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AI and Gen AI in business operations

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As AI moves from pilot projects to production-scale deployments, organizations are beginning to realize measurable returns.

With an average ROI of 1.7x, AI is no longer a future promise – it’s a present-day performance driver. Leading organizations are unlocking ROI and efficiency through AI-driven business operations.

The Capgemini Research Institute’s new report, AI in action: How Gen AI and agentic AI redefine business operations, explores how Gen AI and agentic AI systems are transforming business operations across supply chain, finance, customer service, and people operations. From cost savings to faster decision-making, AI is reshaping the way enterprises operate.

Key findings from the report include:

  • AI is delivering real business value: 40% of organizations expect positive ROI from AI within one to three years, and another 35% within three to five years. AI agents are driving improvements in efficiency, accuracy, and customer satisfaction.
  • Investment in AI is accelerating: 62% of organizations have increased gen AI spending in 2025, with 36% allocating dedicated capital. Proprietary models are preferred by 77% of executives for their performance and integration capabilities.
  • Agentic AI adoption is surging. The use of AI agents – including multi-agent systems – has more than doubled in one year, with 21% of organizations now using them in operations. Production-scale deployments are expected to grow by 48% in 2025.
  • AI is reshaping core business functions, delivering cost savings of 26–31% across supply chain and procurement, finance and accounting, and customer and people operations.

AI in action: How Gen AI and agentic AI redefine business operations is essential reading for business leaders, technology decision-makers, governance teams, and investors seeking to understand the transformative potential of Gen AI and agentic AI. It provides organizations with the essential steps for developing AI-driven business operations:

  • Build a strong foundation of AI readiness
  • Make the workforce AI-ready through change-management and cultural transformation to enable smarter human – AI collaboration
  • Develop a strong approach to process redesign to unlock agentic AI’s full potential
  • Embrace agentic AI for transformational benefits
  • Maintain a sharp focus on cost containment
  • Devise a clear strategy for scaling AI across the enterprise.

To discover how AI is reshaping enterprise operations, and learn how to scale AI for lasting impact, download the report today.



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AI Coding Tools Could Decrease Productivity, Study Suggests

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AI code editors have quickly become a mainstay of software development, employed by tech giants such as Amazon, Microsoft, and Google.

In an interesting twist, a new study suggests that AI tools might actually be slowing experienced developers down.

Experienced developers using AI coding tools took 19% longer to complete issues than those not using generative AI assistance, according to a new study from Model Evaluation & Threat Research (METR).

Even after completing the tasks, participants couldn’t accurately gauge their own productivity, the study said: The average AI-assisted developers still thought their productivity had gained by 20%.

How the study was set up

METR’s study recruited 16 developers with large, open-source repositories that they had worked on for years. The developers were randomly assigned into two groups: Those allowed to use AI coding assistance and those who weren’t.

The AI-assisted coders could choose which vibe-coding tool they used. Most chose Cursor with Claude 3.5/3.7 Sonnet. Business Insider reached out to Cursor for comment.

Developers without AI spent over 10% more time actively coding, the study said. The AI-assisted coders spent over 20% more time reviewing AI outputs, prompting AI, waiting on AI, or being idle.


A graph from METR's study is pictured.

While participants without AI use spent more time actively coding, AI-assisted participants spent more time prompting and waiting for AI, reviewing its output, and idling.

METR



A ‘really surprising’ result — but it’s important to remember how fast AI tools are progressing

METR researcher Nate Rush told BI he uses an AI code editor every day. While he didn’t make a formal prediction about the study’s results, Rush said he jotted down positive productivity figures he expected the study to reach. He remains surprised by the negative end result — and cautions against taking it out of context.

“Much of what we see is the specificity of our setting,” Rush said, explaining that developers without the participants’ 5-10 years of expertise would likely see different results. “But the fact that we found any slowdown at all was really surprising.”

Steve Newman, serial entrepreneur and cofounder of Google Docs, described the findings in a Substack post as “too bad to be true,” but after more careful analysis of the study and its methodology, he found the study credible.

“This study doesn’t expose AI coding tools as a fraud, but it does remind us that they have important limitations (for now, at least),” Newman wrote.

The METR researchers said they found evidence for multiple contributors to the productivity slowdown. Over-optimism was one likely factor: Before completing the tasks, developers predicted AI would decrease implementation time by 24%.

For skilled developers, it may still be quicker to do what you know well. The METR study found that AI-assisted participants slowed down on the issues they were more familiar with. They also reported that their level of experience made it more difficult for AI to help them.

AI also may not be reliable enough yet to produce clean and accurate code. AI-assisted developers in the study accepted less than 44% of the generated code, and spent 9% of their time cleaning AI outputs.

Ruben Bloom, one of the study’s developers, posted a reaction thread on X. Coding assistants have developed considerably since he participated in February.

“I think if the result is valid at this point in time, that’s one thing, I think if people are citing in another 3 months’ time, they’ll be making a mistake,” Bloom wrote.

METR’s Rush acknowledges that the 19% slowdown is a “point-in-time measurement” and that he’d like to study the figure over time. Rush stands by the study’s takeaway that AI productivity gains may be more individualized than expected.

“A number of developers told me this really interesting anecdote, which is, ‘Knowing this information, I feel this desire to use AI more judiciously,'” Rush said. “On an individual level, these developers know their actual productivity impact. They can make more informed decisions.”





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Why Chuck Robbins and Jeetu Patel believe Cisco’s AI reinvention is working

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Just days before Nvidia stormed past $4 trillion market cap, setting off another frenzied rally around artificial intelligence (AI)-linked stocks, a quieter, less meme-able tech giant, Cisco Systems, was building a case for relevance, led by its top brass, Chuck Robbins and Jeetu Patel, in the heart of Mumbai. Long seen as a legacy stalwart of the dotcom era, Cisco today trades at a market cap of $272 billion, a far cry from its 2000 peak of $500 billion. But for its CEO Chuck Robbins and president and chief product officer Jeetu Patel, the story has only begun to play out now.



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Martin Lewis' trick for haggling with a call centre

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Contract ending or ended? Try this if you’re renewing your broadband/TV, mobile, car/home insurance or breakdown cover.



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