- Generative AI Enterprise
- Posts
- 10 must-read AI market reports
10 must-read AI market reports
Plus, key takeaways to help you level up fast.
Lewis Walker
January 28, 2025
WELCOME, EXECUTIVES AND PROFESSIONALS.
A deep understanding of executive and key decision-maker sentiment on AI’s progress and challenges across enterprises is key for driving opportunities and partnerships.
Insights from advisory, tech, and venture firms, drawn from experience and surveys of hundreds of executives, can vary.
Here are 10 must-read viewpoints:
MCKINSEY
Image source: McKinsey & Company
Brief: McKinsey’s first-of-its-kind report informed by a survey of 700+ technology leaders and senior developers across 41 countries, explores how enterprises are increasingly turning to open source AI to build out their technology stacks.
Breakdown:
Over 50% of firms use open source AI like Meta’s Llama or Google’s Gemma, often alongside proprietary options from OpenAI, Anthropic, and others.
Firms that see AI as important to their competitive advantage are more than 40% more likely to report leveraging open source AI.
Open source AI use is highest in tech, media, and telecom (70%). Experienced AI developers are 40% more likely to use it than their peers.
60% find open source AI has lower implementation costs, 46% cite lower maintenance costs, but proprietary AI has faster time to value.
Top open source AI concerns include cybersecurity (62%), regulatory compliance (54%), and IP infringement (50%).
Why it’s important: Leading enterprises are well-positioned to harness open source AI where aligned to their unique context. As with cloud and software, a multimodel approach will be prevalent for many companies, with open source and proprietary AI coexisting across multiple layers of the technology stack.
Full report here. Date published: April 2025
MICROSOFT
Image source: Microsoft
Brief: Microsoft analyzed survey data from 31,000 workers in 31 countries, LinkedIn labor market trends, and trillions of Microsoft 365 productivity signals, revealing the emergence of an entirely new organization: the Frontier Firm.
Breakdown:
Frontier Firms run on on-demand intelligence and hybrid teams of humans and agents scaling fast, staying agile, and delivering value faster.
These firms are already taking shape, and within 2–5 years, most organizations are expected to be on their path to becoming Frontier Firms.
82% of leaders say this is the year to rethink strategy, and 81% expect agents to be integrated into AI plans within 12–18 months.
Microsoft sees three Frontier Firm phases: first, every employee gets an AI assistant that helps them work better and faster.
Second, agents join teams as "digital coworkers" taking on specific tasks. Thirdly, agents execute entire business processes, checking in as needed.
Why it’s important: The time for pilots alone has passed. Real change requires full-scale adoption. Define roles where automation adds value and treat digital employees like teammates. Identify processes for full automation and those best suited for human-AI collaboration.
Full report here. Date published: April 2025
ANTHROPIC
Image source: Anthropic
Brief: Anthropic launched The Anthropic Economic Index revealing “the clearest picture yet” of how AI is being used in real-world work. The results, detailed in a 38-page report, show AI is primarily used to augment human capabilities, among other findings.
Breakdown:
Despite many surveys on AI’s impact on work, large-scale empirical evidence on how these systems are actually used for different tasks remains scarce.
Anthropic analyzed 4M+ Claude conversations, mapping tasks and occupations using the U.S. Department of Labor’s O*NET Database.
AI adoption is highest in software development and writing, accounting for nearly half of total use. Adoption remains early, with ~36% of occupations using AI for at least 25% of tasks.
AI is used 57% for augmentation (e.g., learning, iterating) and 43% for automation (e.g., fulfilling requests).
The true augmentation rate may be higher: e.g. a user asks Claude to draft a memo but later refines it themselves.
Anthropic says it mitigated the limitation of analyzing only Claude Free and Pro data (excluding API and Enterprise) by filtering out non-work conversations.
Why it’s important: Many AI adoption surveys exist from advisory, research, and tech firms, but Anthropic’s stands out for its scale and system-driven, empirical approach, revealing early signals of AI’s impact as it automates, augments, and expands, shaping the future of work and transformation.
Full report here. Date published: February 2025
MCKINSEY & COMPANY
Image source: McKinsey & Company
Brief: McKinsey’s 47-page report finds the biggest barrier to scaling AI is not employees, but leaders who aren't steering quickly enough. Inspired by Reid Hoffman’s new book 'Superagency,' it explores amplifying human potential with AI.
Breakdown:
Employees are more ready for change than leaders imagine. 3x more employees use gen AI for a third or more of their work than leaders think. 70% believe it’ll change 30% or more of their work within 2 years.
Companies need to move fast. Employees trust leaders to balance speed with safety. 47% of C-suite say gen AI is moving too slowly, despite 69% having invested over a year ago.
Employees are 1.3x more likely to trust their own companies to deploy gen AI right than other institutions.
Almost all companies invest in AI, but just 1 percent believe they are at maturity. 92% plan to invest more over the next 3 years.
Leaders need to recognize their role in driving gen AI transformation. Executives are 2.4x more likely to cite employee readiness as a barrier than leadership.
48% of employees say training is key for gen AI adoption, but nearly half feel they’re receiving moderate or no support.
Why it’s important: AI's rapid technological progress in the past two years is stunning. While some see it as a risk to humanity, what if we focused more on what could go right? Leaders may increasingly realize we're poised for a new era of productivity, innovation, and progress. The real risk? Thinking too small.
Full report here. Date published: January 2025
BOSTON CONSULTING GROUP
Image source: Boston Consulting Group
Brief: BCG's new AI Radar survey, published January 2025, captures the sentiment of 1,803 C-level executives who revealed their AI wins and struggles. The initial excitement around AI, especially gen AI, is evolving into a deeper focus on execution and results.
Breakdown:
75% of executives rank AI, particularly gen AI, as a top-three priority for 2025. While only 25% see significant value today, they plan to increase gen AI investments by 60% over the next 3 years.
Leading companies allocate over 80% of AI investments to reshaping core functions and creating products/services, not smaller, productivity projects.
Leading companies focus on 3.5 key AI use cases, compared to 6.1 for others. This leads to 2.1x higher ROI from AI initiatives than their peers.
While top firms track financial impact, 60% lack KPIs for AI value creation. Fewer than 33% have upskilled 25% of their workforce in AI.
Top performers follow the 10-20-70 rule: 10% on algorithms, 20% on data/technology, and 70% on people, processes, and culture. A strategy also effective with AI agents.
Why it’s important: AI success isn’t just technical; it’s sociological. In early 2025, AI stands between potential and reality. As investments and ambitions grow, so does awareness of the work ahead. Progress requires disciplined execution, a focus on value, and a workforce ready to adapt.
Full report here. Date published: January 2025
DELOITTE
Image source: Deloitte
Brief: Deloitte's latest quarterly survey, published in January 2025, drawing insights from 2,773 director- to C-suite-level leaders across 14 countries, explores trends, barriers, and opportunities in gen AI adoption, offering insights on organizational adaptability, regulation, and agentic AI.
Breakdown:
Most organizations aren’t moving as fast as technological innovation. 78% of respondents plan to increase AI spending in the next fiscal year.
Regulatory compliance is now the top concern hindering gen AI deployment, rising from 28% in Q1 to 38% in Q4.
IT is the most mature gen AI function, with cybersecurity, operations, marketing, and customer service also seeing strong results.
Gen AI deployments are increasingly aimed at areas critical for industry-specific competitive differentiation.
C-suite leaders are more optimistic about gen AI, expecting barriers to be overcome and value delivered faster than other leaders anticipate.
Agentic AI is gaining traction for its potential to handle increasingly complex tasks and unlock gen AI’s full potential, but the broad challenges facing enterprises still apply.
Why it’s important: The report conveys pragmatic optimism. Seventy-four percent of respondents say their most advanced gen AI initiative meets or exceeds ROI expectations. Six real-world case studies are highlighted, showcasing enterprise results. However, achieving widespread success requires patience and hard work.
Full report here. Date published: January 2025
ACCENTURE
Image source: Accenture
Brief: Accenture’s 67-slide report, published by CTO Karthik Narain and his research team in January 2025, envisions a profoundly different, autonomous workplace reshaped by gen AI agents, and explores what it takes to build this future.
Breakdown:
As gen AI becomes central to enterprise tech, development costs drop and new systems emerge. 77% of execs agree agents will reinvent systems.
AI agents can personalize at scale but risk brand homogenization. 80% of leaders worry chatbots sound generic, challenging differentiation efforts.
Robots with embedded LLMs have generalist versatility, moving past pre-programmed limits. 74% of leaders see the promise of intelligent robots.
Gen AI empowers employees by amplifying their capabilities, creating a feedback loop that expands the autonomy of people and AI over time.
Leaders see the challenges of creating this future, including high upfront investments, data quality, new skills, and most importantly, building trust.
Why it’s important: Leaders must prepare for a world where AI is everywhere and acting autonomously on behalf of people. Forward-thinking enterprises are already leveraging this transitional period to invest in building the trust needed for innovation, growth and making this future a reality.
Full report here. Date published: January 2025
IBM
Image source: IBM
Brief: IBM's 26-slide report, published in December 2024, outlines five key trends for 2025, offering strategies for attaining competitive advantage. The insights are backed by data from 400 leaders across 17 industries and six regions.
Breakdown:
Trend 1: Agentic AI will transform your business-but first you must reskill your people. In 2024, CEOs estimated 35% of their workforce needed reskilling—up from 5% in typical years.
Executives predict a 21% rise in decision-making by digital assistants within two years, driven by GenAI, which will require new skills in oversight of autonomous decision-making.
Trend 2: Despite efforts to curb technical debt, only 25% strongly believe their IT infrastructure can scale AI enterprise-wide. To address this, leaders can leverage strategies like linking modernization efforts to long-term productivity gains.
Trend 3: In the age of AI, location is everything. In 2024, 67% of executives reported AI influenced their location strategies, and expect that to increase to 93% by 2026, prioritizing comparative advantages like talent and infrastructure.
Trend 4: The rapid pivot to AI has upended IT budgets, but self-funding is imminent. 75% of leaders view GenAI as an innovation investment, reallocating traditional IT funds. By 2026, 95% expect GenAI to be at least partially self-funded.
Trend 5: AI product and service innovation is the #1 CEO goal, yet business models aren't keeping up. 62% of CEOs believe they must rewrite their playbooks for future success; 89% of executives say AI will drive product and service innovation.
Why it’s important: IBM’s findings highlight key strategies for enterprises to tackle workforce readiness, technical infrastructure, operational agility, and business model innovation to win in an AI-driven future.
Full report here. Date published: December 2024
MENLO VENTURES
Image source: Menlo Ventures
Brief: Menlo Ventures surveyed 600 U.S. leaders for its 2024 Generative AI in the Enterprise report, published in November 2024, highlighting key trends in enterprise GenAI spending, adoption, and technology evolution.
Breakdown:
Enterprise GenAI spending surged to $13.8 billion in 2024, a 6x increase from $2.3 billion in 2023. Healthcare leads enterprise spending with $500 million, followed by legal ($350 million) and financial services ($100 million).
Top use case adoption includes code generation (51%), support chatbots (31%), and enterprise search (28%). Price of tools is a minor concern (1%), with leaders prioritizing tools with potential to deliver long-term value (30%).
OpenAI’s enterprise market share fell from 50% to 34%, while Anthropic doubled from 12% to 24%, and Google also gained traction. Enterprises now deploy three or more foundation models. Closed-source options hold 81% market share.
RAG adoption surged to 51% in 2024, up from 31% in 2023, while only 9% of production models are fine-tuned. Agentic architectures debuted with 12% adoption, poised to disrupt the $10 trillion U.S. services economy.
Enterprises are converging on core runtime architectures for production apps, structured across four layers: (1) compute and foundation, (2) data, (3) orchestration, and (4) tooling (detailed breakdown in full article).
Vertical GenAI apps are gaining momentum. Initial applications focused on horizontal solutions like text and image generation, but domain-specific apps are gaining traction as enterprises seek differentiation.
Why it’s important: GenAI is set to reshape the enterprise. Spending growth, verticalization, and emerging architectures signal change ahead. Enterprises that adapt quickly to these shifts could capture outsized value.
Full report here. Date published: November 2024
CAPGEMINI
Image source: Capgemini
Brief: Capgemini published a 150-page report in October 2024, ‘Gener(AI)ting the Future’ which explores the potential of GenAI to impact enterprises and society, through various influential perspectives.
Breakdown:
The report features insights from pioneering founders and leaders in business, policy, and academia. For instance, Mistral AI CEO Arthur Mensch: “The rare talent that we recommend every organization look for is the software engineer who can also do data science.”
AI pioneer Andrew Ng: “For many jobs, AI will only automate or augment 20-30% of tasks. So, there's a huge productivity boost, but people are still required for the remaining 70% of the role.”
Capgemini CEO Aiman Ezzat: “Any use case that requires high performance or deep domain expertise will likely continue to go down the path of specialized models.“
Over the past year, GenAI adoption has increased across various domains (sales and marketing, IT, operations, R&D, finance, and logistics), reshaping roles for employees to concentrate on more complex strategic tasks.
Early adopters of GenAI are reporting positive outcomes, such as improved operational efficiency and enhanced customer experiences, though ethical considerations remain critical to prevent misuse.
The report highlights GenAI's role in tackling climate change, emphasizing the advantages of small language models (SLMs) and includes recommendations for successful GenAI implementation.
Why it’s important: Capgemini's report highlights the transformative potential of GenAI across industries and the importance of strategic leadership and ethical practices to maximize its benefits.
Full report here. Date published: October 2024
DELOITTE
Image source: Deloitte
Brief: Deloitte research, published in October 2024, outlines four possible futures for generative AI's impact on enterprises by 2027, offering scenarios to help enterprises pressure-test and strengthen their AI strategies.
Breakdown:
Scenario 1 (Growth with Costs): Adoption advances across the enterprise. While some workers see this as intrusive, enterprises increasingly leverage agents that automate and amplify complex, open-ended work.
Scenario 2 (The Bubble Bursts): Generative AI underdelivers amid high expectations. Inaccuracy and workforce cuts lead to frustration, as misleading outputs complicate decision-making and lower than anticipated efficiency gains.
Scenario 3 (Advancement Depends on Humans): Success favors strategy over speed as premature scaling of GenAI proves challenging. While early gains are promising, organizations struggle to replicate success across their full scale.
Scenario 4 (All Systems Go): By 2027, GenAI fuels a creative boom, combining with robotics, biology, to other branches of machine learning, unleashing a wave of innovation and growth that spans across the economy.
For each of these scenarios, the research explores opportunities and risks for people, technology, and culture, and offers steps you can take to maximize investments.
These scenarios don’t predict the probability of outcomes but challenge organizations to anticipate and manage GenAI opportunties and risks of each scenario in the context of their strategies and projects.
Why it’s important: These scenarios highlight the transformative potential of GenAI. As industries adapt, enterprises that build resilient, forward-thinking AI strategies can lead the next wave of industry innovation, or risk being outpaced.
Full report here. Date published: October 2024
Image source: Google
Brief: Burak Goktruk, VP of Engineering at Google, spoke at the end of September 2024 about the “tens of thousands of enterprises calling and wanting to try gen AI” and a “36x growth in API usage in 6 months” from January 2024, which “typically takes 5-10 years”.
Breakdown:
Enterprises are undergoing a "mindset change" with significantly less data required to use pre-trained large language models (LLMs) compared to legacy AI, which typically demanded extensive training datasets and iteration. This shift allows even non-specialist developers to create certain AI applications.
Most enterprises are focused on prompt engineering and retrieval-augmented generation (RAG), ensuring factual responses grounded in internal documents or the web.
Parameter-efficient fine-tuning methods like LoRA and QLoRA rather than full fine-tuning are gaining popularity for their lower computational needs.
As base models improve rapidly, enterprises often find that new model releases quickly outperform specialized, domain-specific versions. Goktruk noted the growing use of sparse models, which reduce costs and improve latency by executing only a small fraction of pathways at inference time.
Choosing the right platform is key for most enterprises rather than specific models. API call costs are trending to zero due to improvements in GPU performance. Global demand for AI chips is "probably 100x" higher than supply.
Goktruk's 91 slides also cover topics such as the characteristics and benefits of common model adaptation approaches.
Why it’s important: The rapid pace of innovation in AI presents both opportunities and challenges for enterprises. To stay competitive, businesses should adapt by leveraging appropriate approaches and technologies.
Full report here. Date published: September 2024
ADDITIONAL MUST-READS
Enterprise AI case studies (20 reports)
Playbooks for AI leaders (16 reports)
Agentic AI reports (19 reports)
LEVEL UP WITH GENERATIVE AI ENTERPRISE
Generative AI is evolving rapidly in the enterprise, driving a new era of transformation through agents and innovative applications.
Twice a week, we review hundreds of the latest insights on best practices, case studies, and innovations to bring you the top 1%...
Explore sample editions:
All the best,
Lewis Walker
Found this valuable? Share with a colleague.
Received this email from someone else? Sign up here.
Let's connect on LinkedIn.