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AI in 2026: What Will Actually Matter to Business Leaders In 2026, AI should be improving your business metrics significantly. If you are still not using it, you are leaving a lot of efficiency on the table. SAP’s 2026 AI outlook makes it clear that business advantage will come from where AI is placed in your business and how directly it shapes outcomes. Outlook 1: From Generic AI to Business-Specific Intelligence Enterprises are moving away from general-purpose models toward specialized AI trained on structured business data, because only domain-specific models improve forecast accuracy and execution quality. This means: Faster execution with fewer process failures. Outlook 2: Agentic AI Will Reshape Operations, Not Tools Autonomous AI agents will increasingly plan and execute multi-step tasks, because this is the only way to scale decisions without scaling headcount. Agent governance will become mandatory, because ungoverned agents create operational risk and accountability gaps. This means: Scalable automation without loss of control. Outlook 3: Intent-Driven Systems Will Replace Interface-Driven Work Natural language and intent-based interfaces will reduce dependency on complex enterprise navigation; employees express outcomes faster than they complete workflows. Sovereign and compliant AI architectures will gain importance because regulatory alignment determines where AI can be deployed safely. This means: Faster adoption with lower organizational friction. Two takeaways for legacy business leaders 1. AI returns depend on integration depth. Disconnected pilots cannot change the flow of work through your organization. 2. Data quality defines the AI ceiling. Poorly governed data limits decision confidence and caps long-term value creation. One practical tip to begin integrating AI Select one revenue-critical or cost-critical workflow, identify decisions that delay outcomes and apply AI only where it shortens decision time or removes manual dependence. If AI does not improve speed or economics, it should not be deployed. #AITransformation #EnterpriseAI #AIStrategy #DigitalTransformation #AIImplementation #TechLeadership #BusinessTransformation #AIOperations #OrganizationalChange #DataGovernance

2026 will be a remarkable year for innovation, where agentic AI will help create new business models and solutions we can’t yet predict. But what stands out isn’t just the pace of progress that we’ll see. It’s what this will demand of leaders to sustain it. Because while AI adoption is still accelerating, the technology alone won’t be able to deliver its full potential. My biggest reflection over the holidays: when people say AI or technology “doesn’t deliver,” it’s rarely because the technology isn’t capable. It’s because it hasn’t been fully embraced and adopted at scale, across the whole organisation. The organisations that I expect to reap the biggest rewards will start 2026 by investing wisely in the capabilities of their teams. They must empower leaders to embrace the technology from the top, to invent and reinvent processes front-to-back, and to rethink mental models on how to work and organise in the age of AI. And in turn, leaders have to find and grow talent who will dive deep, roll up their sleeves, move with speed, and nurture high-performing teams. All of this will require reorganising around outcomes and not classic silo functions, and creating space for experimentation so that there is room for the right mechanisms to create great ideas and move them from prototype to production. So, in 2026, be the leader who can turn potential into impact. Be the leader who can scale ideas to create competitive advantage. Be willing to say no as much as you say yes. And be open to challenging your own beliefs and learning. Organisations and leaders have a real opportunity to define the future of their industries if they commit to building the right capabilities, cultures, and mechanisms. That is what makes 2026 such an exciting year. My focus will be helping our EMEA customers navigate this pivotal time, to find that dimension to their leadership, and to create lasting impact. What’s yours?

Gartner recently predicted that by 2028, 56% of CEOs expect to use AI to restructure middle management. This is not to reduce headcount, but to expand a manager’s span of value and move away from routine oversight. 𝗜𝗻 𝗺𝘆 𝗼𝗽𝗶𝗻𝗶𝗼𝗻, 𝘁𝗵𝗮𝘁’𝘀 𝗮 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘀𝗶𝗴𝗻𝗮𝗹. For years, middle management has carried the weight of coordination, tracking progress, ensuring compliance, and resolving bottlenecks. Much of that effort has been necessary… but also largely administrative. AI is beginning to take that load off. When systems can monitor workflows in real time and surface risks early, the need for 'oversight' as we know starts to diminish. This is of course, with the human-in-the-loop model. And that raises an important question: 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗹𝗼𝗼𝗸 𝗼𝘃𝗲𝗿 𝘀𝗵𝗼𝘂𝗹𝗱𝗲𝗿𝘀? In my view, it becomes far more human. Managers are no longer defined by how closely they track work, but by how effectively they are able to enable it. At the same time, these middle managers will need to be more hands-on in this new world, rather than being pure people managers. Companies that expect their managers to be technical and understand field design, architecture, and the related complexity will likely do much better. The roles will shift from supervision to amplification. It will further evolve from simply checking that things don’t go wrong, to actually helping teams do their best work. 𝗧𝗵𝗶𝘀 𝘄𝗶𝗹𝗹 𝗮𝗹𝘀𝗼 𝗰𝗵𝗮𝗻𝗴𝗲 𝘄𝗵𝗲𝗿𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗽𝗲𝗻𝗱 𝘁𝗵𝗲𝗶𝗿 𝗲𝗻𝗲𝗿𝗴𝘆: • From checking status to shaping direction • From enforcing process to building capability • From managing tasks to mentoring people AI can expand a manager’s span of control, but the real opportunity is expanding their span of impact. This is because while AI can provide visibility, but it cannot replace judgment, context, or trust. 𝗧𝗵𝗼𝘀𝗲 𝗿𝗲𝗺𝗮𝗶𝗻 𝗱𝗲𝗲𝗽𝗹𝘆 𝗵𝘂𝗺𝗮𝗻, 𝗮𝗻𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆, 𝘁𝗵𝗲𝘆’𝗿𝗲 𝘄𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗯𝘆. #Leadership #AI #FutureOfWork #Management #DigitalTransformation #PegaIndia #Pega

This report from ICONIQ focuses on the "“how-to”: what it takes to conceive, deliver, and scale AI-powered offerings end-to-end", mapping out how what startups building AI products are doing - successfully or otherwise - based on a survey of 300 startup executives. They define "AI-native" as where the core product or business model is AI-driven, while "AI-enabled" adds AI capabilities to existing products or new non-core AI products. There is a lot in the deck, worth going through, but a few highlights: 🚀 AI-native companies scale faster and earlier. 47% of AI-native products are already in the scaling phase versus only 13% of AI-enabled products, showing structural advantages in speed and maturity. 💡 High-growth companies build more agentic workflows. 47% of high-growth firms are actively deploying AI agents in production, compared to 32% of other companies. 🧠 External AI is optimized for accuracy, internal AI for cost. Accuracy is the top model selection factor for customer-facing products (74%), while cost leads for internal tools (72%). 🧾 API costs are the biggest budgeting challenge. 70% of respondents rank API usage fees as the hardest infrastructure cost to manage, outpacing inference, training, and storage. 💰 Inference spending explodes post-launch. High-growth companies spend up to $2.3M/month on inference at scale—more than 2x that of their peers. 📊 Coding tools lead in real productivity gains. 65% ranked AI coding assistants as the top driver of productivity, with high-growth companies reporting 33% of code written by AI. 📈 AI engineering headcount is rapidly increasing. High-growth companies expect 37% of engineering roles to focus on AI in 2026, up from 28% in 2025. 🧩 Open-source and inference optimization are key cost controls. 41% are switching to open-source models and 37% are optimizing inference efficiency to combat spiraling costs. 🏷️ AI pricing is still immature and mostly bundled. 73% of AI-enabled companies either include AI in premium tiers or at no extra cost, but 37% plan to revise pricing based on usage or ROI. ⚖️ Explainability is a critical barrier to trust. 42% of companies cite explainability as a top-3 deployment challenge, especially in regulated sectors. 📉 Only half of employees use AI tools regularly. Despite 70% of employees having access to AI tools, only 50% use them consistently—dropping to 44% in $1B+ enterprises. 🧪 Monitoring is common but automation lags. 75% of scaled AI products include advanced monitoring, but few teams have fully automated retraining pipelines. 🛠️ Proprietary models are a high-growth differentiator. 54% of high-growth firms fine-tune foundation models and 32% build proprietary models, compared to 32% and 20% respectively among others. 📦 AI-native firms build more agentic and vertical tools. 79% of AI-native firms focus on agentic workflows, while 56% also build vertical applications tailored to specific industries.

AI in Sales—Augment, Don’t Replace! 🚀 AI won’t replace salespeople. But salespeople who use AI strategically will outperform those who don’t. I’ve been in sales since Girl Scout cookies were 50 cents a box, and I’ve seen the game change. But nothing has been more transformative than AI. According to LinkedIn for Sales Connect monthly newsletter, AI can reclaim 29% of a rep’s time by automating admin tasks, data collection, and customer insights. The key? Using AI to amplify human strengths, not replace them. Yet, there’s a challenge: 60% of sales teams report being overwhelmed by the sheer volume of administrative work.AI can help offload up to 10 hours of non-selling tasks per week, effectively doubling selling time from 10 to 20 hours. That’s the kind of efficiency shift that drives real revenue. Here’s how to strategically automate without losing the personal touch: ✅ AI-Powered CRM: Let AI handle lead scoring, email follow-ups, and data entry so reps can focus on relationship-building. ✅ Smart Workflows: Use AI tools to automate routine tasks, freeing up time for strategic selling. ✅ AI as a Guide: Train your team to use AI-generated insights as a tool, not a crutch. Judgment and creativity still win deals! 📌 Actionable Step: Identify 3 repetitive tasks in your sales process (CRM updates, lead research, follow-ups) and integrate AI-powered automation. Measure the time saved and reallocate it to higher-value selling activities. AI isn’t the future of sales—it’s happening NOW. How is your team leveraging it? Let’s talk in the comments! #AIinSales #SalesLeadership   #WomenInSales #EnterpriseSales #1MillionWomenby2030 

🚀 2026: 𝐓𝐡𝐞 𝐘𝐞𝐚𝐫 𝐀𝐈 𝐌𝐨𝐯𝐞𝐬 𝐅𝐫𝐨𝐦 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐖𝐢𝐝𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 At PwC, we believe 2026 will be a tipping point for how organisations turn AI ambition into meaningful, measurable value. In our latest 2026 AI Business Predictions, we highlight six key trends shaping this shift: 🎯 Discipline wins over experimentation; success will go to companies that pick a few high-impact areas, rally leadership behind them, and focus their investments there rather than spreading AI efforts thin. 💷 Agentic AI gets real: AI agents will move beyond pilots and start delivering concrete outcomes where it matters: demand forecasting, personalisation, finance, IT, HR and more. 👩💻 A new workforce paradigm emerges; as agents take over routine tasks, businesses will increasingly rely on versatile “AI-generalists” who can orchestrate, oversee and integrate AI across functions. 🔐 Responsible AI shifts from aspiration to action — governance, risk, and ethics frameworks will become central to AI deployment, balancing innovation with trust. 📈Orchestration & scale: innovation won’t stay in sandbox experiments — orchestration layers and repeatable workflows will industrialise AI across the business. 🌱 AI as a lever for sustainability: AI won’t just drive growth, but help companies accelerate sustainable operations, optimise energy use, and reduce environmental impact. If you’re thinking about where to apply AI in your business — or how to evolve your workforce and operating model — now is the time to act. Please get in touch if you’d like to discuss how these trends could apply to your organisation. 🔗 https://lnkd.in/ghbgm_JQ

AI is transforming productivity across industries but sales is still a frontier waiting to be unlocked. Bain & Company’s research shows that while generative and agentic AI are already freeing up hours of work in marketing and operations, adoption in sales is lagging behind. That’s surprising, because sales is one of the most time-intensive, high-impact functions and even small conversion gains can deliver outsized business results. For CMOs, CROs, and GTM leaders, the opportunity is clear: use AI to give sellers back time, improve decision-making, and boost win rates. And here’s what often gets overlooked: buyer-group expansion and engagement are two of the most powerful drivers of those win rates. The more effectively teams can identify, engage, and influence the full buying committee- the CIO, the CFO, the head of engineering, security, procurement- the greater the likelihood of advancing and winning deals. AI can now do this at a scale and speed that simply wasn’t possible before. AI in sales isn’t about replacing people, it’s about equipping them with better tools. The organisations that act early will be the ones to capture the biggest gains. At Thoughtworks, we’ve been actively working toward this vision. Our award-winning PerformanceAI agent removes the need for sales and marketing teams to click through endless dashboards and instead delivers insights in plain English, on demand. Less time on analysis and more time on insight and action. We’re also reducing manual work through automation, from data entry to intelligence orchestration. We’ve invested in tooling that mines sellers’ conversations across voice, email, and calendar to extract key signals, map the buying group, and match insights to the right accounts and opportunities. And in the AI era, adoption in sales has become much simpler. The technology runs quietly in the background rather than becoming another system sellers need to feed. When human input is needed, we’re moving toward a voice-first experience so no navigating complex CRM interfaces, and sellers can make updates on the go right after a client meeting. How is your team approaching AI in sales today? Let me know in the comments.

CEOs are becoming the new Chief AI Officers. With #Davos coming up next week, AI is firmly on the agenda. And our latest BCG #AIRadar survey captures how business leaders are feeling as they head into the year ahead. How do CEOs feel? ➡️ Accountable: nearly three quarters of CEOs say they are their organization’s main decision-maker on AI, and 50% believe their job is on the line if AI does not pay off. ➡️ More confident than a year ago: CEOs are more optimistic about the ROI of their AI investments. One key reason is the rapid maturity of AI agents, which can plan, act, and learn on their own. Nearly all CEOs expect AI agents to deliver measurable returns in 2026. ➡️ Ready to invest more: corporations expect to more than double their AI spending in 2026, from 0.8% to around 1.7% of revenues. ➡️ Committed, even without short-term payback: more than 90% plan to continue investing in AI at current or higher levels, even if investments do not pay off in the next year. ➡️ Expecting deep industry change: by 2028, 90% of CEOs believe #AI will redefine what success looks like in their industry. For CEOs, the coming year will test how quickly intent turns into progress. The survey highlights the actions that will matter most as leaders steer their organizations through the next phase of AI. A must-read to start the year: https://lnkd.in/eqbqiV2e 👏 A big congratulations to the BCG and BCG X teams involved: Jessica Apotheker, Sylvain Duranton, Vladimir Lukic, Nicolas de Bellefonds and Christoph Schweizer.

Are AI Native companies today showing us the future of the org chart? If you want to know what org charts will start to look like in a few years, consider looking at what the top AI Native companies look like today. —————————— We analyzed functional distributions across 1M+ employees in Pave's real-time dataset, comparing the top AI-native companies (70% of the Forbes AI 50 participate in Pave's data including leaders like OpenAI) against the broader non-AI tech market. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗼𝗿𝗴 𝗰𝗵𝗮𝗿𝘁 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲: 1️⃣ 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀. ~50% of headcount at top AI companies sits in engineering vs. ~37% at non-AI tech. 2️⃣ 𝗦𝗮𝗹𝗲𝘀 𝗵𝗼𝗹𝗱𝘀 𝘀𝘁𝗲𝗮𝗱𝘆. ~19% at AI companies vs. ~20% at non-AI tech. Even the most AI-native companies are investing in humans to sell their offerings. The product may be AI, but the GTM motion is still involving humans. 3️⃣ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗴𝗲𝘁𝘀 𝗰𝘂𝘁 𝗶𝗻 𝗵𝗮𝗹𝗳. ~3% at AI Native companies vs. ~7% at non-AI tech. 4️⃣ 𝗙𝗶𝗻𝗮𝗻𝗰𝗲, 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗹𝗹 𝗿𝘂𝗻 𝗹𝗲𝗮𝗻𝗲𝗿. Each of these functions runs 1-2 percentage points thinner at AI companies. Not dramatic individually, but collectively it paints a picture: AI-Native companies are concentrating headcount in builders and sellers and generally compressing everywhere else. —————————— 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: We recently showed that the entry-level IC workforce is shrinking across all of tech, and that AI-Native companies had ~13% more senior ICs and ~16% fewer junior employees: https://lnkd.in/gYHfxv2Y Today's data adds another dimension. It's not just the level mix that looks different at AI companies. It's the entire functional shape of the org. More engineers. Similar sales investment. But fewer support staff. And a leaner back-office.

#AskDrTim 🔆 AI Is Not a Technology Project. It Is a Leadership Test. Everyone is talking about AI tools. I believe we should be talking about AI leadership. AI is no longer confined to the IT department. It is influencing hiring, learning, customer experience, operations, decision-making and even organisational culture. This is no longer a technology rollout. It is a leadership transformation. The question is no longer: “How do we use AI?” The better question is: “How do we lead people through AI?” People are not only worried about losing their jobs. They are wondering: • Will I still be relevant? • Will decisions remain fair? • Can I trust my leaders? • Does anyone actually know where we are heading? That is why AI cannot be owned by HR alone. It requires the CEO, COO, CHRO, CIO, business leaders and employees moving in the same direction. When leadership is absent, people disengage. When governance is weak, people become fearful. When the narrative is unclear, people resist. Trust becomes the new operating system. As leaders, we are also facing what I call leadership inflation. Today we are expected to deliver results, transform organisations, coach teams, protect culture, manage wellbeing, understand AI and navigate ethics, all at the same time. Sometimes it feels like we are flying the plane while redesigning the engine mid-flight. AI will not reduce the need for leadership. It will increase the demand for better leadership. In healthcare, AI can improve diagnosis, workflow and efficiency. But it cannot replace accountability, compassion or clinical judgement. That lesson applies to every industry. Technology may create speed. Leadership creates meaning. The organisations that succeed will not necessarily be those with the most AI. They will be those whose leaders know how to combine intelligence with humanity. As AI becomes more powerful, governance, ethics and trust become even more important. After all… AI can sharpen the lens. It should not remove the human eye. The future of work will not be won by the company with the most AI tools. It will be won by the company whose leaders know how to keep people human while becoming more intelligent. “AI will change the way we work. Leadership will decide whether people feel replaced, reduced or renewed.” — Dr Timothy Low