Welcome to the September 2025 edition of "This Month in AI." Here you'll find an overview of the latest articles and reports on advancements in artificial intelligence, trends in enterprise adoption, and innovative strategies business leaders like you are using to harness AI's transformative power.
Here’s what’s making news this month:
It’s the billion-dollar question every boardroom is asking: if nearly 95% of enterprise AI projects fail, why are a few companies racing ahead with measurable ROI and double-digit growth? According to MIT’s The GenAI Divide: State of AI in Business 2025, the secret lies in leveraging systems that learn, remember, and adapt, alongside a strategic focus on external partnerships rather than internal development.
Research Highlights:
The takeaway:
Crossing the GenAI Divide isn’t about building everything in-house. The organizations that win are partnering strategically, embedding adaptive systems, and letting users lead the way through minimal human intervention.
The comprehensive Google Cloud’s latest ROI study surveyed 3,466 senior leaders of global enterprises across 24 countries with generative AI deployment within their organizations. The study finds that over half of enterprises already use AI agents, with early adopters reporting 6–10% revenue growth.
The conversation has moved from 'if' to 'how fast,' and the new differentiator is agentic AI. Early adopters of agents are not just automating tasks; they are also redesigning core business processes. By championing AI as a core engine for competitive growth and thus securing dedicated budgets, they provide a clear roadmap for any organization looking to scale, solve complex challenges, and achieve more consistent ROI.
- Oliver Parker, VP, Global Generative AI Go-To-Market, Google Cloud.
By the end of 2025, most enterprise apps will embed AI assistants, setting the stage for agent-driven ecosystems, according to Gartner. Executives have just three to six months to set their agentic AI strategies or risk losing ground. That growth is expected to accelerate quickly: fewer than 5% of enterprise applications today have task-specific AI agents, but adoption is projected to surge to 40% by 2026. Longer-term, agentic AI could reshape the market, driving more than $450 billion in software revenue by 2035—about 30% of the total, up from just 2% in 2025.
Five stages of evolution:
Enterprise apps will shift from static tools to dynamic agentic ecosystems, redefining productivity, pricing models, and user experiences.
A year into the agentic AI revolution, one truth stands out: success doesn’t come easy. Some companies are seeing productivity gains, but many are struggling to realize value or even retrenching when deployments fail. McKinsey’s analysis of over 50 agentic AI builds they led, along with dozens of others in the marketplace, highlights key lessons on how to get agentic AI right.
It's about the workflow, not just the agent
Achieving value requires rethinking workflows—people, processes, and technology together—not just building impressive-looking agents. For example, legal-services providers and insurers that built feedback loops into workflows enabled agents to learn continuously and improve results.
Agents aren’t always the solution
Too often, leaders overapply agents when simpler tools would suffice.
Reuse Beats Reinvention
Instead of spinning up a new agent for every task, leading firms invest in reusable components—validated prompts, reusable code, common orchestration frameworks. This reduces redundancy, accelerates deployment, and eliminates waste.
Humans Remain Central
Even as agents expand, humans remain vital for oversight, judgment, and compliance. The most effective deployments:
The Road Ahead
Agentic AI is advancing quickly, but one lesson is clear: success depends less on the technology itself and more on how organizations redesign workflows, choose the right tools, build user trust, and thoughtfully integrate humans and agents.
AI can’t dream, but it can supercharge the path from idea to market. Bain’s Innovation Report 2025 shows how the smartest companies are blending machine efficiency with human imagination.
Innovation is still broken
Companies run hackathons, fill whiteboards, and launch idea challenges—yet most projects stall. Only 5–25% of new products actually succeed. The system is noisy, slow, and wasteful.
Where AI helps most
AI isn’t replacing creativity—it’s speeding up everything around it. Leading firms are using AI to scan massive datasets for trends, build and test prototypes virtually, predict which ideas are most likely to succeed, and automate the tedious parts of R&D so humans can focus on strategy.
Already, 88% of top innovators say AI has improved their innovation success rates, with many cutting design-to-launch times by more than 20%.
Where AI falls short
Despite these gains, AI still struggles in the areas that matter most for real breakthroughs. It finds it hard to generate truly radical, out-of-the-box ideas and tends to avoid risky bets, sticking too closely to historical data. It also misses the nuances of human empathy and cannot replicate the messy, serendipitous collaboration that sparks creativity in teams. In short, AI can sharpen the process, but it cannot replace the spark of originality.
The human-AI balance
The lesson from Bain’s case studies is clear: growth comes when AI and humans co-pilot. A US life insurer, for example, used AI analysis and synthetic customers to redesign strategy, unlocking a potential $1B business. Similarly, a telecom giant paired AI with traditional research to crack new customer segments without cannibalizing its premium brand. These examples show that the real magic happens when companies let machines accelerate the process while humans shape the vision.
The next chapter
AI is here to accelerate, not imagine. True breakthroughs will still come from humans—dreaming big, taking risks, and steering strategy. The future belongs to companies that know when to let AI run, and when to let human creativity lead.
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