You’re reading AI at the Top - Our new series on Sundays, where we share how leading companies and their executives use AI in business.
IBM’s insights are different from what we have been studying so far.
Because IBM is not just any other Fortune 100 company using AI to scale its business, but also its core business revolves around transforming enterprises with AI.
In today’s AI at the Top, we will learn how the 114-year-old company with 280,000 employees operates with AI:
How does IBM help enterprises with AI?
What is watsonx?
IBM’s AI use cases
How does IBM help enterprises scale with AI?
IBM helps enterprises with four main solutions:
Software: Develops hybrid cloud platforms and provides AI solutions like watsonX, automations, etc. Basically deploying AI across core domains and automating repeatable tasks.
Consulting: Optimises workflows and consults on cloud architecture and technologies.
Infrastructure: Hybrid cloud solutions (perfect for enterprises worried about data security and governance), servers, storage systems, infrastructure-as-a-service, etc.
Financing: Leasing, instalment options, etc., for client acquisition of IBM hardware, software, and services.
IBM reported the software revenue of $27 billion in 2024. That’s 45% of the total revenue share last year.
With software being the core of IBM’s AI game, revenue, and its solutions for the clients, let us understand IBM’s AI product for enterprises: watsonx.
What is watsonX?
In 2007, IBM researchers wanted to build a system to compete and win the game of Jeopardy.
If you’re not familiar with the game, it includes questions like “The ancient city of Carthage was located on the coast of this present-day country,” and you’re supposed to guess the answer.
The questions have puns, nuances, and wordplay to make the trivia complex.
What does it mean?
Winning at Jeopardy = IBM’s Watson could process and answer complex natural language questions.
Four years later, in 2011, Watson defeated two of the highly ranked Jeopardy players, Brad Rutter and Ken Jennings.
Between 2013-2020, Watson has evolved from a developer cloud platform → advisor based on data → an NLP library → chatbots that understand user intent.
Finally in 2023, watsonx was introduced with gen AI capabilities beyond Watson, allowing enterprises to tune, train, and apply AI across industries and multiple business domains.
This includes orgs like NatWest, Vodafone, AT&T, KPMG, etc., with a super broad portfolio:
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But we’re not here to discuss IBM’s products or its partners. We are here to learn how IBM uses AI.
The point of our sharing IBM’s backstory and solutions is to show how the enterprise follows a “Client Zero” approach.
The company considers itself the zeroth user of watsonx and wouldn’t ship features/upgrades IBM itself doesn’t use as an enterprise valued at $268.6 billion in market cap.
We have curated three winning AI use cases for IBM and their impact:
AI in Supply Chain
IBM has supply chain staff in 40 countries, and access to AI only makes it easier to predict and respond to critical issues.
It started off as a ‘Cognitive’ supply chain and integrated Gen AI as the tech evolved.
Demanding sensing + Risk management tool (called Resilinc) notices disruptions in real-time to act quickly and secure a second supply source.
This is how IBM’s Senior Partner of Supply Transformation, Cushman, uses AI in Supply Chain:
“I can ask in natural language about part shortages, order impact, risks to revenues, and tradeoffs. There is a button that recommends actions to solve issues.”
As a result of AI integration, the supply chain costs reduced by $160 million.
AI in Coding
In 2024, IBM hosted a challenge among its employees, inviting them to use its code assistant.
Multiple teams participate and here is how the results have turned out:
107 teams mentioned the time spent on code explanation was reduced by an average of 56%
153 teams, reduce in code documentation, 59% average
112 teams, reduce in code generation, 38% average
Devs now spend time on more complex, creative functions over simple use cases.
AI in HR
HRs and managers use AskHR as their Gen AI tool.
So far, AskHR’s impact in numbers:
30% increase in HR efficiency because of the time saved
IBM Consulting has 20% (↑) in candidate quality and 50% (↓) in cost per hire
Candidates receive faster feedback and updates. Plus interview scheduling is done instantly
94% of employee queries are resolved without HR’s supervision at an 80% faster rate
Performance assessment and workforce planning are fast and unbiased
Messages access employees’ mental health to support them. This has reduced the employee turnover rate by 25%
“watsonx Orchestrate has streamlined recruitment drastically. The time required to fill in new positions dropped by 60%”
🌟 Want to be featured in the next issue? Reach out with your best AI use case and we’ll spotlight it.
What's next for IBM?
IBM’s productivity has been off the charts since January 2023 and the company is all set to achieve $4.5B in savings by the end of 2025.
The next focus is obviously on the agentic use cases to leverage its decision-making.
IBM is clear with its plans all the way till 2030 and beyond. This automation roadmap, mapping IBM’s AI plans from 2024-2030, will help you understand its direction.
Sources:
Multiple content pieces published by IBM on productivity, supply chain, roadmap, coding
IBM leadership’s LinkedIn: CEO, VP of Product
Case studies from Klover, Digitalk Defynd, HR Analytics
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