How AI Influences Value Creation

The value of Humans + AI collaboration in the real world: an academic study of 776 R&D professionals at Procter & Gamble revealed not just substantial performance gains from AI, but a host of other gains, including in emotional state. Some of the stand out insights from the research paper (link in comments): 🚀 AI + teams unlock top-tier innovation. Teams using AI were 9.2 percentage points more likely to produce top 10% solutions compared to the 5.8% baseline—making them about three times more likely to generate standout ideas. This effect was not seen for individuals using AI, highlighting a unique benefit in combining AI with human collaboration. ⏱️ AI makes work faster and more detailed. Individuals with AI completed their work 16.4% faster, and teams with AI were 12.7% faster than their non-AI counterparts. At the same time, AI-enabled groups produced significantly longer and more detailed solutions, with higher average quality scores. 🧩 AI dissolves functional silos. Without AI, Commercial and R&D professionals proposed solutions aligned with their functional backgrounds—market-oriented vs. technical. With AI, this gap disappeared: both groups generated more balanced ideas, regardless of their original specialization. This pattern held across individuals and teams. 📈 AI lifts less experienced employees to team-level performance. Employees whose core job did not include product development performed significantly worse in the control conditions. However, when these non-core employees worked with AI, their performance matched that of teams containing core-role employees. 😊 AI improves emotional states during work. Participants using AI reported significantly higher increases in positive emotions—such as excitement, energy, and enthusiasm—and lower increases in negative emotions like anxiety and frustration. Individuals with AI experienced a 0.457 standard deviation increase in positive emotions, and AI-enabled teams saw an even larger 0.635 boost. 🏢 AI challenges traditional assumptions about team structures. The study found that individuals with AI performed as well as human teams without AI, while AI-enabled teams were significantly more likely to produce top-decile solutions. The authors conclude that this challenges long-standing assumptions about the necessity and structure of collaboration. They suggest organizations may need to rethink how they compose teams and allocate expertise in an AI-integrated environment.

Almost every AI value creation plan I see in PE is a cost takeout story. That is understandable -  cost is measurable, controllable, and shows up in EBITDA fast. But it misses the more consequential question for any hold-period thesis: What is AI doing to your target's top line? In industrial and services businesses, AI affects revenue in three distinct ways, and every portfolio company has some mix of all three. The composition determines whether the business is heading into a tailwind or a headwind, and most diligence processes are not equipped to tell the difference. AI-accelerated revenue. Categories where AI makes customers buy more, buy faster, or buy from a wider set of providers. Industrial distribution with AI-driven cross-sell and replenishment is a clean example. The technology expands share of wallet without a proportional increase in sales headcount. Field services companies using AI to shorten quote-to-cash cycles are compressing the sales funnel and winning business they previously could not staff. These are legitimate revenue tailwinds. AI-disintermediated revenue. Categories where AI removes the reason the customer was buying from a human intermediary in the first place. Specification-heavy distribution, basic engineering services, permitting support, certain categories of inspection and compliance work. The question is not whether these businesses can cut cost with AI- they can but whether their end customer will continue to pay the same price for a service that the customer can increasingly perform themselves with an AI tool. Margin compression often shows up here before volume decline does. By the time volume moves, the multiple has already re-rated. AI-neutral revenue. Categories where the buying decision, the delivery method, and the switching costs are structural and AI changes the back office but not the front office. Much of heavy industrial, regulated services, and physical field work sits here. The cost structure moves; the revenue does not. The uncomfortable truth: a target with strong Layer 1 AI cost takeout and an AI-disintermediated revenue base is a value trap. The margin expansion is real and the exit multiple compression is also real, and they cancel out, or worse. A diligence team that decomposes revenue into these three buckets before signing an LOI will catch this. A team that treats AI as a pure cost-side conversation will not.

AI amplifies what’s already there. 🔍 If strategy is clear, AI accelerates execution. If it’s not, AI accelerates confusion. The pattern I see most often: organizations treating AI as a technology implementation challenge when it’s actually a strategy translation problem. Teams deploy tools before defining outcomes. Pilots proliferate without connection to value creation. Energy diffuses across initiatives that don’t compound. BCG’s research on AI transformation highlights what separates organizations realizing value from those still running experiments: 𝗧𝗵𝗲𝘆 𝘀𝗲𝘁 𝘁𝗮𝗿𝗴𝗲𝘁𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀. High-performing organizations define the business outcome first, revenue growth, margin expansion, customer retention, then work backward to determine where AI creates leverage. 𝗧𝗵𝗲𝘆 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗿𝘂𝘁𝗵𝗹𝗲𝘀𝘀𝗹𝘆. Not every process deserves AI investment. The best applications start where repeatability is high, data infrastructure is solid, and the economic payoff is measurable. 𝗧𝗵𝗲𝘆 𝘁𝗿𝗲𝗮𝘁 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗮𝘀 𝗮 𝘃𝗮𝗹𝘂𝗲 𝗹𝗲𝘃𝗲𝗿. Skills, incentives, and ways of working are deliberately redesigned so AI changes how work actually gets done, not just what tools are available. The pattern is straightforward. AI amplifies whatever is already true about an organization. Clarity becomes leverage. Ambiguity becomes drag. That is why the mandate is shifting. AI transformation is no longer about choosing the right tools. It is about setting direction and managing performance with the same rigor applied to any other driver of enterprise value. Read the full article: https://lnkd.in/gjduA7rK

One question keeps coming up in my discussions with clients and teams around the world: If AI is so powerful, and the advances so impressive, why are so few organizations seeing meaningful return on their AI investments? This week, PwC shares our latest research - Decoding ROI from AI - which addresses that question head on. A handful of findings stood out: First, there is tremendous value being created by AI, but that value is not evenly distributed. Nearly three quarters of the economic gains are being captured by just 20% of the organizations. And those leading organizations are delivering more than seven times (7X!!) the AI-driven performance of their peers. Second, the biggest returns are not coming from efficiency alone. The organizations pulling ahead are aiming AI at their growth agenda - using it to unlock new revenue, rethink their business models and launch new businesses as industry boundaries continue to blur. Third, strong outcomes are built on strong foundations. The basics still matter. The leading organizations are not trying to do everything at once, they are investing deliberately and targeting the capabilities that matter: data, governance and resilient scaling. And the last point is one we've talked about here many times, but with this data we move this discussion point from the anecdotal to the empirical. AI creates value when it is embedded in how the business operates, not just pointed toward single initiatives or functions. The leading organizations reported integration into workflows, decision-making and day-to-day execution, turning capabilities into consistent (and scalable) impact. Put it all together and this is what we describe as "AI Fitness" - the ability to focus on what matters, build strong foundations and scale what works. As usual, it comes down to leadership. Leaders must drive clear choices in where to focus, what to build and how to embed AI not into tasks, roles or functions - but to reimagine how the business operates across functions and value chains. My friend and colleague Matt Wood brings it all to life in the linked film – well worth a watch and share!  #ROIWithAI  #PwC https://pwc.to/3PHueKK

AI has made execution faster than ever. What it has not solved is decision-making. Many tasks that once required weeks of planning, coordination, and specialized expertise can now be completed in a fraction of the time. Content gets created faster, campaigns launch sooner, and production is no longer the primary constraint. The challenge has shifted. When generating ideas, assets, and campaigns becomes easier, the real question is no longer how much can be produced. It is what deserves attention in the first place. This is where judgment becomes more valuable than execution. Choosing the right direction, prioritizing the right opportunities, and knowing what to ignore are increasingly important skills in an environment where everyone has access to similar tools. More output does not automatically create more impact. Without clear decision-making, organizations risk scaling activity rather than results. The teams creating the most value today are not necessarily producing the most content or launching the most campaigns. They are making better decisions about where to focus and why. This week’s newsletter explores why expertise is becoming more accessible, why judgment is becoming more important, and how teams can create meaningful value in an AI-driven environment. For organizations rethinking their competitive advantage, it is worth a read.

Is AI flattening organizations? Not because it removes managers. But because everyone becomes one. AI gives everyone their own workforce. ▪️ Before AI, organizations scaled by adding team members. ▪️ After AI, organizations scale by improving judgment. AI reveals that management was never about hierarchy. Management is about creating value through: ▪️ Prioritization ▪️ Delegation ▪️ Coaching ▪️ Quality control The implication is profound. ▪️ Human value shifts from: Doing → Directing ▪️ Or perhaps more accurately: Operating → Judging Human judgment is continually shifted as AI continuously pushes humans toward higher-order forms of judgment. ▪️ AI generates the options. ▪️ Humans decide which option creates value. Human potential is not replaced. Human judgment is continuously relocated. PS: Imagine the impact on value creation. ▪️ Before AI: 100 employees × 1 value opportunity explored/month = 100 ▪️ After AI: 100 employees × 10 value opportunities explored/month = 1,000

AI does not make strategy smarter by itself. It can only accelerate the quality, or the weakness, of the decisions leaders are already prepared to govern. When AI influences strategy, the executive role becomes more important. Leaders need to make sure data is reliable, models are challenged, and business context is not lost behind technical outputs. AI insights should be connected to real priorities, so teams understand why a decision is made and how it supports execution. Responsibility must also stay visible. When AI supports strategic choices, ownership cannot become vague or hidden inside systems. Good strategy depends on adapting as data and context change, while monitoring outcomes and keeping accountability active over time. AI creates value when executives turn data-driven insights into decisions that people can understand, execute, and own. #AI #Leadership #DecisionIntelligence

>> The AI Bet That Could Change Europe Forever Dr. Patrick Simon, former Senior Partner at McKinsey & Company Most AI conversations focus on tools, models, and productivity. Dr. Patrick Simon focuses on something else entirely: Who captures the value. In this episode of DIGITAL STORM weekly, we sat down with Dr. Patrick Simon, former Senior Partner at McKinsey & Company and now a technology investor, to discuss how AI is reshaping corporate strategy, competitive advantage, productivity, and global economic power. This conversation goes far beyond AI implementation. It explores how leadership, organizational culture, incentives, and strategic positioning will determine which companies thrive and which fall behind in the AI era. Some key insights from Patrick: • AI is changing value chains, not just business processes • Strategy increasingly starts with understanding what AI makes possible • Leadership courage matters more than perfect data • AI-driven productivity gains can create enormous enterprise value • Culture, incentives, and execution matter more than vision alone • Europe risks falling behind due to excessive regulation and technology dependence • Companies must play to win, not simply protect what they already have • The biggest AI opportunity is creating competitive advantage, not just efficiency What makes Patrick’s perspective valuable? He spent over two decades advising CEOs and boards at McKinsey & Company on large-scale transformations before moving into venture investing, where he now evaluates emerging technology companies and AI-driven business models firsthand. If you are an executive trying to understand how AI is changing competition and value creation, this episode is worth your time. 🎧 𝗟𝗶𝘀𝘁𝗲𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗵𝗲𝗿𝗲 🔗 YouTube: https://lnkd.in/ehcpJQqg  🔗 Amazon Music: https://lnkd.in/daTPRypn  🔗 Apple Podcasts: https://lnkd.in/dphbS89P  🔗 Spotify: https://lnkd.in/dQ9gjG2E  🔗 RSS: https://lnkd.in/d4tCXB7n --- ♻️ Repost to share with your network ➕ Follow me Paul Storm for cutting-edge AI insights Join 600,000+ professionals using AI to stay ahead: 🔗 https://lnkd.in/dh_sCAzw

66% of organizations report productivity gains from AI. 74% want AI to drive revenue growth. 20% are actually seeing revenue growth. That gap represents a strategic tension most organizations haven't made explicit yet. The tension: Optimize for productivity metrics, or redesign for value creation? Most AI business cases focus on cost reduction and efficiency. That's rational. Productivity gains are measurable, achievable, and lower risk. But here's the strategic question: If everyone in your industry captures similar productivity gains, does that create competitive advantage or just reset the baseline? Productivity improvements compress margins industry-wide. You need them to stay competitive, but they don't differentiate you. Revenue growth requires doing something your competitors can't. New capabilities, new offerings, new customer experiences. The 20% seeing revenue growth aren't asking "How can AI make us more efficient?" They're asking "What can we deliver that creates new value?" The measurement challenge: We're evaluating AI using productivity metrics designed for process improvement. Time saved. Tickets closed. Reports generated. Costs reduced. But those metrics don't capture faster time-to-market for new products. Customer experiences that increase lifetime value. Market positions that weren't previously accessible. Strategic optionality in uncertain markets. The investment allocation question: Most AI budgets are weighted toward operational efficiency use cases. They have clear ROI and lower implementation risk. But what percentage should be allocated toward exploratory use cases that might create new revenue streams or competitive moats? That's a portfolio management decision. Balancing near-term productivity gains against longer-term strategic positioning. The competitive framing: Your investors are comparing your AI productivity gains to peer benchmarks. But they're also watching for which companies are using AI to create new competitive positions. The organizations that do both (capture efficiency gains and explore new value creation) will have more strategic options when market conditions shift. Are your AI success metrics measuring productivity improvement or business transformation? Because the metrics you choose will drive the outcomes you get. If you optimize for productivity, you'll get efficiency. If you optimize for value creation, you might get transformation. Both matter. The question is: What's the right balance for your competitive context? Read the previous posts in this series: https://lnkd.in/gs9XQ-kx https://lnkd.in/gtgPUyTT https://lnkd.in/guSBUVyk https://lnkd.in/gtgPUyTT https://lnkd.in/gJa-Xkru #StateOfAI2026 report: https://lnkd.in/gh7ATzKD Deloitte AI Institute - https://lnkd.in/gdseWaiR

𝗛𝗼𝘄 𝘁𝗼 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗩𝗮𝗹𝘂𝗲 𝗳𝗼𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀  AI adoption in B2B software is moving quickly, but adoption alone doesn’t build customer confidence. What matters is whether AI can reliably improve how customers manage risk, protect data, and operate more efficiently. For many organizations, while enthusiasm around AI is increasing, there is still palpable hesitation about accuracy, governance, and control. That trust gap is where technology providers have an opportunity to lead. AI is most meaningful when it strengthens customer relationships by making outcomes predictable and governance easier to enforce. When customers can clearly see how AI empowers them to do more while also safeguarding critical content, trust follows naturally. Here are five principles that tend to be important when applying AI in ways customers actually value: 1. Use AI to make customer outcomes more predictable AI is most impactful when it reduces uncertainty. Automatically classifying sensitive content, querying complex documents for instant answers, or improving policy enforcement accuracy are just a few areas that could deliver immediate, tangible impact. When customers can depend on consistent outcomes, adoption follows naturally. 2. Apply AI where complexity already exists AI delivers the most value in environments with high content volume, regulatory pressure, or distributed teams. In these scenarios, AI helps customers manage risk and scale operations without increasing overhead. 3. Communicate AI in the language of risk, productivity, and governance Different stakeholders evaluate AI differently. Security leaders care about data exposure risks, whereas IT teams prioritize control and visibility. AI messaging should reflect those realities instead of focusing on obscure problem-solving capabilities. 4. Build AI enablement into the product experience Documentation doesn’t drive adoption, context does. In-product guidance, intelligent recommendations, and workflow-level assistance help customers understand how AI works, and, more importantly, when to trust it. The faster customers see value, the faster confidence grows. 5. Treat customers as participants in AI evolution AI systems improve through real-world usage and feedback. Creating structured ways for customers to validate outputs and influence roadmap decisions strengthens both performance and long-term trust. AI-driven value ultimately comes down to confidence. Before making financial commitments, customers need to trust that AI is improving how their content is used to drive real business outcomes. As software developers continue to integrate more AI into their solutions, customers are entering an unprecedented era. However, to set them up for success, it's important that solution providers deeply understand their workflows and chart a customized path to ensure output optimization.