How AI Will Affect Future Profits

💡 New preliminary but promising research provides what appears to be the first causal evidence that GenAI doesn't just boost productivity—it directly increases firm profits. 💡 A large-scale field set of experiments involving millions of users at a major e-commerce platform found that GenAI enhancements to business workflows increased sales by up to 16.3%, with the largest improvements in customer service applications. Across four workflows with positive effects, researchers calculated an annual incremental value of approximately $5 per consumer. 💡 What makes this study particularly valuable is that it shows GenAI can increase actual sales and revenue, not just help employees work faster. The productivity improvements came through enhanced consumer experience—specifically higher conversion rates—demonstrating that GenAI can create value by reducing marketplace frictions. 💡 The benefits weren't evenly distributed: smaller sellers, less experienced consumers, and tail products derived disproportionately larger gains, suggesting GenAI's potential to bridge capability gaps across marketplace segments. 💡 This research addresses a critical question many executives are asking: does investment in GenAI actually translate to measurable business outcomes? The evidence suggests the answer is already yes. Link to the paper: https://lnkd.in/eFXWcJHg #AI #Productivity 

AI is becoming a make-or-break factor for banks. But success will not depend on their ability to offer #AI, but on their competence in integrating it. Let’s take a look.   Banking is forecasted to feel the biggest impact from generative AI among sectors and industries as a percentage of their revenues with the additional value calculated between $200 bn and $340 bn annually (source: McKinsey). But why is the impact so powerful? One of the main reasons is because the abrupt surge of gen AI is exponentially increasing the speed with which #banking is being transformed. That is not to say that the transformation has started with or due to AI. On the contrary: during the past 10 to 15 years banking was already in the middle of transforming from a human-based, relationship-first industry to a more automated and technology-driven business following the #fintech revolution and the ascend of nimbler and more innovative competitors. But AI now does 2 things: —  It brings the transition to a new level, across 3 dimensions: speed, outcome and impact. —  It turbo-charges one of the biggest challenges in modern FS: the combination of AI and data that brings under the same roof two inherently opposing forces: mass and customization. In other words, AI seems to find a credible answer to achieving hyper-personalization. In a recent report Deloitte has provided realistic examples on how this is done across both cost efficiency and income growth: Cost efficiency: —  Workforce acceleration efficiencies across the board: 0–15% of total staff cost —  IT development and maintenance acceleration: 10–20% of IT staff cost —  Improved credit-risk assessment leading to 10-15% savings in impairment charges —  Improved FinCrime/fraud detection reducing litigation/redress charges and fraud losses Income growth: —  Next generation market analysis / predictive trading algorithms: 5–7% uplift on trading income —  Improved customer retention: 1–2% uplift on fees & commissions —  Improved customer acquisition through hyper-personalised marketing: 5-10% uplift from interest income and fees & commissions —  Tailored loan pricing based on credit risk assessment: 2–3% increase on net interest income Despite all the excitement around these estimated benefits, success will not be a walk in the park. It will depend on the banks’ ability to integrate AI in a seamless way into their day-to-day operations. Going forward AI will be re-writing much of the scenarios and use cases of the banking value chain. That doesn’t necessarily mean that they will all be different, but most will certainly be enhanced with impact spanning both across the back-end and the front-end. Given that resources are limited, one of the main challenges will be how to identify the ones to focus on. Factors such as #strategy, potential impact and a match with the existing skillset should be guiding the selection process.   Opinions: my own, Graphic source and use cases: Deloitte

AI’s Structural Impact on Enterprise Value and Credit Worthiness AI is in the process of creating a paradigm shift that will redefine the business model for industry sectors throughout our economy. While many companies will adapt, become more efficient, enjoy revenue growth and reduce their cost structure, others will experience the complete opposite impact. The evolving development and adoption of AI creates a profound transformational and fundamental reconfiguration that will transform how industry operates. Investment managers must also adapt as AI changes the equation for traditional underwriting assumptions that is core to determining a borrower’s future market position, competitive edge, cash flow, earnings and growth; the very foundations for value analysis. In the past, a downturn was often impacted by a cyclical decline associated with a slowing economy, recession or tightening of liquidity conditions that led to dislocation or distress. A structural change, however, is not a temporary or passing condition, but rather a long-term, irreversible development that renders old models obsolete. AI requires asset managers, capital allocators, and corporate executives to evaluate this new risk factor that has previously not been considered. Healthcare, Business Services, Software, and Manufacturing companies will all be impacted by AI with the potential to lower marginal costs allowing for greater scale and stronger unit economics. Upfront R&D and CapEx will be required, so the cost for advancement is not free, yet those caught flat-footed may see their cost per unit become uncompetitive vs. a highly efficient first-mover competitor. Take an enterprise software company for instance where the incumbent who fails to integrate AI sees their business deteriorate with the struggle of a less dynamic operating system, higher churn/deteriorating customer retention, compressed margins and eroded competitiveness. An AI-first software company that offers superior and more efficient products will take ARR from the incumbent. As a Private Credit lender, committing to 5–7-year loans is an eternity as AI alters the value proposition. Capital Solutions providers will be busy as companies adjust to this new dynamic. Stress-testing businesses to model this AI paradigm shift, assessing which companies are insulated from AI, or positioned to leverage AI to drive EBITDA or create a defensible moat represents the questions and quandary one must determine. When evaluating an investment, it’s imperative to understand your downside case associated with a cyclical decline that is stressed for recession or a banking crisis. It is critical that these scenarios are underwritten by experienced investment management teams that have lived through these transitional periods, but we now have one more risk factor that is important to understand and underwrite. Marathon Asset Management's Investment Committee and investment team closely considers AI risks when investing. 

Tech companies will spend $400 billion on AI infrastructure in 2025—exceeding the Apollo program's inflation-adjusted budget, repeated every ten months. Both bubble skeptics and AI bulls present compelling evidence, leaving business leaders caught between FOMO and prudent risk management. Unlike dot-com startups burning venture capital, today's AI leaders (Microsoft, Google, Amazon) are massively profitable and will survive even if AI bets fail. The technology demonstrably works for specific tasks. Infrastructure has alternative uses if foundation model companies collapse. Unit economics worsen with scale rather than improve. Financial engineering obscures true profitability (Microsoft-OpenAI circular revenue bookings mirror WorldCom-era accounting). MIT studies show 95% of AI pilots fail to yield meaningful results. The gap between infrastructure spending ($400 billion) and consumer revenue ($12 billion annually) echoes telecom overcapacity that left 85-95% of fiber "dark" in 2002. Don't bet on timing the bubble—build for multiple scenarios. Prioritize AI applications with 12-month ROI that work whether vendors consolidate or not. Rent compute from hyperscalers rather than building proprietary infrastructure. Develop internal expertise that survives vendor failures. Prepare to acquire distressed assets (GPUs, talent, data centers) at 2027 fire-sale prices if correction arrives. Remember Amara's Law: We overestimate short-term impact, underestimate long-term transformation. The internet crashed in 2000 but enabled Amazon, Google, and Facebook by 2005. Position to benefit from both timelines. The bubble thesis is probably correct for 2026-2027. That doesn't make AI investments wrong—it makes vendor selection, contract structure, and capability building more critical than ever. Companies that survive bubbles distinguish hype from utility, build competency during uncertainty, and stay capitalized to buy when others must sell. Investors who know what’s coming can avoid misfortune.

AI is one of the strongest revenue multipliers in finance and high-growth companies, and 2024 had the data to prove it. Here's a look at how AI is a direct revenue-growth engine. Finance Revenue Growth From AI In 2024 ---- Here’s what actually happened: • 30% of financial services firms gained 5–10% more revenue from AI • 16% saw 10–20% revenue growth • 23% grew revenue by 20%+ from AI adoption • AI personalization → 20% average sales increase, with top performers hitting 40% more revenue than competitors • AI automation → massive productivity + meaningful revenue gains Personalization Was The Real Revenue Engine • AI-based personalization drove more revenue than automation • Market size for AI personalization engines hit $455.4B in 2024 • AI automation software? $86B — important, but smaller impact on sales lift • Companies using personalization tools are out-closing, out-retaining, and out-earning everyone else Real-World 2024 Revenue Wins • Gong Revenue AI users generated 77% more revenue per rep • Companies like DocuSign and PayPal saw 57% higher win rates • Accenture grew GenAI services revenue by 390% (clients using AI saw 2.5× higher revenue growth) • NVIDIA hit $115B in revenue, fueled by demand for AI infrastructure • Companies that implemented AI saw positive ROI in 90 days Looking Ahead ----- This is where it gets serious: • Businesses with AI integrated into core operations are on track to see 38% higher profitability in 2025 • AI-mature companies expect 60% more AI-driven revenue by 2027 AI is no longer just a cost-saving efficiency tool. It is now: - A revenue strategy - A sales advantage - A profitability engine - A market differentiator If AI isn’t integrated into your revenue model, customer experience, forecasting, and financial workflows, you're missing opportunity for growth. _________ Finance Ops Consultant, Team Training & Advisory Services Follow Beverly Davis for Strategic Finance Insights

McKinsey & Company 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝟱𝟬+ 𝗽𝗮𝗴𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗔𝗜, 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗼𝘂𝗹𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 $𝟮.𝟵 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗯𝘆 𝟮𝟬𝟯𝟬. There is no doubt that work in the future will not be human-only or machine-only, but a coordinated system of people, agents, and robots operating together. This matters because the structure of work - not the number of jobs - is what will shift first, as core skills are redeployed and new forms of human-machine collaboration become central to productivity. 𝗔𝘁 𝗮 𝗴𝗹𝗮𝗻𝗰𝗲, 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗳𝗶𝘃𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘀𝗵𝗶𝗳𝘁𝘀: ↓ 1 - 𝗪𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗿𝗼𝗯𝗼𝘁𝘀 - 𝗮𝗹𝗹 𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗯𝘆 𝗔𝗜: ➜ McKinsey notes that today’s technologies could theoretically automate more than half of current US work hours. This shows how profoundly work may change, but it is not a forecast of job losses. Adoption will take time. As it progresses, some roles will shrink, others will grow or shift, and new ones will emerge - with work increasingly centered on collaboration between humans and intelligent machines. 2 - 𝗠𝗼𝘀𝘁 𝗵𝘂𝗺𝗮𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝗹𝗹 𝗲𝗻𝗱𝘂𝗿𝗲, 𝘁𝗵𝗼𝘂𝗴𝗵 𝘁𝗵𝗲𝘆 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗽𝗽𝗹𝗶𝗲𝗱 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆: ➜ The reports stats that more than 70 percent of the skills sought by employers today appear in both automatable and non-automatable work. Most skills remain relevant, but how and where they are used will evolve. 3 - 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆’𝘀 𝗻𝗲𝘄 𝗦𝗸𝗶𝗹𝗹 𝗖𝗵𝗮𝗻𝗴𝗲 𝗜𝗻𝗱𝗲𝘅 𝘀𝗵𝗼𝘄𝘀 𝘄𝗵𝗶𝗰𝗵 𝘀𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗺𝗼𝘀𝘁 𝗮𝗻𝗱 𝗹𝗲𝗮𝘀𝘁 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝘁𝗼 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗶𝘃𝗲 𝘆𝗲𝗮𝗿𝘀: ➜ Digital and information-processing skills could be the most affected, while those related to assisting and caring are likely to change the least. 4 - 𝗗𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗔𝗜 𝗳𝗹𝘂𝗲𝗻𝗰𝘆 - 𝘁𝗵𝗲 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝘂𝘀𝗲 𝗮𝗻𝗱 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 - 𝗵𝗮𝘀 𝗴𝗿𝗼𝘄𝗻 𝘀𝗲𝘃𝗲𝗻𝗳𝗼𝗹𝗱 𝗶𝗻 𝘁𝘄𝗼 𝘆𝗲𝗮𝗿𝘀: ➜ According to McKinsey, this growth is faster than for any other skill in US job postings. It is visible across industries and likely marks the beginning of much bigger changes ahead. 5 - 𝗕𝘆 𝟮𝟬𝟯𝟬, 𝗮𝗯𝗼𝘂𝘁 $𝟮.𝟵 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗼𝗳 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝘃𝗮𝗹𝘂𝗲 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝘂𝗻𝗹𝗼𝗰𝗸𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗨𝗻𝗶𝘁𝗲𝗱 𝗦𝘁𝗮𝘁𝗲𝘀:  ➜ If organizations prepare their people and redesign workflows - rather than individual tasks - around people, agents, and robots working together. More in the comments and report below! ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗱𝗲𝗲𝗽𝗲𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝗻 𝗔𝗜, 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝘆𝗼𝘂’𝗹𝗹 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲𝗺 𝗶𝗻 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗛𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽: https://lnkd.in/dbf74Y9E

AI shouldn’t just save you time. It should increase your profit margins. Most teams use AI to automate busywork. The best teams use it to increase throughput, reduce overhead, and make better decisions... fast. I broke down 5 real AI use cases that drive profit, not just productivity: 1. Proposal generation with AI 🧾 2. AI-powered customer support 💬 3. Automated data entry & reporting 📊 4. Lead scoring & follow-up 🔁 5. AI forecasting & decision support 📈 These aren’t “shiny object” hacks. They’re profit levers you can stack to create serious compounding effects inside your business. 🔖 Save this post to revisit these use cases. ♻️ Repost if you found this valuable. Let’s spread the word about AI’s real impact.

Consumer product companies are behind the curve on AI maturity, with only 15% achieving real impact vs. 26% of companies globally, according to a Boston Consulting Group (BCG) report. Consumers and retailers are already there: Agentic AI is changing how consumers discover, evaluate and buy products, and retailers are investing in AI for merchandising, procurement, pricing and promotion, so AI is no longer optional for consumer products companies and is already creating new opportunities for those that are leaning in: - In the short-term, 90% of the value of AI is expected to come from reshaping processes and workflows, and longer-term value from the creation of new, core businesses. AI-first CP companies are 2x faster in getting from insight to market, more relevant, more innovative, more resilient, and are leaner. - AI is reshaping P&L’s by 500-800 bps, which can be reinvested in brand/ consumer access and in technology, as productivity increases: Gross revenue will increase as marketing/content gets more personalized and targeted, and predictive pricing and promotion help reduce trade discounts. Costs will decrease with AI-enabled demand planning, supply chain optimization, inventory management, and product cost negotiation; significantly lower labor costs; and lower advertising/content costs, among others.    - It will take 30% fewer people to get the same output. Organizations will be flatter, leaner, and less siloed/more integrated, resulting in faster decision-making. While enterprise-wide AI platforms and ecosystems may be maintained by IT, business units will have more autonomy to deploy and own AI solutions. - AI is changing the way consumers shop: Agents will take over the research that consumer do now, reviewing, comparing, and recommending options based on consumers’ preferences and value.  AI is already helping companies innovate and market by predicting consumer trends; using ROI predictions to improve marketing and sales strategies; creating and personalizing content and optimizing targeting; and monitoring, optimizing and evaluating campaign performance. BGC recommends the following foundations required to scale AI in consumer product companies: 1. Be clear about priorities: What are you solving for, where do you need to strengthen for competitive advantage, and which consumer/customer trends and behaviors need to be addressed? Identify some game changers and quick early wins. 2. Align focus and resources behind some AI game changers. Pilot and monitor them, while leaving some room for bottom-up experimentation. 3. Stay flexible to work with multiple tech partners 4. Set up an AI delivery office, connected to Finance and Transformation. 5. Plan changes to the organization—the design, the talent strategy, etc.—as AI begins to scale. 6. Drive cultural change by having leadership model new behaviors and mindsets, and upskill the whole organization. #AI #consumerproducts #transformation #changemanagement