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We’re measuring AI all wrong—and missing what matters most

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There’s a peculiar irony in how we evaluate artificial intelligence: We’ve created systems to mimic and enhance human capabilities, yet we measure their success using metrics that capture everything except what makes them truly valuable to humans.

The tech industry’s dashboards overflow with impressive numbers on AI: processing speeds, parameter counts, benchmark scores, user growth rates. Silicon Valley’s greatest minds tweak algorithms endlessly to nudge these metrics higher. But in this maze of measurements, we’ve lost sight of a fundamental truth: The most sophisticated AI in the world is worthless if it doesn’t meaningfully improve human lives.

Consider the story of early search engines. Before Google, companies competed fiercely on the sheer number of web pages indexed. Yet Google prevailed not because it had the biggest database, but because it understood something deeper about human behavior—that relevance and trustworthiness matter more than raw quantity.

AI that builds trust

Today’s AI landscape feels remarkably similar, with companies racing to build bigger models while potentially missing the more nuanced elements of human-centered design that actually drive adoption and impact.

The path to better AI evaluation begins with trust. Emerging research demonstrates that users engage more deeply and persistently with AI systems that clearly explain their reasoning, even when those systems occasionally falter. This makes intuitive sense—trust, whether in technology or humans, grows from transparency and reliability rather than pure performance metrics.

Yet trust is merely the foundation. The most effective AI systems forge genuine emotional connections with users by demonstrating true understanding of human psychology. The research reveals a compelling pattern: When AI systems adapt to users’ psychological needs rather than simply executing tasks, they become integral parts of people’s daily lives. This isn’t about programming superficial friendliness—it’s about creating systems that genuinely comprehend and respond to the human experience.

Trust matters more than technical prowess when it comes to AI adoption. A groundbreaking AI chatbot study of nearly 1,100 consumers found that people are willing to forgive service failures and maintain brand loyalty not based on how quickly an AI resolves their problem, but on whether they trust the system trying to help them.

AI that gets you

The researchers discovered three key elements that build this trust: First, the AI needs to demonstrate a genuine ability to understand and address the issue. Second, it needs to show benevolence—a sincere desire to help. Third, it must maintain integrity through consistent, honest interactions. When AI chatbots embodied these qualities, customers were significantly more likely to forgive service problems and less likely to complain to others about their experience.

How do you make an AI system trustworthy? The study found that simple things make a big difference: anthropomorphizing the AI, programming it to express empathy through its responses (“I understand how frustrating this must be”), and being transparent about data privacy. In one telling example, a customer dealing with a delayed delivery was more likely to remain loyal when a chatbot named Russell acknowledged their frustration and clearly explained both the problem and solution, compared to an unnamed bot that just stated facts.

This insight challenges the common assumption that AI just needs to be fast and accurate. In health care, financial services, and customer support, the most successful generative AI systems aren’t necessarily the most sophisticated —they’re the ones that build genuine rapport with users. They take time to explain their reasoning, acknowledge concerns, and demonstrate consistent value for the user’s needs.

And yet traditional metrics don’t always capture these crucial dimensions of performance. We need frameworks that evaluate AI systems not just on their technical proficiency, but on their ability to create psychological safety, build genuine rapport, and most importantly, help users achieve their goals.

New AI metrics

At Cleo, where we’re focused on improving financial health through an AI assistant, we’re exploring these new measurements. This might mean measuring factors like user trust and the depth and quality of user engagement, as well as looking at entire conversational journeys. It’s important for us to understand if Cleo, our AI financial assistant, can help a user with what they are trying to achieve with any given interaction.

A more nuanced evaluation framework doesn’t mean abandoning performance metrics—they remain vital indicators of commercial and technical success. But they need to be balanced with deeper measures of human impact. That’s not always easy. One of the challenges with these metrics is their subjectivity. That means reasonable humans can disagree on what good looks like. Still, they are worth pursuing.

As AI becomes more deeply woven into the fabric of daily life, the companies that understand this shift will be the ones that succeed. The metrics that got us here won’t be sufficient for where we’re going. It’s time to start measuring what truly matters: not just how well AI performs, but how well it helps humans thrive.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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Genesys CEO: How empathetic AI can scale our humanity during economic uncertainty

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In light of the U.S. tariff announcements and rising economic uncertainty, I believe companies will instinctively turn to efficiency measures to weather potential disruption. And while efficiency is critical, it’s empathy—together with operational rigor—that will determine who thrives.

In an era increasingly shaped by AI, the most memorable customer experiences harness the power of “and”—they are fast and human, automated and deeply personal. Being seen and understood isn’t at odds with scale. It’s what elevates it. 

From transactions to trust

Over the past decade, organizations have invested in technology to make customer service faster, more consistent, and less reliant on human intervention.

While automated chatbots and self-service tools have become commonplace, many experiences still feel impersonal and often frustrating. That’s because they were built for efficiency, not empathy.

But business is shifting from a service economy, where value is measured by speed and volume, to an experience economy, where value is created through emotional resonance, trust, and personalization. 

This concept of the “experience economy” was first introduced by B. Joseph Pine II and James H. Gilmore, who argued that we are moving into an era where the primary offering is not a product or a service, but rather the experience itself. In their words, “work is theatre and every business a stage.” That framing may sound dramatic, but it’s more relevant now than ever. Consumers aren’t just buying outcomes. They’re buying how those outcomes feel.

It’s a shift that’s easy to see in our daily lives. We’ll choose a coffee shop not just for the quality of the coffee, but for how the space makes us feel. We’ll return to a brand that remembers our preferences. We’ll tell friends about the airline that made a frustrating delay easier to navigate with clarity. These experiences create differentiation in a world where many services have become commoditized. In fact, according to a survey we conducted in 2024, 30% of consumers say they have stopped using a brand after a negative experience in the past year.

The five levels of experience

Technology has historically lagged behind this evolution, but that’s changing, too. Artificial intelligence is now capable of understanding sentiment, adapting to behavior in real time and personalizing every interaction. This evolution requires more than incremental upgrades. It calls for a new approach—one where conversations across channels, moments, and touchpoints are designed to feel seamless, personalized, and emotionally intelligent.

To understand how organizations are navigating this shift, we developed a five-level maturity model that maps progress from basic transactions to fully orchestrated, emotionally intelligent experiences.

Levels 1 and 2: Rely on rigid, rules-based systems like legacy phone trees or entry-level chatbots to handle simple customer requests. These interactions are often siloed, reactive, and limited in their ability to adapt. 

Level 3: Integrates predictive and generative AI to personalize interactions in real time. Virtual assistants don’t just answer questions—they start to anticipate needs, resolve problems proactively, and adapt based on context.

Agentic AI is the bridge to the highest levels of experience orchestration, enabling systems to take initiative, make decisions, and coordinate actions across channels to pave the way for emotionally intelligent and fully orchestrated experiences.

Level 4: AI will begin to reflect emotional intelligence. It will detect tone and sentiment, respond with appropriate empathy, and even switch communication styles based on the customer’s preferences or language. This will enable systems to handle more complex, emotionally charged conversations like resolving a billing dispute or managing a delayed flight without losing the human touch.

Level 5: Universal orchestration. This is an aspirational frontier. AI will be adaptive and predictive, and capable of acting as a kind of personalized virtual concierge that understands individuals holistically across time and channels. For many industries, it is poised to become a competitive imperative.

The economic value of empathy

There’s no doubt that automation and augmentation drive real value. Businesses that implement AI-driven tools to handle routine customer interactions and provide real-time employee assistance often see meaningful improvements in efficiency, cost savings, and scalability, while also driving increases in employee engagement and customer satisfaction.

But the real prize lies beyond efficiency, in loyalty.

When businesses invest in empathetic AI that can personalize experiences, optimize journeys, and foster trust, they unlock a new level of potential economic impact. Consider a regional bank with a thousand customer service agents. By layering empathetic AI capabilities into its operations, it could not only reduce churn and improve employee retention but also create new top-line opportunities through more effective upselling, cross-selling, and long-term customer loyalty.

Empathy, in a very real way, can pay.

Empathy by design

Empathy is often thought of as a uniquely human trait. But in the context of AI, it becomes both a design challenge and a philosophical one.

Building emotionally intelligent systems requires training models to recognize more than just words. They must interpret tone, pace, hesitation, and sentiment. They must connect disparate data points to understand context, like why a customer is calling, how they’re feeling, and what they’ve experienced before, then adjust their response accordingly.

Some of the more advanced systems now match customers with agents based on emotional state and skill compatibility, offer proactive help before an issue escalates, and adjust tone in real time. They’re also capable of continuous learning, using journey data to refine interactions and ensure the experience gets better over time.

This is a new kind of intelligence. This is empathy by design. 

The human future of AI

As we move further into the experience economy—during times of macroeconomic tailwinds or headwinds—one thing is clear: Being human is a business advantage. In fact, a Forrester analysis shows that companies that improve CX can drive significant revenue growth. 

The most valuable experiences in life, and in business, are those that make us feel seen, understood, and valued. They help turn customers into loyalists, and brands into beacons. Empathy isn’t a feature. It’s the future. And AI, when built with that truth at its core, can help us deliver something truly powerful: technology that scales service and scales humanity.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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The U.S. trade deficit: It’s time to dump do-it-yourself economics and go back to basics

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Since President Trump’s inauguration on Jan. 20, it seems that many people—particularly the chattering classes—have suddenly become experts in international trade. Mr. Trump’s tariffs have spawned a litany of what economist David Henderson termed “do-it-yourself economics.” These are economic ideas that reflect the intuitive notions of laypeople and owe nothing to the ideas generated by trained economists and the economics profession. Not surprisingly, Henderson concluded that the gap between the notions of do-it-yourself economics and orthodox economics is widest in the sphere of international trade.

This gap is evident in the current brouhaha over trade and tariffs, particularly in the two opposing camps: those responsible for formulating the administration’s trade agenda (Mr. Trump and his cabinet) and those critiquing it (primarily commentators and journalists). The result of this dynamic is not only that the Trump administration has enacted wrongheaded trade policies, but also that the opposition to these policies is largely ineffective or irrelevant. Both camps are engaged in do-it-yourself economics.

The misconceptions emanating from both camps stem from one common oversight: Neither Mr. Trump nor his detractors have familiarized themselves with the savings-investment identity, a basic yet crucial mechanism that governs the magnitude of a country’s trade balance. Indeed, by definition, a country’s trade balance is governed entirely by the gap between its domestic saving and domestic investment. If a country’s domestic saving is greater than its domestic investment, like China’s, it will register a trade surplus. Likewise, if a country has a savings deficiency, like the United States, it will register a trade deficit. The United States’ negative trade balance, which the country has registered every year starting in 1975, is “made in the USA,” a result of its savings deficiency. To view the trade balance correctly, the focus should be on the domestic economy.

As it turns out, one of us, Hanke, analyzed the United States’ large and persistent trade deficits and found that they are primarily driven by its large and persistent fiscal deficits at the federal, state, and local government levels. In other words, in the aggregate, there is a savings deficiency in the United States, and this savings deficiency comes from the public sector—the U.S. private sector actually generates a savings surplus. This aggregate gap between savings and investment is filled by foreign imports of goods and services, resulting in an easy-to-finance capital inflow surplus and a trade deficit.

Armed with the basic truth of the savings-investment identity, we now turn to Mr. Trump’s camp. Mr. Trump and his advisors believe that the United States’ trade deficit is the result of foreigners ripping off and taking advantage of the United States. Indeed, Uncle Sam is characterized as being a victim of unfair trade practices. This characterization is clearly wrong on two counts. First, the trade deficit is not caused by foreigners; rather, it is homegrown, the result of choices made by Americans (in the aggregate) to invest beyond what they save.

Second, the trade deficit is not necessarily harmful. It instead appears to be a privilege extended to Americans by foreigners willing to invest in U.S. assets. This is a symbiotic relationship: Americans get cheap access to capital, while foreign governments and institutions get a safe place to park their money and earn a return.

When it comes to trade policy, the Trump administration’s detractors are just as lost as the White House. A recent high-profile article in the New York Times—Totally Silly.’ Trump’s Focus on Trade Deficit Bewilders Economists,” contains an indicative summary of what journalists and commentators have to say about trade deficits. There’s just one little problem with the article and its respondents: No one ever explicitly mentions the true source of the trade deficit, which is elucidated by one of the most basic identities in economics. The identity tells us that if savings are less than investment, the gap must be filled by a trade deficit.

Both Mr. Trump’s cabinet and those criticizing his policies have a fundamental misunderstanding of what drives the U.S. trade deficit. As a result, the trade debate has turned into a futile filibuster, highlighting the dangers of do-it-yourself economics. It’s time to go back to the basics.

Steve H. Hanke is a professor of applied economics at the Johns Hopkins University and the author, with Leland Yeager, of Capital, Interest, and Waiting. Caleb Hofmann is a research scholar at the Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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