AI is a 100x force that will elevate human capabilities to superhuman levels. This isn’t hyperbole – we’re witnessing the dawn of the Collaborative Intelligence Era, where AI and humans will work together through “fusion skills” to fundamentally change how we work, live, and play.
I recently participated in the AI Governance Alliance (AIGA) of the World Economic Forum, which includes over 400 representatives from top tech companies, academia, policymakers, investors, consulting, banking, services, and government agencies. The AIGA launched an AI Transformation of Industries initiative and partnered with Accenture to create this white paper.
My key takeaway? AI is completely redefining how we think about productivity and value creation. It’s not just another tech trend – it’s a critical opportunity to drive sustainable growth and innovation.
For founders building AI companies, this newsletter offers insights into enterprise AI adoption patterns and where major industries stand in their AI transformation journey. For a deep dive, read our CIO Enterprise AI report. The key here is understanding which industries are racing ahead (financial services, media, and technology lead the pack), which business functions deliver immediate ROI (marketing, sales, and service operations show the strongest traction), and how to time your market entry to align with enterprise adoption readiness.
Bottom line: AI is a fundamental shift that will transform every industry through human-AI collaboration. Organizations that understand this shift will thrive; those that don’t risk being left behind.
The Current AI Landscape: Breaking Through Experimental Boundaries
According to McKinsey, 72% of organizations are now using genAI in at least one job function, though most are still in the experimental stage. But investment momentum is accelerating rapidly, according to IDC:
The industries seeing the biggest impact are those that rely heavily on human expertise and knowledge work: healthcare, financial services, media/entertainment, sports, consumer, and professional services. They’re benefiting from genAI’s ability to generate content, deliver insights, and provide solutions that boost productivity and decision-making.
What they’re spending their AI budgets on varies by industry (see below):
The industries where people spend the most time on tasks AI could automate are financial services, software, and media. See below for data on industries with high potential for automation and augmentation.
In terms of corporate job functions, those that generate large volumes of data have been leading the AI charge so far – marketing and sales, product development, service operations, and risk management. However, we can expect more AI-driven disruption in finance, HR, marketing, and sales functions in the coming year. The more data there is to work with, the easier it will be to create, train, and scale AI models. See below for a summary of job functions by industry that can be reimagined using AI.
The real opportunity going forward? Identifying the right AI applications for the specific industry, figuring out how to amplify their impact, and carefully managing the inevitable complexities and risks that come with implementation.
Bottom line: AI adoption is accelerating rapidly with spending projected to reach $630B by 2028. Industries with data-rich environments and knowledge-intensive work are leading adoption, with financial services, media, and technology at the forefront.
How AI is Creating Real Business Value Today
The focus is shifting from using AI merely for efficiency gains to deploying it for growth opportunities and, in some cases, completely reinventing entire value chains. Currently, 82% of businesses see AI as one of their main levers for reinventing their businesses, with tangible results already emerging:
Here are some emerging use cases beyond operational efficiency that caught my eye:
1. AI driving revenue generation
AI’s creative partnership with fashion designers is revolutionizing the industry. A leading tech firm has developed an AI design assistant that analyzes historical sales data, color trends, and social media engagement to predict which designs will resonate with customers before production.
Designers who once spent weeks creating collections can now generate dozens of variations in minutes, balancing artistic vision with commercial reality. The AI identifies elements from best-selling designs that should be incorporated into new collections, ensuring a mix of innovative and proven concepts.
Takeaway: AI is driving top-line growth by enhancing human creativity and accelerating innovation cycles.
2. Enhancing customer experience
Mojang Studios (behind Minecraft) integrated an advanced data intelligence platform that processes player interactions 66% faster than their previous system—a game-changer for a platform with over 140 million monthly active users.
The system personalizes experiences based on individual player behavior, identifies patterns humans might miss, and monitors player sentiment on social media for real-time feedback. For a 15-year-old game to maintain, this level of personalization demonstrates how AI can breathe new life into established platforms.
Takeaway: AI enables hyper-personalization at scale, delivering individualized experiences to millions of customers simultaneously—something impossible with human-only approaches.
3. Solving “unsolvable” business problems
Semiconductor giant AMD partnered with a major software company to develop a genAI supply chain troubleshooter that’s redefining how they manage order commitments. Before implementing this solution, teams would spend hours manually reviewing order commitments, often delaying critical decisions.
The results have been dramatic: 3,120 hours of productivity saved annually, and root-cause analysis time reduced by 90%. Perhaps most valuable is the empowerment of employees and improvement in customer relationships—AMD can swiftly pinpoint and rectify order issues while redirecting talent to strategic initiatives.
Case study takeaway: AI is tackling previously “unsolvable” business problems, particularly those involving complex systems with multiple variables. In AMD’s case, this created triple benefits: time savings, better customer relationships, and more strategic use of human talent.
4. Human-AI collaboration in healthcare
A forward-thinking hospital services company deployed a machine learning model that analyzes over 72 variables to predict discharge readiness within 24 hours. The AI doesn’t make discharge decisions—instead, it flags patients who meet specific criteria and provides actionable insights through a cloud-based interface accessible to the entire care team.
Since implementing the system across 12 hospitals, daily discharge rates have increased by 4.6% in just six months. Patients benefit from more timely transitions and reduced risk of hospital-acquired complications.
Case study takeaway: The most successful AI implementations don’t replace human judgment—they enhance it, analyzing complex data sets and surfacing actionable insights while leaving final decisions to human experts.
Moving Beyond Experimentation: AI Transformation Roadmap
As companies move beyond experimentation, we’ll see a broad shift to enterprise-wide business reinvention — and, ultimately, the creation of new business ecosystems.
To get there, organizations need to prioritize AI applications that deliver real, measurable value — and then focus on scaling these applications across the business. This means it’s important for companies to accurately assess where they are in their AI journey.
The AIGA AI Transformation report provides a helpful rubric with five phases (full chart below):
Bottom line: Most organizations are still in phases 1 or 2 of AI adoption, running disconnected experiments. True transformation requires moving to phases 3-5, where AI becomes integrated into the core business and eventually transforms entire value chains.
2025: The Year of AI Acceleration
As AI moves beyond experimentation, we’ll see a broader shift towards enterprise-level reinvention of business and operating models. Early signs of transformation are already visible. Leading organizations are implementing changes to transition towards AI-enabled models, and some AI-native models are already emerging. New AI-powered intermediaries could disrupt incumbents while driving value dynamics shifts and the development of emerging business ecosystems.
For organizations looking to capitalize on this shift, I recommend focusing on:
This approach will help accelerate growth, avoid common pitfalls, and unlock AI’s full potential in your business.
Bottom Line: AI adoption is accelerating rapidly, with 2025 poised to be a pivotal year as organizations move from experimentation to enterprise-wide implementation. The companies that will thrive are those that understand this fundamental shift and embrace human-AI collaboration as a strategic imperative.
Success will come to founders who can identify high-impact use cases, iterate quickly, and scale effectively across their organizations. The AI transformation is not just coming—it’s here, and the opportunities for visionary founders have never been more exciting.
