This Month in AI - November 2025

​Welcome to the November 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:

AI Works Best with Humans in the Loop

For all the noise around fully autonomous AI agents, real-world evidence continues to point in a different direction. A recent study including Upwork’s latest evaluation of AI agents on live client projects is making one thing clear: AI is most powerful not when it replaces humans, but when it works with them.

The study shows something many practitioners have already felt intuitively. Today’s most advanced agents can generate ideas, draft code, analyze data or structure marketing plans, but left on their own, they struggle to deliver complete, reliable, professional-grade work.

The Human Difference

The moment a human expert steps into the loop, everything changes. What emerges is a hybrid workflow where AI handles the heavy lift - speed, iteration, pattern recognition, while humans supply what machines still can’t: judgment, nuance, domain context, and the ability to recognize what “good” actually looks like. In these partnerships, quality doesn’t just improve; it skyrockets. In some cases, project completion rates jumped by nearly 70% with as little as 20 minutes of expert feedback.

Instead of chasing an autonomous agent future, the more practical and more impactful path is to design human-supervised, feedback-driven workflows. This compresses timelines from days to hours, elevates output quality, and creates a new class of roles around AI oversight, feedback design, and quality verification.

And that’s the shift many organizations are just beginning to recognize: AI is not a shortcut to eliminating work; it’s an accelerant for doing better work. The research echoes a broader truth playing out across industries - the companies seeing real ROI from AI aren’t the ones trying to remove humans from the loop, but the ones equipping humans to guide the loop.

The future of work won’t be defined by machines acting alone. It will be defined by systems where humans teach, correct, steer, and amplify what AI can do. In that model, the gains are not incremental, but transformative.

​The New AI Divide in 2025: High Performers vs. Everyone Else

​McKinsey’s The state of AI in 2025: Agents, innovation, and transformation survey paints a clear picture: AI has crossed the adoption chasm, but not yet the impact chasm.

Nearly 88% of companies now report using AI in at least one business area, up sharply from the year before, signaling that AI is no longer experimental. Yet, despite this surge in usage, only a minority of organizations have succeeded in scaling AI across multiple functions or embedding it into enterprise-wide operating models.

One of the most important shifts highlighted this year is the emergence of agentic AI. About 62% of organizations say they are already experimenting with AI agents, systems that can autonomously execute multi-step tasks like planning, coding, customer routing, or supply-chain adjustments. McKinsey positions agents as the next major unlock for productivity and transformation, potentially reshaping white-collar work at scale.

But the report also exposes a widening performance gap. A small cluster of “AI high-performers”- just 6% of companies - are capturing disproportionately higher business value. What sets them apart is not just adoption, but ambition:

• They redesign workflows instead of layering AI onto legacy processes.
• They deploy AI across more functions, from marketing to product development to corporate strategy.
• They invest deeper, with stronger leadership sponsorship and clearer governance.
• They focus on both efficiency and innovation, treating AI as a driver of new value, not just cost savings.

Across the broader market, companies are seeing incremental wins - reduced time in software development cycles, improved customer response, and lower operational effort, but only 39% report a measurable impact on profits so far.  The underlying reason: most organizations still struggle with foundational blockers like data readiness, cross-functional alignment, responsible AI governance, and scaling beyond pilots.

Taken together, the report suggests that 2025 is the year when AI becomes table stakes, but meaningful competitive advantage will belong to the companies willing to rethink how they operate, not just automate what they already do.

​AI is Becoming More Important to Companies

AI is no longer just an experiment. According to Bain’s Executive Survey: AI Moves from Pilots to Production, companies are scaling AI faster, seeing measurable impact, and preparing for industry disruption.

The survey of nearly 200 executives shows that AI is climbing the corporate agenda: 74% now rank it as a top-three strategic priority, up from 60% a year earlier, and 21% consider it their number-one priority. Adoption is broad, extending far beyond early pilots into multiple domains including software development, customer service, marketing, knowledge work, and IT.

From pilots to production at scale
Contrary to the misconception that AI rarely moves beyond pilot projects, Bain’s findings reveal that a growing share of use cases are being deployed at scale. In software development alone, 40% of pilots have moved into full-scale production, with customer service, sales, and marketing also seeing 20–30% scaling rates. Across domains, adoption is accelerating faster than previous technology waves: for instance, 73% of companies now use AI in software development, up from 66% a year earlier, with similar growth in customer service, marketing, IT, and knowledge worker functions. Even in areas with lower adoption, growth is rapid, signaling that AI is becoming a core part of business operations across industries.

Real results
Satisfaction with AI rises as companies transition from using it as an assistant to assigning it agentic workflow automation. Across organizations meaningfully adopting generative AI, 80% of initiatives met or exceeded expectations, yet only 23% of all respondents could directly tie AI deployment to new revenue or cost reductions.

When successful, the outcomes are tangible: measurable productivity gains, faster project completion, and real business transformation.

Navigating risk and disruption
As AI scales, concerns about disruption are rising, particularly in technology sectors where 44% of companies now report high or very high disruption risk. Yet businesses are learning to manage these risks, including challenges around data privacy, security, and integration, while simultaneously unlocking the benefits of AI-guided decision-making.

The takeaway
Generative AI is no longer a niche tool - it is an essential lever for competitive advantage. Companies moving thoughtfully from pilot programs to scaled deployment are seeing real impact, faster insights, and higher satisfaction. The Bain survey underscores that success is not guaranteed, but with strategy, governance, and human oversight, AI can transform the speed, quality, and reach of business insights.

​How Agentic AI Is Poised to Help Manufacturing

Deloitte’s 2026 Manufacturing Industry Outlook shows that manufacturers are doubling down on smart manufacturing in a big way. 92% of industry leaders now believe smart manufacturing will be the primary driver of competitive advantage, and the confidence is backed by real results. Companies already using smart-manufacturing practices are reporting:

• Up to 20% improvements in production output
• 7–20% boosts in employee productivity
• 10–15% in newly unlocked capacity

These gains show why AI and increasingly agentic AI are becoming central to how modern manufacturing operates.

According to the report, Agentic AI can help manufacturers across supply chains, operations, and customer service by taking limited autonomous actions with human oversight. Specifically, agentic AI can:

• Identify and engage alternative suppliers during supply chain disruptions
• Capture institutional knowledge from retiring employees
• Make factory roles more attractive to younger generations
• Maximize uptime through autonomously generated shift handovers and work instructions
• Improve customer experience by simplifying and speeding up equipment repair

Deloitte also highlights aftermarket services as one of the biggest near-term opportunities. With access to data across inventories, service schedules, customer platforms, and MES systems, agentic AI could:

• Detect component wear and autonomously trigger parts orders, service scheduling, and inventory reallocation
• Adjust service-level agreements based on equipment usage and risk
• Evaluate machine telemetry, validate claims, and support warranty decisions

​AI-First Retail: Why the Next Generation of Retail Winners Will Be Built on AI

​Retail is entering a pivotal moment. BCG’s latest executive perspective, AI-First Companies Win the Future for Retail , shows that AI isn’t just another technology trend for the industry. It’s becoming the operating system of the next generation of retail. But while the promise is massive, the pressure to keep up is equally real.

The promise vs. the pressure

AI is reshaping everything from discovery to delivery, but retailers are struggling with the pace of change. According to BCG’s findings:

• AI can unlock 5%–15% conversion lifts through more personalized journeys and AI-powered shopping agents.
• Early adopters are already using AI to cut operational costs, accelerate decision cycles, and optimize supply chains.
• Yet most retailers say they are overwhelmed by the speed of AI innovation and many fear they lack the tech and talent foundations to keep up.
• The competition isn’t slowing down. As BCG notes, retailers that hesitate risk losing relevance because AI-native players are already reshaping customer expectations.

In short, the upside is immense, but the capability gap is widening.

What separates AI-first leaders

Even as many retailers dabble with AI, only a small set of “AI-first” organizations are creating material business impact. BCG’s research shows these leaders share a few defining traits - already widening the performance gap and setting future industry benchmarks.

• Strategic focus, not experimentation overload. They choose a few high-impact AI bets and scale them.
• Cross-functional operating models. Retail, tech, data, and ops work as one team — not in silos.
• Ownership of AI experiences. Many are building “owned agents” and proprietary AI workflows instead of relying solely on external tools.
• Reinvestment flywheel. Savings from AI productivity go straight back into talent, data foundations, and scalable AI infrastructure.

Why now: the convergence moment

For retailers, waiting is no longer neutral. It’s a competitive risk. BCG points to four forces accelerating AI’s inevitability in retail:

• AI systems are more capable and cheaper to deploy than ever.
• Consumers are already using AI tools to decide what to buy.
• The scarcity of AI talent is creating a “first-mover advantage.”
• Competitors are scaling AI rapidly, raising the bar for everyone else.

What this means for retail leaders

BCG’s message is clear: AI will define the next era of retail winners. But success depends on more than technology. It requires rethinking teams, workflows, and decision-making.

Retail leaders will need to:

• Build AI into core strategies, not side projects.
• Strengthen data foundations and modernize operating models.
• Equip teams to collaborate faster and make decisions with real-time intelligence.
• Prepare to meet customers inside AI ecosystems, not just on stores and websites.

In other words: AI may automate tasks, but it will redefine how teams collaborate, make decisions, and compete. Leaders who embrace this shift now will shape the future of the industry.

​That’s it for the November edition of This Month In AI. We hope you enjoyed the read.

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