positioning and marketing. IBM is one of the smartest companies I’ve ever worked with, but smartness can be a real problem about as often as it’s a real benefit. Somebody up in the dazzling IQ clouds doesn’t communicate well with the masses, and markets are made up of the masses or they’re not much of a market. With better positioning, and possibly offered in a true SaaS form, this could be something strong. They’re just not there yet, and they’re certainly giving competitors ideas they’ll likely run with. Maybe further than IBM will.
That’s especially true because it’s becoming obvious to many different kinds of companies that new technology adoption requires more than just throwing a pile of tools and APIs at a user and hoping they figure out something useful to do with it all. There are other developments in other sectors of the IT world that are emerging, and it’s only a matter of time before somebody figures out that what’s really needed are useful services, not frameworks. The frameworks are essential in that they build the useful services, but a clever vendor could jump the line and go right to the service dimension. IBM is trying that in AI, and we’ll see more of that as 2021 rolls on.