The Agentic Era: Parsing Promise from Peril in Google's AI Business Model

Google's I/O 2025 presentation painted a compelling picture of the "agentic era"—a future where AI systems don't just respond to queries but proactively complete complex tasks across workflows. The technical demonstrations were genuinely impressive: real-time language translation, 3D conferencing systems, and AI agents capable of reasoning through multi-step problems. Yet beneath the glossy keynote lies a more complex reality that business leaders need to understand before making substantial commitments to this vision.

The promise is undeniable, but so are the practical challenges that could determine whether we're witnessing the dawn of a new computing paradigm or an expensive technological detour.

The Technical Foundation Is Solid

Google's infrastructure investments are paying dividends in measurable ways. Their 7th generation Tensor Processing Unit delivers 10x the performance of previous generations, enabling Gemini 2.5 Pro to achieve an Elo benchmark score of 1448, sweeping the LLMArena leaderboard in all categories. These aren't marketing metrics—they represent genuine advances in computational capability.

The practical applications demonstrate real utility. Real-time bi-directional language translation in Google Meet, voiced using AI-generated speech, addresses a genuine business need for global organizations. Project Mariner's ability to teach AI systems to perform specific tasks and then apply that learning to similar challenges suggests we're moving beyond simple pattern matching toward more sophisticated problem-solving capabilities.

However, the gap between technical capability and business viability remains substantial. While 78% of respondents say their organizations use AI in at least one business function, most have yet to see organization-wide, bottom-line impact from generative AI use. This disconnect between adoption and value realization should give leaders pause.

The Economics Don't Add Up Yet

The financial reality of AI deployment presents significant challenges that Google's keynote didn't address. Average monthly AI spend among companies jumped from $62,964 in 2024 to $85,521 in 2025, representing a 36% increase, yet only about half of those organizations (51%) said they could evaluate the return on investment for their AI commitments.

Google's own subscription model illustrates the cost pressures. At up to $250/month for premium features plus $299/year for developer program membership, the expenses add up quickly for organizations deploying AI at scale. For many businesses, especially smaller ones, these costs may not align with the incremental value provided by AI-enhanced workflows.

IDC projects worldwide spending on AI technology to reach $337 billion in 2025 and more than double to $749 billion by 2028. While this growth suggests confidence in AI's potential, it also represents a massive capital allocation that needs to generate corresponding returns. The sustainability of this investment trajectory depends on businesses realizing concrete value, not just theoretical promise.

The Agent Reality Check

The concept of agentic AI—systems that can autonomously complete complex, multi-step tasks—represents the most ambitious aspect of Google's vision. Early implementations show genuine promise: early enterprise deployments of AI agents have yielded up to 50% efficiency improvements in functions like customer service, sales and HR operations.

Yet IBM's research reveals significant implementation challenges. As one expert noted, "Most organizations aren't agent-ready," emphasizing that success depends less on model capabilities and more on enterprise readiness. The infrastructure, processes, and governance frameworks needed to deploy autonomous agents safely don't exist in most organizations.

The risk profile of agentic systems introduces new concerns. When agents can take actions autonomously, "the scale of the risk is higher—there's only so much that a human can do in so much time, whereas the technology can do things in a lot less time and in a way that we might not notice". For regulated industries like banking and healthcare, these risks can be prohibitive.

The Personalization Paradox

Google's "Personal Context" feature exemplifies both AI's potential and its problematic implications. The ability to analyze your emails, documents, and search history to generate personalized responses could genuinely improve productivity. Yet it also represents a fundamental shift in how we conceptualize human interaction and digital privacy.

The business model implications are concerning. Google built its empire on data monetization, and Personal Context extends this approach into even more intimate aspects of our digital lives. While the feature may provide utility, it also deepens our dependence on Google's ecosystem in ways that could limit future flexibility.

The competitive dynamics are equally complex. In 2025, AI agents will start to reshape demand for software platforms, as companies use them to fill the gaps of existing systems. This could reduce demand for traditional software upgrades, fundamentally altering the economics of enterprise software.

The Implementation Reality

Real-world AI agent deployments reveal both the potential and the constraints of current technology. JPMorgan Chase's Contract Intelligence (COiN) platform automates legal document review, saving approximately 360,000 hours annually. Similarly, DHL's AI-powered sorting robots have increased parcel sorting capacity by over 40% with 99% accuracy.

These successes share common characteristics: they're applied to specific, well-defined tasks within controlled environments. They complement rather than replace human expertise, and they operate within established workflows with clear success metrics.

Gartner predicts that AI agents will be found in a third of software applications within three years, up from 1% in 2024, while enabling 15% of day-to-day work decisions to be made autonomously. However, this growth will likely be uneven, concentrated in areas where the value proposition is clear and the risk profile is manageable.

A Measured Path Forward

The evidence suggests a more nuanced approach to AI adoption than Google's keynote implied. Organizations are pursuing tactical benefits and off-the-shelf solutions, with most pursuing 20 or fewer experiments or proofs of concept and over two-thirds saying that 30% or fewer of their experiments will be fully scaled.

This cautious approach makes sense given the current state of the technology. The cumulative result of incremental value at scale—20% to 30% gains in productivity, speed to market and revenue—may be more realistic than transformational breakthroughs.

For marketing and business leaders, the optimal strategy likely involves selective adoption focused on specific, measurable use cases rather than wholesale transformation. The organizations seeing success are those that view AI as an enhancement to existing capabilities rather than a replacement for human judgment.

The Verdict: Promise with Patience Required

Google's technical achievements are genuine, and the agentic era may indeed represent the future of computing. However, the gap between technical possibility and business reality remains substantial. The costs are real, the risks are significant, and the value proposition varies dramatically across different use cases and organizational contexts.

The smart approach isn't to dismiss Google's vision entirely or to embrace it uncritically. Instead, business leaders should view agentic AI as an emerging capability that requires careful evaluation, strategic implementation, and realistic expectations about timelines and returns.

The agentic era is coming, but it's arriving more gradually and unevenly than the keynote suggested. Success will belong to organizations that balance enthusiasm with pragmatism, investing thoughtfully rather than broadly, and maintaining focus on concrete business outcomes rather than technological novelty.

Ready to develop a strategic approach to AI that balances innovation with practical results? Contact Winsome Marketing's growth experts to create AI strategies that deliver real value without the hype.