IBM appears to be shifting its priorities when it comes to AI, focusing on the practical applications in the business world rather than on the technology per se. This approach distinguishes IBM from other players in the field.
In discussions involving AI leadership, names such as Nvidia, OpenAI, Google, and Microsoft frequently come up, largely dominating the narrative in media and Wall Street circles.
However, the story significantly changes when you turn to the enterprise sector. Particularly when inquiring about vendors propelling high-impact AI projects, you’ll hear a very different narrative. Here, IBM garners nearly as much recognition as Nvidia or OpenAI. More so, IBM decisively leads the pack when it comes to crucial AI projects.
This state of affairs begs the question: are we overlooking something important here? The answer is a resounding “yes”.
Observations of the strategic impact that vendors can exert on enterprise technology consumers have been a considerable highlight of my career. A ranking of 100 is indicative of customers that follow the guidance of their supplier consistently, while a score of zero denotes complete insignificance. The majority of tech companies typically fall into the tens or twenties on this scale, however, IBM has consistently scored a minimum of 35. In fact, contrary to the gradual reduction in significant strategic influences from other suppliers, IBM has been steadily increasing its footprint. Specifically, in significant accounts, IBM attained an impressive tally of over 50 in 2023. When it comes to influencing AI discussions, IBM’s authority soars into the 70s. For both IBM and AI at large, there are critical reasons for this.
Firstly, IBM’s simple yet impressive strategy sets it apart: IBM empowers their customers with knowledge and encourages their wisdom. This is unusual territory, as most companies adopt a contrary approach. Over decades, I have heard sales management teams discouraging consultative sales and customer educative sessions. The primary goal has been just to hit their sales targets. Marking a stark contrast, IBM, for the past six decades, encouraged their people to be thinkers, even distributing notebooks bearing that single mantra: Think. Participating in strategic discussions with IBM teams and CIOs and CTOs, I have inevitably been struck by the compelling dialogue they generate. It’s more than technology they are dissecting; it’s the consequential business transformation that technology can propel.
The underlying strategy is twofold: IBM drives its AI propositions by focusing on the business case, rather than thrusting AI at its clients. Why advocate brainstorming if the targeted end result is an unthinking check writer? This raises a crucial point, leading to a consequential question: Why is the check-writing exercise insufficient? The response to this is clear. The perception of AI adding value may justify pilot schemes but not significant venture investments. Most people perceive AI as an entertainment tool, nothing more. At present, the only hypothesis proven is that a freebie is hardly ever refused. However, an analytical CIO or an insightful planner can easily debunk this mindset, confronting the tangible question. It is not a matter of the appeal or the subtleties of AI, but how do I use AI to my distinct financial advantage?
To kick off this thought process, let’s consider where AI will have minimum impact. Recently, I stumbled across a piece on the use of AI for employee review generation. How monetarily productive do you think that is? Practices such as PR writing, document assisting, or emailing may marginally contribute to productivity but have negligible effect on financial statements. IBM is challenging its clients to consider how they can efficiently apply AI to justify a substantial investment. This is yielding prompt and precise responses, namely the necessity of distinct benefits to correspond to substantial investments.
When companies seek significant shifts in their profit margins, they look towards elements such as their sales targeting approaches, efficiencies in manufacturing and transportation, overall functioning of their operations, and also their business intelligence. Large corporations understand that it needs impactful changes to experience substantial financial shifts. Now, do they accomplish this through better press releases or well-crafted manuals? Or could it be that the hype and coverage around AI made big corporations realise the potential areas of improvements? Certainly not. For years, business enterprises have been aware of these areas of potential improvement. They have consistently been addressing these issues and seizing opportunities through analytics. And when AI surfaced, they identified its potential, and that’s where companies like IBM pitched in and started concentrating its AI initiatives.
IBM identified AI, especially its Watson technology, to be an instrumental tool for business intelligence and analytics. The realization didn’t occur suddenly during a sales pitch, but it was a conscious understanding developed over time. They have driven their AI dialogues with enterprises, which eventually led to finding real-world applications of AI, specifically “generative” AI. Advanced language models trained on a company’s data can inform managers and planners on areas of potential improvement. However, this isn’t the kind of AI that garners public interest. There are no bait clicks or PR issues to address. But it brings immense value, the sort of value that aids in solid financial growth. It’s this kind of real-world value that has driven the success of AI and IBM’s success within the AI space.
Now, you might argue, IBM isn’t the only company capable of such strategic thinking. That other companies in AI might begin to see the real value of AI and build their own business intelligence systems. So, does that mean IBM, with its traditional approach, will lag behind? Let’s think about it.
IBM has been a competitor since the 1950s when they began selling business computers. However, all those contemporary rivals no longer exist. The System/360, announced by IBM in the 1960s, still forms the foundation of the Z-series mainframes that IBM continues to sell today. The reason for this isn’t that it was the greatest architecture ever, but because it’s the architecture on which enterprises built critical applications. And it’s the enduring value of those applications that keeps IBM thriving in the mainframe market sixty years on. Why are these applications still relevant? It’s simple, they weren’t merely popular, they were practical. IBM’s recent collaboration with Wipro to bring Watson technology to key verticals is a perfect example.
I am not here to lavish undue praise on IBM, but to commend the strategy that has forged their status as a timeless colossus. Public relations may be compelling, and allegorical tales may be captivating. We can spin infinite fables around subjects such as artificial intelligence, and these narratives get more intriguing with every recounting. However, at the end of the day, the driving force behind technological endeavors is the same element that fuels technology markets. The key parameter impacting businesses today has remained unchanged even after a century, which is the financial outcome. The most crucial statement an AI service provider can make to a prospective client is “Allow me to demonstrate the commercial rationale.” And you’ve probably guessed who is leading this dialogue. Look at the recent partnership they detailed with Wipro, a partnership that offers “tailored industry applications” in five fundamental sectors: banking, retail, healthcare, energy, and manufacturing.
Does all this commentary seem absurd or dull to you? Does it sound like tedious business parlance? Does it contradict everything else that you have heard or read about AI? Does it question everything that had appeared novel and thrilling about AI? If the response to all these questions is affirmative, then I have one piece of advice for you, and you may have anticipated this too.
Take a moment to ponder.
