Key AI Trends for Ceos to Consider

Traditional ML completely transformed media and advertising in the last decade; the broad applicability of generative AI will bring about even greater change at a faster pace to every industry and type of work. Here are 7 takeaways from my CNBC AI panel at Davos earlier this year with Emma Crosby, Vladimir Lukic, and Rishi Khosla: • For AI efforts to succeed, it needs to be a CEO/board priority. Leaders need to gain firsthand experience using AI and focus on high-impact use cases that solve real business pain points and opportunities. • The hardest and most important aspect of successful AI deployments is enlisting and upskilling employees. To get buy-in, crowdsource or co-create use cases with frontline employees to address their burning pain points, amplify success stories from peers, and provide employees with a way to learn and experiment with AI securely. • We expect 2024 to be a big year for AI regulation and governance frameworks to emerge globally. Productive dialogue is happening between leaders in business, government, and academia which has resulted in meaningful legislation including the EU AI Act and White House Executive Order on AI. • In the next 12 months, we expect to see enterprise adoption take off and real business impact from AI projects, though the truly transformative effects are likely still 5+ years away. This will be a year of learning what works and defining constraints. • The pace of change is unprecedented. To adapt, software development cycles at companies like Salesforce have accelerated from our traditional three product releases a year to now our AI engineering team shipping every 2-3 weeks. • The major risks of AI include data privacy, data security, bias in training data, concentration of power among a few big tech players, and business model disruption. • To mitigate risks, companies are taking steps like establishing responsible AI teams, building domain-specific models with trusted data lineage, and putting in place enterprise governance spanning technology, acceptable use policies, and employee training. While we are excited about AI's potential, much thoughtful work ahead remains to deploy it responsibly in ways that benefit workers, businesses, and all of society. An empowered workforce and smart regulation will be key enablers. Full recording: https://lnkd.in/g2iT9J6j

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

𝗧𝗟;𝗗𝗥: As per McKinsey, success of AI depends 𝗽𝗿𝗶𝗺𝗮𝗿𝗶𝗹𝘆 𝗼𝗻 𝗖𝗘𝗢 𝗹𝗲𝘃𝗲𝗹 𝘀𝗽𝗼𝗻𝘀𝗼𝗿𝘀𝗵𝗶𝗽 and the ability to 𝗿𝗲𝘄𝗶𝗿𝗲 𝗮𝗻 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻’𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 (vs just deploying intelligent chatbots). Interestingly as per METR, AI performance in terms of the 𝗹𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘁𝗮𝘀𝗸𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 𝗲𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗽𝗮𝘀𝘁 𝟲 𝘆𝗲𝗮𝗿𝘀, 𝘄𝗶𝘁𝗵 𝗮 𝗱𝗼𝘂𝗯𝗹𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝗼𝗳 𝗮𝗿𝗼𝘂𝗻𝗱 𝟳 𝗺𝗼𝗻𝘁𝗵𝘀. This will have a huge impact on business rewiring and faster time to outcomes. Some key points from McKinsey & Company State of AI report (https://mck.co/4hMale0): • 78% of organizations now use AI in at least one business function, up from 55% last year. • 𝗟𝗮𝗿𝗴𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗲𝗮𝗱 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝘀 𝗮𝗻𝗱 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗮𝗺𝘀. • CEO oversight of AI governance shows strongest correlation with positive financial impact. • Organizations increasingly mitigate AI risks around accuracy, security, and IP infringement. • Companies are both hiring AI specialists and reskilling existing employees. • Over 80% of organizations still see no material enterprise-level EBIT impact from AI. On a related topic to workflow redesign, METR did some great work (https://bit.ly/4hCk2LQ) where they showed AI's ability to complete tasks (measured by equivalent human time required) has been doubling approximately every 7 months for the past 6 years which means that 𝘄𝗶𝘁𝗵𝗶𝗻 𝟮-𝟰 𝘆𝗲𝗮𝗿𝘀, 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗼𝘂𝗹𝗱 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘄𝗲𝗲𝗸-𝗹𝗼𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗼𝗻𝗲 𝗯𝘆 𝗵𝘂𝗺𝗮𝗻𝘀! (hat tip to Ethan Mollick for the METR link) Organizations that strategically reimagine their operations 𝗮𝗿𝗼𝘂𝗻𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀—centralizing risk and data governance while distributing tech talent in hybrid models as the McKinsey survey suggests—will capture greater value. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗘𝗢𝘀 𝗮𝗻𝗱 𝗖𝗔𝗜𝗢𝘀: Rather than waiting for AI to demonstrate enterprise-wide EBIT impact, 𝗳𝗼𝗿𝘄𝗮𝗿𝗱-𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 𝗼𝘂𝘁 𝘄𝗵𝗶𝗰𝗵 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘁𝗮𝘀𝗸𝘀 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗵𝗮𝗻𝗱𝗹𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗻𝘁𝗵𝘀 𝗮𝗻𝗱 𝘆𝗲𝗮𝗿𝘀, allowing them to proactively restructure roles, retrain employees, and redesign processes to leverage this exponential growth in AI task completion capabilities.

After reviewing dozens of enterprise AI initiatives, I've identified a pattern: the gap between transformational success and expensive disappointment often comes down to how CEOs engage with their technology leadership. Here are five essential questions to ask: 𝟭. 𝗪𝗵𝗮𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀𝘀𝗲𝘁𝘀 𝗴𝗶𝘃𝗲 𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗰𝗮𝗻'𝘁 𝗲𝗮𝘀𝗶𝗹𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲? Strong organizations identify specific proprietary data sets with clear competitive moats. One retail company outperformed competitors 3:1 only because it had systematically captured customer interaction data its competitors couldn't access. 𝟮. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? Look for specific examples of fundamentally reimagined business processes built for algorithmic scale. Be cautious of responses focusing exclusively on efficiency improvements to existing processes. The market leaders in AI-driven healthcare don't just predict patient outcomes faster, they've architected entirely new care delivery models impossible without AI. 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗺𝗮𝗶𝗻 𝗵𝘂𝗺𝗮𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱? Expect a clear decision framework with concrete examples. Be wary of binary "all human" or "all algorithm" approaches, or inability to articulate a coherent model. Organizations with sophisticated human-AI frameworks are achieving 2-3x higher ROI on AI investments compared to those applying technology without this clarity. 𝟰. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? The best responses link AI initiatives to market-facing metrics like share gain, customer LTV, and price realization. Avoid focusing exclusively on cost reduction or internal efficiency. Competitive separation occurs when organizations measure algorithms' impact on defensive moats and market expansion. 𝟱. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗵𝗮𝘃𝗲 𝘄𝗲 𝗺𝗮𝗱𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Look for specific organizational changes designed to accelerate algorithm-enhanced decisions. Be skeptical of AI contained within traditional technology organizations with standard governance. These questions have helped executive teams identify critical gaps and realign their approach before investing millions in the wrong direction. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: V𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 own 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

Agentic AI trends that are a reality already (or someone's working on it 😄): 1. AI Agents won’t just save time — they’ll make money. AI agents will shift from boosting productivity to generating revenue directly. ⏩️ Example: An agent closes outbound deals, writes term sheets, or wins new clients autonomously. 2. Agents will help phase out legacy systems. Instead of replacing old CRMs or ERPs overnight, agents will quietly absorb and replace them from the outside in. ⏩️ Example: An agent learns your workflow, automates key actions, makes the system obsolete over time, and codes them. 3. Agents can mimic human behavior. New AI agents simulate real personalities and groups — unlocking a new kind of behavioral A/B testing. ⏩️ Example: Test how 1,000 investors might react to your pitch deck before ever sending it. Take a look at the research from Stanford University. Link in the comments. 4. Agents will pay each other. Financially autonomous agents can now manage wallets, pay for APIs, or contract other agents. ⏩️ Example: One agent pays another to complete a task, like gathering market data or translating a deck. Project: Payman Ai 5. AI-native fraud is coming fast. Fake voices, documents, and faces will flood markets — especially in finance, identity, and compliance. ⏩️ Example: A deepfaked CEO voice authorizes a $1M transaction. Detection tools must keep up. 6. AI-native institutions are next. AI VCs already exist - AI banks, PE firms, and hedge funds are on the horizon. ⏩️ Example: An AI agent allocates capital, writes IC memos, and reports to LPs without human input. We are building something fascinating here. But also check out one of the Y Combinator startups I left in the comments. 7. New multimodal AI like GPT-4o changes the game. Agents can now see, hear, and speak - making them more useful in real-world tasks. ⏩️ Example: An agent reads a contract PDF, checks for risks, explains it on a call, and sends a summary. 8. AI agents will be insured. As agents make critical decisions, enterprises will insure them like human employees, but we still need to minimize hallucinations. ⏩️ Example: A credit agent makes a false investment call → insurance covers the loss. ARE WE IN THE FUTURE? #AI

🧠 How to balance big swings and safe bets to build the most powerful AI portfolio for your business "The winners will be those who build diversified portfolios that balance transformational ambitions with incremental improvements, macro visions with micro victories, human wisdom with machine capabilities." THE CONTINUOUS EVOLUTION MODEL Static strategies die in dynamic environments. Your AI portfolio needs built-in adaptation mechanisms: Regular Rebalancing: Quarterly reviews of project mix. Are you maintaining appropriate risk levels? Have new capabilities opened fresh opportunities? Learning Loops: Every experiment feeds strategic understanding. Failed projects often teach more than successful ones. Cultural Evolution: Organizations must embrace perpetual beta. Yesterday’s mindset won’t create tomorrow’s success. FROM THEORY TO PRACTICE A financial services firm might simultaneously pursue: • A moonshot project using AI to predict market movements with unprecedented accuracy • A medium-risk initiative automating compliance reporting • Several low-risk projects improving customer service chatbots Each initiative serves distinct portfolio purposes. The moonshot could transform the business model entirely. Compliance automation delivers clear ROI within 18 months. Chatbot improvements show immediate returns while building AI capabilities. The CEO’s role is to ensure that each initiative receives appropriate resources while maintaining portfolio balance—not picking favorites, but orchestrating the symphony. 📌 Read the full article here: https://lnkd.in/ekffy6A7 #AI #DigitalTransformation #Innovation #Strategy #CEOs #Business

I was readings some exciting insights from Gartner's latest predictions on data and analytics trends that are set to shape our business practices through 2025 and beyond! One of the most striking forecasts is that by 2027, 50% of all business decisions will be augmented or automated by AI agents. This shift underscores the critical need for organizations to leverage AI in conjunction with robust data governance and analytics to make informed, intelligent decisions. Carlie Idoine, Vice President Analyst at Gartner, rightly emphasizes that while AI is a powerful tool, it must be aligned with effective governance and risk management. At EY, we advocate for executive AI literacy as a cornerstone of successful AI adoption. Gartner predicts that organizations prioritizing this will see a 20% improvement in financial performance by 2027. By upskilling our leaders in AI, we can make strategic investments and better manage opportunities and risks. However, as we adopt synthetic data to enhance privacy and expand datasets, we must remain vigilant about the associated risks. Gartner warns that 60% of data and analytics leaders may face critical failures in synthetic data management by 2027. Effective metadata management will be key to navigating these challenges and ensuring AI accuracy. Looking ahead, the rise of generative AI (GenAI) applications is on the horizon, with a prediction that 30% of GenAI pilots will involve bespoke builds by 2028. This trend highlights the importance of flexibility and control in our AI strategies. As we move forward, it’s clear that AI will play an increasingly vital role in the boardroom, with 10% of global boards expected to employ AI advice by 2029. This reinforces the need for robust data governance and clear regulatory policies. I encourage all leaders to stay informed and proactive in embracing these trends. Let’s ensure we harness the power of AI responsibly and strategically to drive our organizations forward! #AI #DataAnalytics #Innovation #Leadership #Gartner #BusinessStrategy #SyntheticData #GenerativeAI .

In preparing for a upcoming keynote speech on #genai and the impact on #work; I found these Insights global study by Google #Cloud and National Research Group some of the best I have seen. As a management consulting leader, I'm struck by the clear imperative for organizations to educate themselves on gen AI today. Here are some key takeaways: 1) 74% of enterprises using gen AI report ROI within the first year - faster than most #software deployments 2) 86% of organizations seeing revenue growth estimate a 6%+ increase in annual revenue (real revenue growth!) 3) 84% can move a gen AI use case from idea to production in under 6 months (once again, speed WINS) 4) 45% of organizations report employee productivity has doubled or more due to gen AI (maybe some technology to make our lives easier!) The message is clear: gen AI is not just another tech trend, but a key driver of business transformation and competitive advantage. The study also reveals a "gen AI #leadership gap" - only 16% of organizations are truly leading in this space. These leaders are seeing outsized gains in revenue, productivity, and innovation. To close this gap, organizations must prioritize gen AI education at all levels. This means: 1) Building unified C-suite support and vision for gen AI initiatives 2) Focusing gen AI efforts on core business functions 3) Investing in AI talent development across the organization 4) Prioritizing data quality and infrastructure to support gen AI It is more clear to me than ever that the time to act is now. Those who invest in understanding and strategically implementing gen #AI today will be best positioned to thrive in the AI-driven future of business. Link to the complete study if interested - https://lnkd.in/gmn-yAwE #GenerativeAI #BusinessStrategy #Innovation #Leadership Mercer Ravin Jesuthasan, CFA, FRSA JESS VON BANK #google Adriana O'Kain Ryan Malkes

🔥 𝐓𝐚𝐫𝐢𝐟𝐟𝐬. 𝐑𝐞𝐜𝐞𝐬𝐬𝐢𝐨𝐧. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 🔥 These were the top three topics in CEO conversations during Q2 2025. According to IoT Analytics' latest report based on 8,000 earnings calls, executive priorities are shifting fast. If you’re leading a company in today’s climate, here’s what should be on your radar: 1️⃣ 𝐓𝐚𝐫𝐢𝐟𝐟𝐬 𝐭𝐚𝐤𝐞 𝐜𝐞𝐧𝐭𝐞𝐫 𝐬𝐭𝐚𝐠𝐞. Mentions of tariffs jumped to 74% of calls, the highest level since tracking began. This sharp rise is reshaping corporate strategies: • Supply chains are being reevaluated, with a renewed focus on reshoring and local-for-local production. • Pricing models are being adjusted in real time. • CFOs are flagging hard costs. Apple's Tim Cook, for example, has warned of a potential $900 million impact from tariffs this quarter alone. 👉 The message: trade policy is no longer background noise. It's driving board-level decisions. 2️⃣ 𝐑𝐞𝐜𝐞𝐬𝐬𝐢𝐨𝐧 𝐜𝐨𝐧𝐜𝐞𝐫𝐧𝐬 𝐠𝐫𝐨𝐰, 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐢𝐧𝐠𝐞𝐧𝐜𝐲 𝐩𝐥𝐚𝐧𝐬 𝐚𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐢𝐧 𝐦𝐨𝐭𝐢𝐨𝐧. Mentions of "recession" rose 343% quarter-over-quarter. Most CEOs aren't predicting a deep downturn, but many are preparing for low or flat growth. To brace for volatility, companies are activating playbooks focused on liquidity, cost control, and operational agility. 👉 This is not panic. It's preemptive resilience. 3️⃣ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐧𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐟𝐫𝐨𝐦 𝐛𝐮𝐳𝐳 𝐭𝐨 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐦𝐩𝐚𝐜𝐭. While mentions of general #AI are down, practical use cases are gaining traction: • Agentic AI refers to systems that can learn, adapt, and make decisions with limited human input. • AI agents are specialized tools that automate specific tasks, often within broader agentic frameworks. Both are gaining ground: • Mentions of #agenticAI rose 39% • Mentions of AI agents rose 41% • Tools like Cursor, Mistral AI, and the Model Context Protocol (MCP) are getting early attention 👉 The focus is shifting from models to workflows. Leaders are investing in automation that delivers measurable ROI, not just innovation theater. ✖️ 𝐖𝐡𝐚𝐭'𝐬 𝐟𝐚𝐝𝐢𝐧𝐠? Sustainability talk continues to decline. ESG, climate, and emissions saw double-digit drops in mentions. These issues haven't disappeared, but they're no longer framing the executive narrative, especially as AI infrastructure itself raises new sustainability questions. 💡 𝐖𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐫𝐚𝐝𝐚𝐫 𝐚𝐬 𝐚 𝐭𝐞𝐜𝐡 𝐂𝐄𝐎? ✅ How exposed is your organization to trade policy shifts, and how quickly can you adapt your supply and pricing? ✅ Are your teams deploying agentic AI in ways that go beyond experimentation? ✅ Do you have the operational muscle to weather economic uncertainty and still build momentum? 👉 𝐓𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐡𝐚𝐥𝐟 𝐨𝐟 𝟐𝟎𝟐𝟓 𝐰𝐢𝐥𝐥 𝐫𝐞𝐰𝐚𝐫𝐝 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐰𝐡𝐨 𝐚𝐜𝐭, 𝐧𝐨𝐭 𝐫𝐞𝐚𝐜𝐭. #TechStrategy #CEOInsights #TechTrends

Spent an hour with the Chief AI Officer at a F500 company yesterday: Very interesting: 1. CEO believes that people-to-people relationships are most important thing in their business. Has mandated a human first approach. 2. That's causing them to spend most of their time on co-pilot products (augmentation) and very little time on agents (in theory, replacement). 3. Mostly in experimentation mode right now. But... "Very few of our experiments will make it into production". Believes these experiments are a false positive for many AI vendors. 4. Huge barriers to adoption around governance, risk, compliance, safety, privacy... Most interesting thing is that assessing these risks is not in Chief AI Officer's purview. It's up to security, legal, etc to figure out. These third party groups that don't have much AI expertise have veto authority over AI decisions. 5. Default option is Microsoft, Google, and to a lesser extent Salesforce because of dynamic number 4. Startups and mid-sized tech vendors are building great products, but aren't worth the effort. 6. Spending a lot of time assessing LLMs and just trying to keep pace with rate of change. Less time on wrappers and products built on top of LLMs (with exception of what MSFT, GOOG offer). 7. Believes LLM vendors could stop development today and it would take company a couple of years just to catch up and adopt what they've already built. It's one conversation, but I do think it's fairly representative of what's happening in the enterprise.