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Greek Consumers Embrace Strategic Planning In Beauty And Wellness Spending

Shifting Priorities In Personal Care

Greek consumers are now approaching beauty and wellness expenditures with a renewed sense of discipline and foresight. A recent survey commissioned by Revolut and executed by market research leader Dynata reveals a pronounced shift towards viewing personal care budgets as a strategic financial category rather than an arena for impulsive spending.

Planned Spending And Financial Prudence

The findings show that 25% of respondents actively allocate budgets for personal care. Even under financial pressure, maintaining these routines remains a priority. Around 10% reported cutting back in other areas to preserve their spending on beauty and wellness, while 3% exceeded their planned budgets to maintain consistency.

Promotional Incentives And Value-Driven Decisions

Promotions continue to influence spending decisions. Around 20% of respondents said discounts encourage additional purchases. When asked what would drive further spending, 33% pointed to personalized offers, 31% to loyalty programs, and 25% to interest-free instalments. A smaller group, 9%, expressed interest in dedicated savings tools for managing personal care expenses.

Diminished Celebrity Influence And Trusted Recommendations

Survey results also indicate a shift in how consumers make decisions. Recommendations from family and friends were cited by 31% of respondents, while 17% relied on professional advice, including dermatologists. Influence from social media creators was reported by 7%, and from celebrities by 2%, suggesting a move toward more trusted and direct sources.

The Role Of Financial Technology

Ignacio Zunzunegui, Head of Growth for Southern Europe and Latin America at Revolut, commented on the evolving consumer behavior: “The findings show that Greek consumers are approaching spending on beauty and wellness with greater planning and consideration. We are seeing increasing demand for financial tools that help manage lifestyle-related spending, whether through saving, accessing benefits via purchases, or closely tracking expenses.” Zunzunegui further emphasized the role of financial technology in enabling consumers to adhere to their preferred habits while maintaining fiscal control.

This shift in personal care spending reflects a broader move toward more disciplined financial management. As consumers place greater emphasis on planning and value, rather than impulse purchases, companies in the beauty and wellness sector may need to adjust by offering more targeted promotions, flexible payment options, and personalized offers aligned with these preferences.

AI Spending Is Complicating The Fed’s Fight Against Inflation

Silicon Valley leaders have long argued that artificial intelligence will make technology and services dramatically cheaper. OpenAI CEO Sam Altman has described a future where intelligence becomes extremely inexpensive, while Tesla and SpaceX CEO Elon Musk has predicted that AI and robotics will create greater abundance and drive down costs.

So far, those benefits have yet to materialise at scale. AI adoption remains relatively slow, while the enormous investment needed for data centres and AI infrastructure is putting pressure on electricity prices, supply chains and other costs. For the Federal Reserve, this creates a difficult balancing act: AI could eventually boost productivity and reduce inflation, but its current buildout is contributing to higher prices.

OpenAI chief economist Ronnie Chatterji said AI needs to be adopted by organisations and generate measurable value before its broader economic impact becomes visible in productivity statistics.

AI Adoption Remains Uneven

Capital spending on AI infrastructure in the U.S. is expected to reach $581 billion this year, according to Goldman Sachs Research, with global investment potentially reaching $1 trillion.

Despite the scale of spending, adoption remains far from universal. A May survey by the U.S. Census Bureau found that 17% to 20% of U.S. businesses reported using AI, with adoption significantly higher among large companies.

Companies that have implemented AI at scale also highlight the challenges. Julie Averill, former CIO of Lululemon, said successful deployment requires changes in employee behaviour and trust in the technology. OpenAI has observed a similar divide: its most advanced business users deploy AI at around eight times the rate of average companies.

Why Productivity Gains May Take Time

Economists point to the limits of automation. AI can perform individual tasks effectively, but many jobs combine tasks that are difficult to automate.

Stanford professor Charles Jones refers to these as “weak links”. Radiology, for example, involves interpreting scans but also communicating with patients and working with colleagues. AI can automate part of the job without eliminating the profession itself.

As a result, the full economic impact of AI may not become clear until businesses adopt the technology more broadly and reorganise their operations around it.

AI Adds To The Fed’s Policy Challenge

AI’s economic impact has become part of the Federal Reserve’s policy debate. Fed Chairman Kevin Warsh has argued that AI could eventually become a significant disinflationary force by increasing productivity and strengthening U.S. competitiveness.

Other officials are more cautious. In July, the Fed kept interest rates at 3.5% to 3.75%, while some officials expressed concern that AI infrastructure spending could add to inflationary pressures.

Minneapolis Fed President Neel Kashkari pointed to massive data-centre investment as a new source of demand. Household electricity prices rose 10% in the two years through July, compared with a 6.2% increase in overall consumer prices. Meanwhile, shortages of chips and other AI components are pushing up costs. JPMorgan Chase estimates that DRAM prices could rise 400% by the end of 2026 compared with 2024.

Warsh has consequently adopted a more cautious tone, saying that while AI investment is laying the groundwork for future growth, the timing and scale of its economic effects remain difficult to predict.

For the Fed, the challenge is clear: AI could eventually deliver major productivity gains, but the cost of building that future is already showing up in the economy.

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