AI will drive bottom line benefits for IBM through software and consulting, says CEO Arvind Krishna, but the infrastructure business will benefit indirectly.
As IBM turned in its latest numbers at the end of a quarter that saw it announce plans to acquire Apptio to bolster its own automation and AI capabilities, Krishna expanded on the firm’s strategic thinking in this space:
AI is a transformative technology that has the potential to unlock tremendous business value. According to a recent McKinsey study, AI could add up to $4.4 trillion annually to the global economy. Our focus is on enterprise AI, designed to address these opportunities and solve business problems. The list of use cases is long and includes IT operations, code generation, improved automation, customer service, augmenting HR, predictive maintenance, financial forecasting, fraud detection, compliance monitoring, security, sales, risk management, and supply chain amongst others.
As for his thesis that bottom line benefit for IBM will come mainly from consulting and software, he compared the current industry shift to what happened with hybrid cloud in consulting:
Where we began with this journey in 2019, our book of business was, to be precise and to round it out, zero. In the first year, we signed about $1 billion of business. And at this point, we have from inception to date signed $9 billion [of business] with an annual run rate of $2 billion in consulting. I would tell you [we] expect that we’ll play out AI in a similar way. I hesitate to call it anything else until we get six months or so down the road, in which case then we’ll have more knowledge.
Clients want help with AI, he said, and IBM is tooling up to provide that:
We have over 20,000 data and AI consultants, and recently launched our new Center of Excellence for generative AI, already staffed with more than 1000 consultants with specialized generative AI expertise. The investments we’re making in products and skills will help us to seize the AI opportunity. Our path is clear – in the same way we have built a consulting practice around Red Hat’s hybrid cloud platform that is now measured in the billions of dollars, we will do the same with AI.
From the software perspective, AI is being infused across the product range, stated Krishna:
We are already building products that address specific enterprise use cases, such as digital labor with Watson Orchestrate, customer service with Watson Assistant, and co-generation with Watson Code Assistant. And at our Think Conference in May, we announced what’s next – our enterprise-ready AI and data platform to help clients and partners capitalize on the AI opportunity.
This past week has seen the release of Watsonx, IBM’s generative AI platform pitched at helping enterprises design Large Language Models (LLMs) to meet their specific operational and business requirements. Krishna said:
Large Language Models are a step-change in the evolution of AI, with more than 80% of enterprises exploring their use. We believe the opportunity for Large Language Models for enterprises is immense, given the existing amount of business data. This includes sensor data, chemistry data, material data, geospatial data, code, and, of course, speech. Because enterprise AI draws from both public and private data, it is more effectively trained if companies adopt a hybrid cloud approach. Enterprise AI can also be based on multiple models, including public, private, and open-source.
Our Watsonx platform takes into account this reality and is differentiated in a few important ways. For instance, instead of relying on a single model, Watsonx enables companies to leverage the best models to meet their needs, whether they are open-source technologies, IBM’s models, or those co-created with us. Another fundamental aspect of Watsonx is trust, ensuring transparency and bias-free models. In addition, beyond offering companies the capability to tap into existing AI models, IBM empowers them to create their own.
Real world client use cases for Watsonx are already being seen, he added with the platform having been shaped by more than 150 businesses across industries from telco to banking:
For example, Samsung is exploring generative AI to deliver unprecedented innovation for clients. Citi is pursuing the potential use of large language models for connecting controls to internal processes. NatWest is embedding Watsonx into its chatbot to improve customer experience, and SAP is integrating IBM Watson AI into their solutions.
IBM is also working with an expanding ecosystem of partners to co-create and innovate across industries and use cases from space to sports, including work with NASA to build the first foundation models for analyzing geospatial data and Wimbledon, where Watsonx was used to produce tennis commentary.
It’s also a global opportunity, he noted, although inevitably rates of AI maturity are at different levels around the world, but the adoption questions have a lot of commonality:
It is a international phenomenon, not confined to the US. Now, we’ve got to dig under it. If I look at the maturity of clients to actually have their enterprise ready to embrace AI, to be able to interact with their clients, their employees, that does vary, I have to acknowledge it. The North American market is probably the most further ahead on this. I think Western Europe comes second, likely together with some of the more advanced and developed markets in South America.
Following that then is Asia and all of what would we call the ‘Global South’. Japan has a strong interest, but they tend to be cautious on adopting technology, not necessarily in experimenting. In experimenting, they’ll be pretty quick. And then it will go into South Asia, where always, I think, technology adoption tends to lag by a year or two behind the West.
That said, every government, every enterprise, every CEO, every CIO that I talk to wants to talk about AI. What it can do to their company, what should it be in their country and all of the questions around sovereignty of models and data and privacy, and not depending only on a few international players, [all those] come into the conversation. That’s why we talk a lot about private models and models that can be left behind with the client, because that is coming up more and more.
It’s clear that IBM is, inevitably, betting big on generative AI as a technology shift that’s here to stay. If it can replicate what it’s achieved with Red Hat OpenShift, the prospects look healthy.