AI's Impact on Business

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

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗯𝗿𝗶𝗻𝗴𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘀𝗵𝗶𝗳𝘁 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗱𝗲𝗰𝗮𝗱𝗲𝘀 — 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝘆: ⬇️ At IBM, we just released a new report showing how agentic AI is hitting core business functions, such as Finance, HR, Procurement, Order-to-Cash, Customer Service, and Sales Support. AI-Agent will create a completely new operating model within companies: They will across global workflows execute, escalate, and optimize. Here are six key findings of the report which stood out to me: ⬇️ 1. 𝗧𝗼𝘂𝗰𝗵𝗹𝗲𝘀𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝘃𝗶𝘀𝗶𝗼𝗻 — 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝘀𝗰𝗮𝗹𝗲𝗱 ➜ By 2027, 85% of execs expect agentic systems to run major parts of operations — 24x7, touchless, and outcome-driven. 2. 𝗧𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗿𝗼𝗹𝗲 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 — 𝗳𝗿𝗼𝗺 𝗱𝗼𝗶𝗻𝗴 𝘁𝗼 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 ➜ Employees will no longer “execute the process.” They’ll manage outcomes, monitor agents, and handle complex exceptions. The paper calls this “digital labor management” — and it’s becoming probably a new profession. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗮 𝘁𝗼𝗼𝗹 — 𝗶𝘁’𝘀 𝗮 𝗻𝗲𝘄 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 ➜ Agents aren’t RPA bots or just deterministic workflows. Agents adapt, self-correct, and collaborate. They make decisions, route exceptions, and personalize interactions — with minimal oversight. In the future, they will be connected in multi-agent workflows and build new ecosystems within companies based on a new operating model. 4. 𝗛𝘂𝗺𝗮𝗻𝘀 𝗿𝗲𝗺𝗮𝗶𝗻 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗳𝗼𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 ➜ AI agents execute — but it’s people who set direction, define ethical boundaries, and bring empathy to decisions. In the next operating model, human oversight is what makes AI work responsibly. 5. 𝗧𝗲𝗰𝗵 𝗮𝗹𝗼𝗻𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 ➜ 74% of execs cite skills gaps as their biggest blocker. Governance, data architecture, and identity management for AI agents must now be treated as core enterprise capabilities. 6. 𝗕𝗲𝗶𝗻𝗴 𝗿𝗲𝗮𝗱𝘆 𝗺𝗲𝗮𝗻𝘀 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ Real-time feedback loops, persistent memory, inter-agent coordination, and outcome governance aren’t nice-to-haves. They’re the foundation for scaling enterprise-grade agent systems. If you’re building an AI-enabled operations function — this is essential reading. Full report and commentary in the comments. Enjoy!

I recently wrote that AI is not just a technology shift – it's a work shift. So, how does that play out? First, AI changes how we do tasks. Next, it changes how we do our jobs. Then, it changes entire functions. The result? A brand new way of getting work done and thinking about growth. Step 1: AI transforms tasks: AI works with you. It helps you do what you’ve always done — just faster. A marketer drafts blog posts in minutes. A rep writes emails with higher personalization, less effort. A support leader summarizes tickets in seconds. This is where most teams are today: AI as a productivity booster. Step 2: AI transforms jobs. AI works for you. It starts delivering outcomes. A content agent spins one blog into a full campaign. A prospecting agent books qualified meetings without human touch. A customer agent handles most Tier 1 support tickets. The job itself starts to evolve. You spend less time doing — and more time creating, optimizing, and scaling. Step 3: AI transforms functions. As agents take on entire workflows, the structure of departments begins to shift: Support shifts from to proactive experience design. Marketing shifts to creative strategy. Sales shifts to high-impact closing. Role ratios change. Skillsets shift. We are not quite here but we can see the path. The result for scaling businesses? A whole new way of approaching work, structuring teams, and thinking about growth.

Think AI will cut contact center staff? If you’ve been reading the headlines, you’ve seen plenty of predictions that AI will reduce contact center—and overall customer service—staffing. The logic seems simple: AI can handle customer interactions directly, so organizations won’t need as many people. Add to that AI tools that help agents retrieve information, document cases, and shorten handling time, and the argument looks even stronger. But the assumption that contact center work will decline across the board? That’s misleading. I am first to put my hand up when there are opportunities to improve efficiency and effectiveness—and there almost always are. But there are also many factors adding to contact center workload: Unmet demand. In too many cases, customers can’t even get through quickly. As organizations improve experiences, that suppressed demand surfaces. Product and service complexity. Think connected devices, customized financial advice, challenges in the insurance sector, changes in healthcare ... you get the gist, this list could go on and on. More channels. These can include phone, text, email, chat, messaging apps, social media, video, et al. As any experienced contact center manager will tell you, adding channels rarely replaces old ones—it just adds to the mix. The self-service paradox. The more you automate the more defined interactions, the tougher ones land with your team. Proactive outreach. Organizations are starting to use AI to reach out—wellness checks, customer retention, and others. That’s more contacts overall, not fewer. Regulation and compliance. Especially in healthcare, finance, and utilities, oversight is increasing, adding to review work, including of decisions and summaries made by AI. Security and fraud. Scams are escalating in sophistication—often using AI. Detecting deepfakes, verifying identity, and resolving disputes are high-stakes responsibilities that require experienced humans. Business change. New products, subscription models, mergers—these always generate customer questions. Differentiating on experience. Customer experience is one of the most powerful (and few remaining) ways to stand out. Think concierge service, retention specialists, account advisors—roles that depend on skilled people. AI will play a powerful role in service delivery. But the real story is a redefinition and rebalancing of work. Don’t assume AI will magically erase demand. The headlines may scream “AI is cutting contact center jobs,”—don’t buy it. Customer expectations are only going up. Meeting them will require both the best of AI and the best of us.

The Death of SaaS (as We Know It) Satya Nadella recently shared a fascinating perspective: AI is poised to replace traditional application layers, embedding business logic directly at the database level. This marks a profound shift: one that could redefine the very foundation of SaaS. Imagine a future where AI doesn’t just power apps but replaces them. Business logic, instead of flowing through multiple layers of UI, middleware, and APIs, is orchestrated directly with the database. This means the end of bloated, layered software and the beginning of lean, AI-native architectures. The ripple effects are massive. SaaS as a subscription model may lose relevance as modular AI-driven workflows dominate. Interfaces will transform, shifting away from dashboards and fixed workflows to adaptive, real-time experiences—think voice commands, conversational AI, or neural interfaces. Even the app store economy may collapse under the weight of this new paradigm, replaced by marketplaces for AI-driven workflows instead of apps. This could imply the extinction for the SaaS we know today. For developers, businesses, and consumers, this shift will reshape how software is built, sold, and used. The question isn’t if SaaS is dying; it’s what comes next. What do you think? Is this the end of SaaS, or the beginning of something even more disruptive?

AI is dramatically reshaping business models. This framework is the foundation of my new LinkedIn Learning course "AI-Driven Business Model Innovation". See below for a brief summary of the 6 domains of AI’s impact on value creation, together with the major driving forces and the capabilities required as business models rapidly evolve. Link to the course - free for LinkedIn subscribers - in comments. DRIVING FORCES 🧠 Driving Forces of AI Evolution We’re at a structural shift in business. AI capabilities are accelerating, costs are falling, and data is becoming a strategic asset. These forces are reshaping the foundations of value creation — demanding that leaders rethink not just what their business does, but how it evolves. SIX DOMAINS OF AI-DRIVEN BUSINESS MODEL INNOVATION ⚙️ Scalable Efficiency AI enables organizations to operate at a new scale — automating tasks, streamlining decisions, and amplifying productivity. This isn’t just about cost-cutting and efficiency — it’s augmenting talent for higher-value work and building systems that continuously learn and improve. 🎁 Enhanced Value Propositions AI enhances what you offer — and how it’s experienced. From smart, adaptive products to deeply personalized services, it allows you to deliver more relevance, utility, and meaning to every customer. The frontier of value lies in customer responsiveness and learning at scale. 💞 Shifting Customer Relationships AI transforms how we engage with customers — not just improving service, but enabling co-creation, building trust, and responding to individual needs in real time. The most successful companies will be those that become embedded in customers’ lives through intelligent, trusted relationships. 🏗️ Redesigning Organizations Organizations must evolve from static hierarchies to adaptive systems that blend human and AI capabilities. This means rethinking workflows, decision-making, and structures to be more fluid, responsive, and innovation-driven. AI is not a bolt-on — it enables dramatic reconfiguration of value creation. 🧑💻 The AI Agent Economy AI agents are becoming participants in the economy — acting on behalf of users, negotiating, coordinating, and executing tasks. This shift calls for new strategies, where businesses design for agents as well as humans, and where trust and interoperability become core to competitive advantage. 🌐 AI in Platforms and Ecosystems The most powerful business models today are built around data-rich ecosystems. AI turns data into action, unlocking new platform value and shared innovation. Success increasingly depends on how well you participate in — or build — dynamic, intelligent ecosystems. CAPABILITIES 🚀 Capabilities for AI Evolution Thriving in this landscape requires more than tools. It demands vision, adaptability, experimentation, and the ability to work across boundaries — human, organizational, and technical. These capabilities are the foundation of tomorrow's business models and success.

Last month, a drone from Skyfire | AI was credited with saving a police officer’s life after a dramatic 2 a.m. traffic stop. Many statistics show that AI impacts billions of lives, but sometimes a story still hits me emotionally. Let me share what happened. Skyfire AI, an AI Fund portfolio company led by CEO Don Mathis, operates a public safety program in which drones function as first responders to 911 calls. Particularly when a police department is personnel-constrained, drones can save officers’ time while enhancing their situational awareness. For example, many burglar alarms are false alarms, maybe set off by moisture or an animal. Rather than sending a patrol officer to drive over to discover this, a drone can get there faster and determine if an officer is required at all. If the alarm is real, the drone can help officers understand the situation, the locations of any perpetrators, and how best to respond. In January, a Skyfire AI drone was returning to base after responding to a false alarm when the police dispatcher asked us to reroute it to help locate a patrol officer. The officer had radioed a few minutes earlier that he had pulled over a suspicious vehicle and had not been heard from since. The officer had stopped where two major highways intersect in a complex cloverleaf, and dispatch was unsure exactly where they were located. From the air, the drone rapidly located the officer and the driver of the vehicle he had pulled over, who it turned out had escaped from a local detention facility. Neither would have been visible from the road — they were fighting in a drainage ditch below the highway. Because of the complexity of the cloverleaf’s geometry, the watch officer (who coordinates police activities for the shift) later estimated it would have taken 5-7 minutes for an officer in a patrol car to find them. From the aerial footage, it appeared that the officer still had his radio, but was losing the fight and unable to reach it to call for help. Further, it looked like the assailant might gain control of his service weapon and use it against him. This was a dire and dangerous situation. Fortunately, because the drone had pinpointed the location of the officer and his assailant, dispatch was able to direct additional units to assist. The first arrived not in 5-7 minutes but in 45 seconds. Four more units arrived within minutes. The officers were able to take control of the situation and apprehend the driver, resulting in an arrest and, more important, a safe outcome for the officer. Subsequently, the watch officer said we’d probably saved the officer’s life. [Reach length limit; full text: https://lnkd.in/g3QdKp5Q ]

The AI hype cycle is fading, but its prevalence is only set to grow.    As I shared with Rocio Fabbro at Quartz, AI is becoming less of a rising star and more of a behind-the-scenes operator that’ll quietly (but significantly) influence how organizations think about every process, product, and decision.    We’re at an inflection point where AI is poised to evolve much like electricity 💡: invisible in our daily lives but powering everything. It won’t be about if AI is being used -- but how it’s driving transformation across industries.    Here’s what else is ahead according to Deloitte’s 2025 Tech Trends (https://deloi.tt/3BYn523):    🤖 AI Everywhere: We’re moving from experimentation to operationalization, with AI embedded into other major innovations—spanning customer service, supply chains, product development, and beyond.    📊 Fusing Small and Large Language Models: It’s not a matter of “either/or” between large and small language models—it's both. Organizations are combining the right models to address business needs.    🖥️ Practical Applications for Quantum Computing: From post-quantum cryptography to solving problems beyond the limits of traditional computing, the horizon is expanding. The AI of 2025 will be smarter, more focused, and deeply integrated into everything we do – albeit more quietly. The hype may fade, but the impact is just beginning! 

I've often emphasized that making AI work in the enterprise isn’t just about technology—it’s about delivering real business outcomes. Here’s what we’ve heard from our customers: ✅ A leading real estate firm reduced time spent searching for information and is on track to save $5.5 million this year. ✅ A home improvement retailer cut engineering debugging time, leading to $2.4 million in annual savings. ✅ A telecommunications company slashed customer support resolution time from 2 minutes and 21 seconds to just 18 seconds. ✅ One company re-deployed 12 engineers from an internal support project, saving 24,000 hours annually for higher-impact work. ✅ An online home retailer automated responses in high-volume Slack channels, enabling the redeployment of 1–3 full-time employees. ✅ A collaboration platform accelerated account research, cutting annual report analysis time from 2 hours to 10 minutes. This is what real AI-driven impact looks like. What’s the most impactful way AI has changed the way your team works?

This ChatGPT feature will change travel & hospitality forever. The day AI stops recommending and starts selling is here. Until recently, ChatGPT could tell you what to buy. Now, it can sell it to you directly. No browser tabs. No booking engines. Just: “I want this.” → “Buy.” → Done. It’s called Instant Checkout, built with Stripe . At launch it’s limited to simple products, but the direction is clear AI is becoming a commerce layer, not just a conversation. Why this changes everything for hotels For years, hotels have been optimising for search: keywords, metasearch, OTA rankings, paid clicks. That era is ending. Soon, travellers won’t “look” for hotels they’ll ask an assistant to design their stay. “Find me a design-led hotel in Florence with great coffee and an outdoor tub.” “Book a weekend where I can switch off completely, yoga, silence, forest.” And ChatGPT won’t just show links. It will build the itinerary, compare inventory, and complete the purchase inside the chat. That means the question is no longer “How do we rank higher on Google?” BUT “How do we become understandable and buyable to AI?” What tomorrow could look like Instead of booking engines, imagine this flow: Guest: “Plan me a 2-night recharge in Tuscany.” ChatGPT: “Would you like thermal baths, vineyard spa, or forest retreat?” Guest: “Vineyard.” ChatGPT: “I’ve found 3 properties. One includes cold plunges and biodynamic dining, €890. Confirm?” That’s not science fiction it’s the logical next step of Instant Checkout. The entire funnel collapses into a single dialogue. How hotels can prepare right now 1️⃣ Make your experiences machine-readable Structure your offers like data. “Private wine tasting, €120, 60 min, available Tue–Sat, includes transfer.” 2️⃣ Rethink your product catalogue Instead of “rooms” and “rates,” design modular experiences: morning rituals, energy resets, chef-led tastings, sunset rituals. 3️⃣ Expose your inventory to AI Ensure your website and PMS feed clear, structured information that APIs and agents can read and understand. 4️⃣ Keep ownership of fulfilment When bookings happen through assistants, whoever fulfils keeps the relationship don’t outsource that part. Hospitality used to be about visibility. Now it’s about readability. The hotels that speak the language of algorithms clear, structured, meaningful experiences will appear first when AI plans your guest’s next trip. AI won’t replace hoteliers. But it will reward the ones who make their experiences easy to buy. P.S. If you found this valuable, that’s exactly what we do. We help hotel brands refine their positioning, branding and user journey to drive direct bookings. Send me a DM for more info :)