McKinsey's Tech Trends 2025: AI at the Core

🌱 Can AI drive both profit and planet-friendly progress? The answer is YES—and it’s happening now. In our latest article, we explore how AI is becoming a powerful enabler of sustainability, helping businesses reduce waste, optimise energy, and future-proof operations. From smart supply chains to predictive maintenance, AI isn’t just about automation—it’s about building smarter, greener enterprises. If you're a business leader or tech enthusiast looking to align with the future, this is a must-read. 👉 Dive into the article: https://lnkd.in/gPbFJvNc #SustainableTech #AIforBusiness

The AI revolution is creating unprecedented changes in business operations, distinguishing itself from previous technology cycles through its fundamental impact on organizational efficiency and competitive positioning. LACV portfolio company Sailes is among the big names mentioned by Forbes as a company that's solving real problems around ROI with their proprietary technologies. Companies that recognize this transformation as essential rather than optional are positioning themselves for sustained market leadership. Read more: https://bit.ly/3V5sHNO

🤖 𝐁𝐫𝐞𝐚𝐤𝐢𝐧𝐠 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐆𝐞𝐧𝐀𝐈 𝐃𝐢𝐯𝐢𝐝𝐞 In today's world of ceaseless hype around artificial intelligence, a stark reality emerges: a recent landmark MIT study, The GenAI Divide: State of AI in Business 2025, uncovers that a staggering 95% of generative AI pilot projects in enterprises yield no measurable return on investment—despite billions poured into the effort. This finding underscores a crucial truth: success in AI doesn’t stem from the power of the models themselves, but from the ecosystem in which they’re deployed. Leaders across industries are grappling with this gap—what separates the handful who succeed from the overwhelming majority who stumble? The relevance of this issue couldn’t be greater. Businesses worldwide are wagering on AI to enhance productivity, automate processes, and transform operations. Yet, enthusiasm risks outstripping outcomes. Without the right strategies in place, AI may become less a catalyst for transformation and more a cautionary tale of misplaced optimism. The study reviewed over 300 AI deployments, conducted 150 leadership interviews, and surveyed 350 employees. The overwhelming consensus: integration missteps—not flaws in AI—are to blame. Generic tools deployed without alignment to business processes fail to deliver impact. A lack of adaptation, poor workflow integration, and misallocated budgets compound the issue, especially in large corporations distributing funds thinly across many initiatives. In contrast, agile startups allocate narrow, goal-focused resources to high-impact tasks like coding or ad creation, seeing rapid, measurable gains. For most enterprises, ambition outpaced execution. Pilots often remain in flux, failing to embed into the core of operations. Tools that don’t retain context, learn from feedback, or signal uncertainty are abandoned—especially when employees must constantly verify outputs. Startups thrive by narrowing their scope and solving discrete problems; big companies falter by chasing headlines instead of outcomes. From a broader lens, this phenomenon mirrors past technological trends where revolutionary innovation initially yielded limited impact. The parallels of the “productivity paradox” are clear: breakthroughs take time to mature, and gains require organizational readiness as much as technical ability. In short, the challenge enterprises face isn’t a failure of technology—it’s a failure of adaptation. The 95% statistic is not a harbinger of doom, but a directive: for AI to deliver its promise, leaders must prioritize thoughtful integration, foster adaptive systems, and focus investments where real efficiencies emerge. Now is the time for business leaders to ask not whether they can deploy AI, but how they can align it with strategy, culture, and workflows to bridge the GenAI Divide—and be among the 5% rewriting what success looks like. ... 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 "𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 & 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩" (Link below)

I very much agree that innovation is a journey. But we totally see big tech and the start-up world treating it as a race. I'm glad to see the adoption hype finally cooling in big companies. The underlying challenges cited in the article are not new and must be addressed first before trying to fully leverage AI. For example, lack of reliability of data = garbage in and hallucinations out. #accelerateinnovation https://lnkd.in/gKmjbw5b

[High-Impact Introduction] “What if a colleague could think, act, and anticipate the next move… without being human?” Agentic AI is here. It’s no longer just about automation—it’s about digital entities that can operate as virtual coworkers: autonomous, multimodal, and genuinely proactive. ⸻ [Critical Analysis & Strategic Insights] 1. Agentic AI as the frontier trend McKinsey highlights agentic AI as one of the top frontier technologies of 2025: foundation models that not only understand and generate but can autonomously act across complex workflows—true coworkers in digital form (McKinsey, 2025). 2. AI as a force multiplier The impact of AI is not isolated. It amplifies other innovations—from advanced semiconductors to robotics to sustainable energy (McKinsey, 2025). 3. Efficiency, speed, scale PwC estimates that AI could cut product development cycles in half and dramatically increase operational efficiency. In industries like aerospace, automotive, and pharma, this will radically reshape time-to-market (PwC, 2025). 4. Agentic AI meets sustainability Beyond productivity, AI can serve ESG agendas: automating data collection, enabling smarter energy decisions, and helping organizations meet sustainability targets (McKinsey, 2025). 5. The human factor remains irreplaceable Even as automation advances, roles in HR, leadership, and consulting underscore the enduring value of emotional intelligence, cultural nuance, and strategic judgment (FT, 2025). ⸻ [Predictive Outlook] • The next 12–24 months will be decisive: expect agentic AI adoption to scale rapidly across R&D, operations, and customer experience. • New roles will emerge: “AI orchestrators” and “AI trust & governance managers” will ensure reliability, compliance, and integration. • A competitive divide will widen: early adopters embedding agentic AI with strong governance will gain lasting advantages that late movers will struggle to close. ⸻ [Positioning Statement] Investing in agentic AI is not a tactical choice—it’s a strategic imperative. We’re moving from tools to operational partners. Companies that can harness this new generation of AI, while keeping human intelligence at the center, will build resilience and sustainable competitive edge. ⸻ [Strategic Conclusion + Call to Action] In short: Agentic AI represents the convergence of autonomy, computational depth, and operational foresight. This is not a future possibility—it’s a present reality. The question isn’t if but how fast you’ll adapt. Which business process would you trust first to a “virtual coworker”? Share your perspective in the comments—let’s shape the conversation together.

Research on successful AI cost-savings programs; of the three success factors listed, one hits most hard: "They reshape functions and processes from the ground up." Said another way, they start with an identified, aligned business goal, and they start with a "blank marker board" mindset about how achieve the desired outcome. No reproducing or papering over of existing people, skills, roles and processes...starting with the goal in mind and constructing an ideal way to get there. That's the easy part; the hard part is articulating why change needs to happen and developing and executing a transition/evolution of people, process and tech that maintains continuity and provides acceleration to your business. This part is where nearly every (insert: AI, digital, Web 3.0) transformation - big or small - break down. But they don't have to...if leaders are open to learning about how adoption and change can be measured, supported, coached, and incorporated into the DNA of their organization, real and sustainable results will follow. Curious to see what others think or what experiences you've had that might resonate>

The powerful trio of agentic AI, generative AI, and skilled human resources will transform businesses and impact how they operate, innovate, and thrive. https://hubs.li/Q03yCZDL0 Written by @Deborah Collier digital ski of Digital Skills Authority

AI Governance can be an Acceleration Platform?! I’ve seen brilliant AI initiatives collapse, and not because the tech failed, but because governance became the bottleneck. Every new GenAI project runs into the same wall: - Manual compliance checks slowing progress. - Disconnected processes that create risk gaps. - Teams reinventing controls instead of building solutions. Than momentum dies, ideas stall, and AI’s value remains locked away. The fix isn’t more rules. It’s smarter governance: Centralized. Automated. Integrated. On a unified platform, compliance becomes an enabler: - Provisioning drops from weeks to hours. - Guardrails are built-in, so innovation flows safely. - Modular infrastructure reduces rework and cost, paving the way for secure scale. And we’re already seeing this in action: - JPMorgan Chase uses GenAI with strong governance to stress-test risk and spot new moves in real time. - Unilever runs AI-powered scenario planning and supply-chain tweaks, with compliance baked into the workflows. - Accenture’s “AI Front-Runners” report highlights that only 8% of companies scale AI effectively, and those that do succeed because governance, data, and business strategy are aligned. - Anthropic spin-outs and infra startups like Modular & Weaviate are proving governance can be engineered directly into the stack by designing monitoring, evaluation, and security into agent orchestration from day one. - Healthcare startups at AI World’s Fair showcased “telemedicine agents” that could only scale because governance and compliance were automated, not manual. This is the balance leaders should aim for: autonomy with protection. Governance not as a roadblock, but as a competitive edge.

https://lnkd.in/gCP5WFCA 95% of generative AI pilots are failing. Not because the technology isn’t powerful. Not because leaders don’t see the opportunity. But because most organizations are still relying on the same change playbooks that were already outdated a decade ago. Risk logs. Readiness assessments. Stakeholder matrices. Artifacts that look neat on paper, but rarely change the real behaviors that either drive performance…or block it. Here’s the truth: hope is not a process. If you don’t know where your culture is today, the future will always feel like a mystery. And mystery doesn’t build momentum—it stalls it. The companies that will thrive in the age of AI will be those that: - Stop confusing artifacts with outcomes - Identify the hidden behaviors that enable or block change - Build a culture that adapts at the speed of disruption Because if you do what you’ve always done…you’ll get what you’ve always gotten. And in this environment, that means falling behind fast. What do you think? Has OCM become too focused on producing deliverables instead of driving sustainable change?