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Companies are pouring billions into AI. It has yet to pay off.

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2 weeks ago

August 17, 2025

Nearly four decades ago, when the personal computer boom was in full swing, a phenomenon known as the “productivity paradox” emerged.

It was a reference to how, despite companies’ huge investments in new technology, there was scant evidence of a corresponding gain in workers’ efficiency.

Today, the same paradox is appearing, but with generative artificial intelligence (Gen AI). According to recent research by McKinsey & Co, nearly eight in 10 companies have reported using Gen AI, but just as many have reported “no significant bottom-line impact”.

AI technology has been racing ahead with chatbots such as ChatGPT, fuelled by a high-stakes arms race among tech giants and super-rich startups, and prompting an expectation that everything from back-office accounting to customer service will be revolutionised. But the payoff for businesses outside the tech sector is lagging behind, plagued by issues including an irritating tendency by chatbots to make stuff up.

That means that businesses will have to continue to invest billions to avoid falling behind – but it could be years before the technology delivers an economy-wide payoff, as companies gradually figure out what works best.

Call it the “gen AI paradox,” as McKinsey did in its research report. Investments in Gen AI by businesses are expected to increase 94 per cent in 2025 to US$61.9 billion (S$79.5 billion), according to IDC, a technology research firm.

But the percentage of companies abandoning most of their AI pilot projects soared to 42 per cent by the end of 2024, up from 17 per cent the previous year, according to a survey of more than 1,000 technology and business managers by S&P Global, a data and analytics firm.

Projects failed not only because of technical hurdles, but often also because of “human factors” like employee and customer resistance or lack of skills, said S&P Global senior analyst Alexander Johnston.

Gartner, a research and advisory firm that charts technological “hype cycles”, predicts that AI is sliding towards a stage it calls “the trough of disillusionment”.

The low point is expected next year, before the technology eventually becomes a proven productivity tool, said Gartner chief forecaster John-David Lovelock.

That was the pattern with past technologies such as personal computers and the internet – early exuberance, the hard slog of mastering a technology, followed by a transformation of industries and work.

The winners so far have been the suppliers of AI technology and advice. They include Microsoft, Amazon and Google, which offer AI software, while Nvidia is the runaway leader in AI chips.

Executives at those companies have bragged how AI is reshaping their own workforces, eliminating the need for some entry-level coding work and making other workers more efficient.

AI will eventually replace entire swaths of human employees, many predict, a perspective that is being widely embraced and echoed in the corporate mainstream. At the Aspen Ideas Festival in June, Mr Jim Farley, chief executive of Ford Motor, said: “Artificial intelligence is going to replace literally half of all white-collar workers in the US.”

Whether that type of revolutionary change occurs, and how soon, depends on the real-world testing ground of many businesses.

“The raw technological horsepower is terrific, but it’s not going to determine how quickly AI transforms the economy,” said Mr Andrew McAfee, a principal research scientist and co-director of the Massachusetts Institute of Technology’s (MIT) Initiative on the Digital Economy.

Still, some businesses are finding ways to incorporate AI – although in most cases the technology is still a long way from replacing workers.

One company where AI’s promise and flaws are playing out is USAA, which provides insurance and banking services to members of the military and their families.

After several pilot projects, some of which it closed down, the company introduced an AI assistant to help its 16,000 customer service workers provide correct answers to specific questions.

USAA is tracking its AI investments, but does not yet have a calculation of the financial payoff, if any, for the call centre software.

But the response from its workers, the company said, has been overwhelmingly positive. While it has software apps for answering customer questions online, its call centres field an average of 200,000 calls a day.

“Those are moments that matter,” said Mr Ramnik Bajaj, the company’s chief data analytics and AI officer. “They want a human voice at the other end of the phone.”

That’s similar to an AI app developed more than a year ago for fieldworkers at Johnson Controls, a large supplier of building equipment, software and services.

The company fed its operating and service manuals for its machines into an AI program that has been trained to generate a problem summary, suggest repairs and deliver it all to the technician’s tablet computer.

In testing, the app has trimmed 10 to 15 minutes off a repair call of an hour or more – a useful efficiency gain, but hardly a workplace transformation on its own.

Fewer than 2,000 of the company’s 25,000 field service workers have access to the AI helper, although the company is planning an expansion.

“It’s still pretty early days, but the idea is that over time, everyone will use it,” said Mr Vijay Sankaran, the chief digital and information officer at Johnson Controls.

The long-term vision is that companies will use AI to improve multiple systems, including sales, procurement, manufacturing, customer service and finance, he said.

“That’s the game changer,” said Mr Sankaran, who predicts that shift will take at least five years.

Two years ago, JPMorgan Chase, the nation’s largest bank, blocked access to ChatGPT from its computers because of potential security risks. Only a few hundred data scientists and engineers were allowed to experiment with AI.

Today, about 200,000 of the bank’s employees have access to a general-purpose AI assistant – essentially a business chatbot – from their work computers for tasks such as retrieving data, answering business questions and writing reports.

The assistant, tailored for JPMorgan’s use, taps into ChatGPT and other AI tools, while ensuring data security for confidential bank and customer information. Roughly half of the workers use it regularly and report spending up to four hours less a week on basic office tasks, the company said.

The bank’s wealth advisers are also employing a more specialised AI assistant, which uses bank, market and customer data to provide wealthy clients with investment research and advice. The bank says it retrieves information and helps advisers make investment recommendations nearly twice as fast as they could before, increasing sales.

Ms Lori Beer, the global chief information officer at JPMorgan, oversees a worldwide technology staff of 60,000. Has she shut down AI projects? Probably hundreds in total, she said.

But many of the shelved prototypes, she said, developed concepts and code that were folded into other, continuing projects.

“We’re absolutely shutting things down,” Ms Beer said. “We’re not afraid to shut things down. We don’t think it’s a bad thing. I think it’s a smart thing.”

Mr McAfee, the MIT research scientist, agreed.

“It’s not surprising that early AI efforts are falling short,” said Mr McAfee, who is a founder of Workhelix, an AI consulting firm. “Innovation is a process of failing fairly regularly.” NYTIMES

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August 31, 2025

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Maritime Networks Show Boards How To Navigate AI Governance

Published

2 hours ago

August 31, 2025

Solange Charas

From Sails to Servers

Solange Charas, PhD, HCMoneyball

When boards grapple with AI governance today, they often feel they’re navigating uncharted waters. But we’ve sailed these seas before. Five centuries ago, maritime networks created the world’s first global information superhighway, transforming how value was created, managed, and measured. The governance lessons from that era offer a strategic blueprint for today’s C-suite leaders managing AI transformation. As Forbes has noted, boards must navigate AI governance in an uncertain regulatory environment, making historical precedents increasingly valuable.

Between 1400 and 1700, maritime innovations didn’t just change transportation—they fundamentally reshaped business models, workforce development, and financial systems. The parallels to today’s AI revolution are striking, and the governance implications are clear: organizations that treat AI as merely a technology deployment will miss the strategic transformation it demands.

The Original Platform Economy: Governance Lessons from Maritime Networks

Modern boards often view AI through the lens of operational efficiency. History suggests this misses the point entirely. The Dutch East India Company (VOC), founded in 1602, understood that maritime technology wasn’t just about better ships—it was about creating entirely new organizational structures.

The VOC pioneered what we’d now recognize as platform governance: standardized global processes, the world’s first modern stock exchange for capital formation, complex multi-continental logistics networks, and hybrid workforce models that mixed employees with contractors. Most importantly, they created compensation structures that aligned individual performance with enterprise returns—paying workers modest wages plus profit shares.

This wasn’t just innovative management; it was strategic governance that recognized foundational technology requires fundamental changes to how organizations create and capture value. Today’s boards face an identical challenge with AI.

Human Capital as Strategic Asset: Then and Now

The maritime revolution created entirely new professional categories that hadn’t existed before: navigators who mastered complex mathematical calculations, cartographers who combined technical precision with creative insight, and insurance underwriters who developed sophisticated risk assessment capabilities.

Traditional roles didn’t disappear—they evolved. Local traders expanded their capabilities to operate globally. Ship captains transitioned from operational roles to complex management positions overseeing intercontinental operations. Craftsmen upskilled to work with new materials and production methods.

The key insight for today’s CHROs and CFOs: successful maritime powers invested systematically in workforce transformation. Spain’s Casa de Contratación created standardized navigator certification programs—perhaps history’s first technical bootcamp. Maritime academies proliferated across Europe, teaching navigation, cartography, and global commerce.

This systematic approach to skills development wasn’t a cost center—it was a strategic investment that enabled competitive advantage. The same principle applies to AI transformation today. As Forbes has highlighted, human capital is the ultimate differentiator in technological transformations.

Financial Governance: Measuring Maritime ROI vs. AI ROI

The governance challenge boards face with AI mirrors what maritime-era leaders confronted: how do you measure returns on transformational technology?

Historical data reveals striking patterns:

Trade volumes increased tenfold between 1400-1700
Spice prices in European markets dropped 70% due to transportation efficiency
Port cities like Amsterdam experienced 400% population growth
Specialist navigators earned wage premiums of 3-4x typical artisan compensation

Today’s AI metrics show remarkably similar patterns:

McKinsey reports AI can drive 23% average productivity improvements
AI specialists command 35-50% wage premiums above traditional technical roles
Organizations implementing AI systematically see measurable improvements in operational efficiency and revenue growth

The critical governance lesson: early adopters rarely dominate technological revolutions. Systematic adapters do. AI implementation success comes from systematic approaches, not speed. The Portuguese developed superior maritime technology first, but the Dutch built superior organizational systems around that technology and ultimately dominated global trade. AI implementation success comes from systematic approaches not speed!

Three Governance Imperatives for AI Leadership

Maritime history reveals three essential governance principles that apply directly to AI transformation:

1. Ecosystem Investment Over Technology Investment

Maritime success required more than better ships. It demanded navigation schools, financing mechanisms, legal frameworks, and insurance markets. Similarly, successful AI implementation requires governance ecosystems: training programs, ethical frameworks, data infrastructure, and risk management protocols.

Boards must ask: Are we building AI capability or AI ecosystems? The former leads to pilot projects that don’t scale. The latter creates sustainable competitive advantage.

2. Balanced Risk Management

Thriving maritime nations balanced protection of existing industries with incentives for innovation. England’s Navigation Acts protected domestic shipping while encouraging new ventures. Dutch financial innovations managed risk while enabling new business models.

Today’s boards need similar balanced approaches to AI policy. This means establishing governance frameworks that both protect against algorithmic bias and legal exposure while enabling workforce augmentation and operational innovation.

3. Systematic Human Capital Development

The most successful maritime powers created formal institutions for skills development. They recognized that technological advantage comes from human capability, not just technical capability.

CHROs and boards must treat AI literacy as a strategic imperative, not a training afterthought. This means creating systematic development programs, tracking human capital ROI alongside AI ROI, and ensuring that workforce transformation supports rather than undermines organizational resilience.

Measuring What Matters: Human Capital ROI in the AI Era

The SEC’s enhanced human capital disclosure requirements under Reg S-K Item 101(c) reflect growing recognition that workforce strategy is material to enterprise value. Maritime-era governance offers a template: track both technological adoption and human adaptation with equal rigor.

Key metrics should include:

AI-human collaboration effectiveness, not just automation rates
Internal mobility and reskilling success rates
Innovation pipeline strength as AI augments human creativity
Employee engagement and retention during technological transition

These aren’t “soft” HR metrics—they’re predictive indicators of sustainable competitive advantage.

The Governance Imperative: Leadership in Transformation

History’s lesson is unambiguous: technological revolutions reward systematic adapters, not early adopters. The Portuguese pioneered maritime technology but the Dutch mastered maritime governance.

For today’s boards, this means treating AI not as an IT project but as a governance challenge that spans strategy, finance, and human capital. Success requires CHROs and CFOs working in concert to ensure AI enhances rather than erodes organizational capability.

The organizations that emerge stronger from AI transformation will be those that remember what maritime history teaches: technology alone never changes the world. People, institutions, and governance do.

Author Note: This column builds on collaborative research with Stela Lupushor examining historical patterns in technological transformation and their implications for modern workforce strategy.

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