Business

Why AI alone can’t guarantee business success, expert cautions

Published

10 hours ago

July 11, 2025

As companies around the world race to adopt artificial intelligence (AI), strategy expert Shotunde Taiwo urges business leaders to look beyond the hype and focus on aligning technology with clear strategic goals.

Taiwo, a finance and strategy professional, cautions that while AI offers transformative potential, it is not a guaranteed path to success. Without a coherent strategy, organisations risk misdirecting resources, entrenching inefficiencies, and failing to deliver meaningful value from their AI investments.

“AI cannot substitute for strategic clarity,” she explains, stressing the importance of purposeful direction before deploying advanced digital tools. Business leaders, she says, must first define their objectives, only then can AI act as an effective enabler rather than an expensive distraction.

Taiwo stated that many organisations are investing heavily in AI labs, data infrastructure, and talent acquisition without clearly defined business outcomes. This approach, she notes, risks undermining the very efficiencies these technologies are meant to create.

For example, a retail business lacking a distinctive value proposition cannot expect a recommendation engine to deliver meaningful differentiation. Similarly, manufacturers without well-structured pricing strategies will find limited benefit in predictive analytics. “AI amplifies what’s already there,” she adds. “It rewards businesses with strong foundations and exposes those without.”

According to Taiwo, the true value of AI emerges when it is guided by intelligent, strategic intent. High-performing organisations use AI to solve well-defined problems aligned with commercial goals, often framed by business analysts or strategic leaders who understand both operational realities and broader business priorities.

She cites Amazon’s recommendation engine and UPS’s route optimisation algorithms as models of effective AI deployment. In both cases, technology served a clear purpose: boosting customer retention and streamlining logistics, respectively. When guided by strategy, AI becomes a force multiplier, enhancing forecasting, enabling automation, and improving personalisation where workflows are already well-defined.

On the other hand, even the most advanced AI systems falter in the absence of sound strategy. Common pitfalls include deploying machine learning models without a business case, focusing on tools rather than problems, collecting data without a clear use, and optimising narrow metrics at the expense of enterprise-wide goals. These missteps often result in underwhelming pilots and disillusioned stakeholders, issues strategic professionals are well-equipped to navigate and avoid.

In this sense, AI adoption can serve as a strategic diagnostic. Taiwo suggests that when business leaders struggle to define impactful AI use cases, it often reflects deeper ambiguity in their organisational direction. Key questions, such as where value is created, who the primary customer is, or which decisions would benefit most from improved speed or accuracy, are not technical, but fundamentally strategic.

AI, she says, acts as a mirror, revealing strengths and weaknesses in how a business is positioned, differentiated, and aligned across functions. Strategic leaders and business analysts are uniquely positioned to interpret these insights, inform course corrections, and guide effective technology investments.

Looking ahead, Taiwo argues that strategy in the AI era must be data-literate, agile, ethically grounded, and above all, human-centred. Leaders must understand what data they have, and how it can be harnessed, without needing to become technologists themselves.

Organisations must be nimble enough to act on AI-driven insights, whether through supply chain reconfiguration or dynamic pricing. Ethics, too, are critical, especially as AI increasingly impacts areas such as hiring, lending, and content moderation. “AI is not a replacement for strategy – it is a reflection of it,” she said.

In organisations with clarity and discipline, AI can unlock significant value. In those without, it risks adding cost and complexity. The responsibility for today’s leaders is to ensure that technology serves the business, not the other way around.

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Business

AI Coding Tools Could Decrease Productivity, Study Suggests

Published

33 minutes ago

July 11, 2025

Steven Tweedie

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.

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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Business

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

Published

2 hours ago

July 11, 2025

V. Keshavdev

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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