Real-world adoption of AI: How AI is Quietly Rewiring the Global Marketplace
A reflection on the real-world adoption of AI across industries, from financial services to healthcare, legal, manufacturing, and beyond, as theoretical excitement gives way to tangible, operational transformation.
As 2025 draws to a close, I find myself reflecting on a year where the abstract buzz around artificial intelligence solidified into something far more concrete. The conversations have shifted from what AI could do to what it is doing, transforming workflows, creating new efficiencies, and fundamentally altering the competitive landscape across every sector of the economy.
While the financial services sector has been a prominent early adopter, with projected AI spending expected to reach nearly $100 billion by 2027, the tendrils of this technological revolution are reaching much further, quietly and profoundly reshaping industries far beyond Wall Street. From hospital corridors to law firm conference rooms, from factory floors to agricultural fields, AI is no longer a futuristic concept; it is part of daily business.
My work this year has given me a front-row seat to this transformation. I've had the privilege of speaking with founders, executives, and frontline operators across a spectrum of fields, and a clear pattern has emerged. The initial phase of broad, untargeted enthusiasm is giving way to a more discerning, ROI-driven approach. Organizations are no longer just experimenting with AI; they are strategically embedding it into their core operations. This isn't just about adopting new tools; it's about rewiring the very DNA of how businesses operate. Some are more efficient than others. This is a brief look at some
Financial Services: The $100 Billion Proving Ground
Financial services have long been at the forefront of technological adoption, and AI is no exception. According to a recent analysis by Insight Partners, the sector is expected to spend nearly $100 billion on AI by 2027. But what's most interesting is not the scale of investment, it's the shift in mindset.
"There's a healthy amount of skepticism around how much efficiency AI can truly deliver. The potential productivity gains are real, but it's my job to separate substance from noise to determine what will actually deliver impact." Jennifer Charters, EVP and CIO at Lincoln Financial
Most financial institutions now view AI as a helpful tool that can create leverage in targeted areas, but not a universal fix. Firms are increasingly selective in their approach, prioritizing companies that can demonstrate measurable ROI. The main use cases today are productivity-oriented: investment knowledge management, developer efficiency, and customer service.
Perhaps most striking is the emergence of agentic AI in financial services. BNY Mellon, for example, is already operating over 100 "agentic digital employees" in production, handling tasks ranging from enhancing code to validating payment instructions. This represents a fundamental shift from AI as a tool to AI as a collaborator—systems that don't just respond to commands but work toward goals autonomously.
Healthcare: From Digital Laggard to AI Leader
For years, healthcare was dismissed as a digital laggard, a sector mired in regulation, resistant to change, and a full generation behind every major technological wave. Now, the script has flipped. Healthcare has emerged as the new leader in enterprise AI adoption, deploying AI at more than twice the rate of the broader economy.
A comprehensive report from Menlo Ventures, based on a survey of over 700 healthcare executives, reveals that 22% of healthcare organizations have already implemented domain-specific AI tools, a sevenfold increase over 2024 and a tenfold increase over 2023. By comparison, just 9% of companies in the broader economy have adopted AI, and most of those rely on general-purpose tools rather than industry-specific applications .
A desire for novelty does not drive this acceleration, but by necessity. Healthcare is a $ 4.9 trillion industry that accounts for one-fifth of the U.S. economy, yet it represents only 12% of national software spending. Facing razor-thin margins (often under 1%), a severe labor crisis projected to result in shortages of over 200,000 nurses and 100,000 physicians by decade's end, and rising patient expectations, healthcare leaders are turning to AI as a critical tool for survival and growth .
AI spending in healthcare nearly tripled year-over-year, reaching $1.4 billion in 2025 . The impact is most visible in three key areas:
Area | Investment | Impact |
Ambient Clinical Documentation | $600M | Automates note-taking, freeing clinicians for patient care |
Coding and Billing Automation | $450M | Reduces errors, streamlines invoicing |
Patient Engagement & Prior Authorization | 10-20x YoY growth | Enables 24/7 access, personalized care |
Major health systems are moving fast. Kaiser Permanente deployed Abridge's ambient documentation platform across 40 hospitals and more than 600 medical offices, representing the largest generative AI rollout in healthcare history. Advocate Health has reviewed more than 225 AI tools and implemented 40 live use cases. Mayo Clinic is investing more than $1 billion in AI over the next several years, spanning 200+ projects .
What's most striking is that 85% of this new AI spending is flowing to startups, not legacy vendors. This indicates a fundamental shift in the technology landscape, in which agile, AI-native companies are outmaneuvering incumbents such as Epic, Oracle Health, and Athenahealth.
The Legal Field: A Cautious but Accelerating Embrace
The legal profession, traditionally risk-averse and slow to change, is also beginning to embrace AI, albeit with a characteristic degree of caution. In my pickleball league, I recently spoke with a veteran patent lawyer in Oregon. He told me that he was both excited and concerned about the rise of AI in his field. At the very least, he was very impressed with what AI can do in the legal field.
According to the 2025 Legal Industry Report from the American Bar Association, which surveyed over 2,800 legal professionals, 31% now personally use generative AI for work-related tasks, up from 27% in the previous year .
However, a significant gap exists between individual experimentation and firm-wide adoption. Larger firms (51+ lawyers) report a 39% adoption rate, while smaller firms (50 or fewer lawyers) hover around 20%. The disparity reflects the practical, cultural, and economic factors that shape technology adoption in law: concerns around ethics, accuracy, and client confidentiality remain significant barriers.
When considering investments in legal-specific AI tools, firms are prioritizing solutions that address their unique needs:
Priority | Percentage |
Integration with trusted software | 43% |
Understanding of firm workflows | 33% |
Trust in output vs. consumer tools | 29% |
Ethical alignment | 26% |
Beyond legal research and document review, AI is gaining traction in law firms' business operations. The ABA report highlights that 54% of legal professionals use AI to draft correspondence, and 47% are interested in AI tools that can provide insights from a firm's financial data. This signals a move toward using AI not only for legal work but also for running a more efficient and profitable business.
The procurement landscape is also shifting. Hospital procurement timelines for AI tools have shortened from 8.0 months to 6.6 months (an 18% reduction), while outpatient providers have experienced even faster acceleration. In contrast, payer procurement cycles have lengthened to 11.3 months, reflecting greater risk aversion and regulatory caution.
Manufacturing and Logistics: The Rise of Physical AI
In the physical world of manufacturing and logistics, AI is not just processing data; it's moving atoms. The rise of "physical AI" is driving a new wave of industrial automation, offering robust solutions to manufacturing challenges such as rising costs, labor shortages, and supply chain disruptions.
The smart manufacturing market is booming, with AI at its core. Intelligent robots and systems are optimizing factory floors, enabling everything from automated quality control to self-learning robotic arms. According to the World Economic Forum, physical AI represents a new phase of industrial automation that extends beyond traditional robotics. These systems can perceive, reason, and adapt to their environments in real time.
Similarly, in logistics, AI is being used to address complex challenges such as vehicle routing, demand forecasting, and warehouse optimization. Generative AI is now being used to reshape supply chains beyond simple automation, enhancing efficiency and decision-making across the entire value chain. The result is a more resilient, efficient, and responsive supply chain, critical in a world where disruptions have become the norm rather than the exception.
Agriculture: From Bytes to Bushels
Even the most traditional of industries, agriculture, is being transformed by AI. The AI in agriculture market is projected to grow from $1.7 billion in 2023 to $4.7 billion by 2028. This growth reflects a fundamental shift in how we think about farming, from intuition-based decisions to data-driven precision.
Farmers are using AI-powered drones and sensors to monitor crop health in real time, identify areas requiring irrigation or fertilization, and predict yields with greater accuracy. Machine learning models analyze satellite imagery, weather patterns, and soil conditions to optimize planting schedules and resource allocation.
This is not just about increasing efficiency; it's about making agriculture more sustainable and resilient in the face of climate change. AI can help farmers adopt regenerative agriculture practices, including reduced tillage, cover cropping, and precision nutrient management, all while maintaining or improving yields.
Retail and E-commerce: The Dawn of Agentic Commerce
In the world of retail and e-commerce, AI is personalizing the shopping experience in unprecedented ways. But the next frontier is already here: agentic commerce. AI agents are increasingly researching, comparing, and even purchasing products on behalf of consumers, serving as intelligent intermediaries between buyers and sellers.
This has the potential to fundamentally disrupt online retail. As McKinsey notes, agentic AI is ushering in a new era for consumers and merchants alike. The focus is shifting from attracting eyeballs to providing the best possible data and value to these autonomous agents. Brands and retailers that can effectively communicate with AI agents through structured data, accurate product information, and seamless APIs will have a significant competitive advantage.
Spending on generative AI, which powers these agents, has already increased 3.2x year over year, from $11.5 billion in 2024 to $37 billion in 2025. This explosive growth reflects the conviction that agentic AI is not a distant future, it's the present.
The Common Threads: A New Paradigm of Work
Across these diverse industries, a few common themes emerge that warrant highlighting.
First, adoption is accelerating, but scaling remains the challenge. The McKinsey Global Survey on AI found that 88% of organizations now report regular AI use in at least one business function, up from 78% a year ago. However, most organizations are still in the experimenting or piloting stages. Only about one-third have begun to scale their AI programs across the enterprise. The transition from pilots to scaled impact remains a work in progress.
Second, agentic AI is the next frontier. Sixty-two percent of survey respondents report that their organizations are at least experimenting with AI agents. These systems, capable of planning and executing multiple steps in a workflow, represent a fundamental evolution from AI as a tool to AI as a collaborator. The companies seeing the most value from AI are those that have embraced this shift.
Third, workflow redesign is critical. High-performing organizations are nearly three times as likely as others to have fundamentally redesigned their workflows around AI. Simply layering AI on top of existing processes yields limited results. True transformation requires rethinking how work gets done.
Fourth, data quality and governance remain universal blockers. Across industries, the same challenges emerge: fragmented data, a lack of standardized frameworks, and uncertainty regarding regulatory compliance. Until more straightforward rules and stronger control mechanisms emerge, governance will continue to define the speed and scope of AI adoption.
Finally, and perhaps most importantly, this wave of AI adoption is not about replacing humans; it's about augmenting them. The most successful AI initiatives are those that focus on human-AI collaboration, freeing up people from repetitive, low-value tasks to focus on more strategic, creative, and empathetic work. This is a profound shift in how we think about productivity and value creation.
Looking Ahead
Looking back on this transformative year, it's clear that we are moving beyond the initial hype cycle of AI. The technology is becoming embedded in the fabric of our economy, creating new opportunities and challenges in every sector. The organizations that will thrive in this new era are not those that simply adopt AI, but those that learn to co-create with it, harnessing its power to unlock new levels of human potential.
The real opportunity belongs to companies that can deliver practical, governed, and measurable value. Whether in healthcare, legal services, manufacturing, agriculture, or retail, the winners will be those who approach AI not as a magic bullet but as a powerful tool that requires thoughtful implementation, continuous learning, and a commitment to human-centered design. I can only hope that higher education will continue to play a role in showcasing and leading the way in how AI can and should be human-centered. We will see what 2026 brings.
References
[3] MyCase. (2025, May 6 ). The Legal Industry Report 2025. American Bar Association.
[5] McKinsey & Company. (2025, April 17 ). Beyond automation: How gen AI is reshaping supply chains.
[7] Menlo Ventures. (2025, December 9 ). 2025: The State of Generative AI in the Enterprise.
