Every Business Is Now a Data and Tech Company

Ninety percent.

That's the share of S&P 500 market value now held in intangible assets. Data. Algorithms. Intellectual property. Software.

In 1975, that number was 17%. Physical assets, factories, equipment and inventory dominated the balance sheet. Today, those same tangible assets represent a rounding error in what makes companies valuable.

This isn't a trend. It's a completed transition. And most enterprise leadership teams are still operating with mental models built for an economy that no longer exists.

The inversion happened while we were looking elsewhere

Ocean Tomo's Intangible Asset Market Value Study tracks one of the most significant economic transformations in modern history. In four decades, enterprise value has completely inverted. What was 83% tangible is now 90% intangible. The factories still matter. But they're no longer where the value lives.

The market capitalisation leaderboard tells the same story. In 1990, the top ten companies included Exxon Mobil, General Electric, Walmart and IBM. Enterprises defined by physical footprint, manufacturing capability and distribution networks. By 2025: NVIDIA, Apple, Alphabet, Microsoft, Amazon, Meta. Companies whose competitive advantage derives from intelligence, not capital.

NVIDIA generates approximately $3.6 to $4.4 million in revenue per employee, depending on the calculation period. JPMorgan Chase generates roughly $500,000 to $850,000. That's not a marginal difference. It's an order of magnitude. And it reflects fundamentally different approaches to value creation, where algorithms and data assets multiply human productivity in ways that capital-intensive models cannot replicate.

The investor community has already updated its frameworks. AI companies trade at 25 to 30x revenue multiples compared to approximately 6x for traditional SaaS. Anthropic reached a $183 billion valuation in September 2025, tripling in six months. The market has declared its verdict on what constitutes competitive advantage.

The question is whether your board has received the memo.

Why most digital transformations fail

Despite massive investment, the majority of digital transformation initiatives fail to achieve their objectives. McKinsey's research suggests only 16% of digital transformations fully succeed. Bain's 2024 analysis of business transformations found that 88% fail to achieve their original ambitions. The consistency of these findings points to something systemic rather than execution variability. IDC forecast global digital transformation spending would reach $2.3 trillion by 2023, with projections now exceeding $2.8 trillion for 2025. Combining these spending figures with documented failure rates, analysts estimate hundreds of billions in wasted investment annually.

The technology isn't the problem. The mindset is.

Gartner found that 46% of CIOs cite culture as their largest obstacle to digital transformation. McKinsey's research shows that transformations are 5.3 times more likely to succeed when senior leaders role-model the behaviour changes they're asking employees to make. BCG's analysis of transformation failures reveals a consistent pattern: companies focus on specific technologies rather than doing the harder work of integrating change into overall business strategy.

The fundamental issue is identity. Traditional enterprises see themselves as financial services companies, manufacturers or retailers that use technology. Tech-native companies see themselves as technology companies that happen to operate in specific domains.

When banks claim to be "tech companies at heart" by virtue of their IT spending, JPMorgan, the biggest-spending bank, still invests less than half what Google or Amazon spend on technology. The language changes faster than the mindset.

This distinction sounds academic. It isn't. It determines how you hire, how you organise, where technology sits in your structure, and whether your board can even ask the right questions about your competitive position.

The cost-centre trap

Most traditional enterprises still treat technology as a support function. The average IT department invests 55% of its budget maintaining existing operations and only 19% building new capabilities, according to Deloitte.

In insurance, the problem is more acute. Approximately 70% of insurers' IT budgets are consumed keeping legacy systems running, according to PwC. That leaves almost nothing for innovation, transformation or building capabilities that might matter in five years.

The organisational design reinforces this. More than half of CIOs globally still don't report to the CEO. Technology leadership remains subordinated, viewed as operational rather than strategic. McKinsey found that large banks are 40% less productive than digital natives, releasing new features every four to six months while fintechs ship updates every two to four weeks.

At Allianz Direct, a third of employees work in technology or data roles. Cross-functional agile squads. Engineering-focused culture. Most traditional insurers have nowhere near this concentration.

The gap isn't closing. It's widening.

The insurance paradox

Here's where this becomes pointed.

Insurance was built on data. John Graunt laid the groundwork for demography and vital statistics in 1662 by analysing Bills of Mortality. Edmond Halley published the first life table in 1693. The first life insurance company, founded in 1762, was the first to use "actuary" as a job title. Underwriting is prediction. Pricing is statistics. Claims processing is pattern recognition.

Insurance has been in the data business for over 350 years. It just doesn't see itself that way.

Leon Gauhman, Chief Product and Strategy Officer at Elsewhen, captures the challenge directly: established insurance companies aren't confronting the fact that they need to become technology companies. Rather than bringing cultural change to think like technology firms, most insurers bolt additional technologies onto existing operating models.

Industry surveys consistently reveal the capability gap. Gartner reported that while digital transformation is the number one goal for insurance CIOs, only 5% indicate they are harvesting results from digitalisation.

The contrast with insurtechs is stark. They define themselves as tech companies first, using a technology mindset to approach insurance. Traditional insurers define themselves as financial services companies that use technology.

Same industry. Different species.

The governance gap that determines outcomes

MIT research reveals the performance consequences of this identity confusion. Only 26% of boards are both digitally and AI-savvy by current criteria. The financial impact is measurable: AI-savvy boards show ROE 10.9 percentage points above industry average. Companies without AI-savvy boards average 3.8 percentage points below.

That's not a rounding error. That's the difference between market leadership and managed decline.

Only 31% of finance and insurance boards meet MIT's criteria for AI savviness. Meanwhile, 66% of directors report their boards have limited to no knowledge or experience with AI. Nearly one in three say AI doesn't appear on their agendas.

The information flow compounds the problem. Only 27% of directors are very satisfied with reporting on emerging technology adoption. Half don't feel they receive enough information on AI risks. But they also lack the expertise to ask the right questions.

You cannot govern what you do not understand. And you cannot transform an organisation when leadership hasn't internalised that the nature of business itself has changed.

What this means for what comes next

This isn't about adopting AI tools. It's about recognising that intelligence has replaced capital as the primary source of competitive advantage. The shift has already happened. The question is whether your organisation's identity, talent strategy, organisational structure and board composition reflect the world as it now exists.

Most don't. Which is why most can't see what's coming.

The threats emerging from this new competitive landscape are concrete, measurable and accelerating. Customers researching your products through AI that may never surface your brand. Transactions completed by AI agents that bypass your website entirely. Competitors operating with 15x your unit economics. Attacks that move faster than your security team can respond. A workforce using AI whether you've sanctioned it or not.

These aren't hypothetical futures. They're happening now. And they're invisible to organisations still operating with yesterday's mental model.

The first step isn't a technology investment. It's a recognition: every business is now a data and intelligence business. Including yours. Especially yours.

What you do with that recognition determines what happens next.