Reasons Companies Are Adopting AI Solutions

One in three companies are planning to invest at least $25m in AI this year, but only a quarter are seeing ROI so far. Why? I recently sat down with Megan Poinski at Forbes to discuss Boston Consulting Group (BCG)'s AI Radar reporting, our findings, and my POV.   Key takeaways below for those in a hurry. ;-)   1. Most of the companies have a data science team, a data engineering team, a center of excellence for automation, and an IT team; yet they’re not unlocking the value for three reasons:   a. For many execs, the technologies that exist today weren't around during their school years 20 years ago. As silly as it is, but there was no iPhone and for sure no AI at scale deployed at people’s fingertips.   b. It's not in the DNA of a lot of teams to rethink the processes around AI technologies, so the muscle has never really been built. This needs to be addressed and fast...   c. A lot of companies have got used to 2-3% continuous improvement on an annual basis on efficiency and productivity. Now 20-50% is expected and required to drive big changes. 2. The 10-20-70 approach to AI deployment is crucial. Building new and refining existing algorithms is 10% of the effort, 20% is making sure the right data is in the right place at the right time and that underlying infrastructure is right. And 70% of the effort goes into rethinking and then changing the workflows. 3. The most successful companies approach AI and tech with a clear focus. Instead of getting stuck on finer details, they zero in on friction points and how to create an edge. They prioritize fewer, higher-impact use cases, treating them as long-term workflow transformations rather than short-term pilots. Concentrating on core business processes is where the most value lies in moving quickly to redesign workflows end-to-end and align incentives to drive real change.   4. The biggest barrier to AI adoption isn’t incompetence; it’s organizational silos and no clear mandate to drive change and own outcomes. Too often, data science teams build AI tools in isolation, without the influence to make an impact. When the tools reach the front lines, they go unused because business incentives haven’t changed. Successful companies break this cycle by embedding business leaders, data scientists, and tech teams into cross-functional squads with the authority to rethink workflows and incentives. They create regular forums for collaboration, make progress visible to leadership, and ensure AI adoption is actively managed not just expected to happen.

🔎 The latest WEF report on enterprise AI adoption is incredibly detailed and well-researched! It’s one of those reports that feels more like a story than just numbers & numbers. ⛳ Some patterns that stood out to me 👉 GenAI adoption is led by human-centric industries like healthcare, finance, media, and entertainment—not just tech companies. These industries are using AI for automation, personalization, and content creation, shifting the focus from pure tech to human-centered applications. 👉 Scaling AI is *still* a major challenge—74% of enterprises struggle to move beyond PoCs, and only 16% are truly prepared for AI-driven transformation. Many remain stuck in early adoption phases with fragmented experiments and no clear strategy. 👉 The most successful AI adoption relies on "fusion skills"—where AI augments human intelligence, not replaces it. Organizations that combine critical thinking, judgment, and collaboration with AI see far better results than those pushing pure automation. 👉 Workforce concerns are a real barrier. Many employees fear job displacement and burnout, leading to resistance. Companies that focus on reskilling and AI literacy will see smoother adoption and long-term success. 😅 These are unprecedented times, and learning from others’ experiences is invaluable. The key patterns keep seeing in multiple reports: ⛳ Start with the problem first: A solid strategy that prevents AI PoCs from getting stuck. ⛳Augment before automating: Don’t rush to replace humans, make them more powerful. ⛳ Invest in upskilling employees: AI adoption is smoother when people feel equipped, not threatened. ⛳ A good strategy is everything: Without one, AI initiatives fail before they even start. Link: https://lnkd.in/gsRJT2D5

A year ago, AI was considered a side project. Now it is a core strategy. Forward-looking businesses are moving from hype to implementation, using AI to solve targeted pain points with measurable outcomes. According to McKinsey's latest State of AI report, organizations are rewiring their entire operations around AI to capture measurable value. Here's 11 ways companies are seeing AI-driven ROI: 1/ Customer Service Automation Companies are moving beyond basic chatbots to full-service AI agents. ↳ 45% reduction in response time ↳ 30% cost savings in support operations 2/ Predictive Maintenance AI analyzes equipment data to prevent costly downtime. ↳ 20% decrease in equipment downtime ↳ $2M average annual savings for manufacturing 3/ Personalized Marketing Deep learning models predict customer behavior and optimize campaigns. ↳ 3x increase in conversion rates ↳ 40% reduction in customer acquisition costs 4/ Supply Chain Optimization AI-driven forecasting revolutionizes inventory management. ↳ 15% inventory reduction ↳ 25% improvement in forecast accuracy 5/ Sales Intelligence Advanced analytics turn data into actionable sales insights. ↳ 35% increase in qualified leads ↳ 28% shorter sales cycles 6/ Document Processing NLP transforms unstructured data into business intelligence. ↳ 80% reduction in manual processing time ↳ 60% decrease in errors 7/ Product Development AI accelerates innovation and reduces time-to-market. ↳ 40% faster time-to-market ↳ 25% reduction in development costs 8/ Risk Management Machine learning spots patterns humans miss. ↳ 50% better fraud detection ↳ 30% reduction in false positives 9/ Employee Productivity AI assistants augment human capabilities. ↳ 4 hours saved per employee weekly ↳ 20% increase in output quality 10/ Process Mining AI identifies inefficiencies and optimization opportunities. ↳ 35% efficiency improvement ↳ $3M average operational savings 11/ Knowledge Management AI transforms company data into accessible insights. ↳ 60% faster information retrieval ↳ 40% reduction in training time The key difference in 2025? Custom-built solutions tailoring models to your unique workflows, data sets, and industry context. As AI matures, the gap will widen between companies that customize and those that generalize. What AI initiatives are delivering the best ROI in your organization? Share below 👇 Sign up for my newsletter: https://lnkd.in/gyJ3FqiT ♻️ Repost to your network if they are looking for AI-related content.

AI adoption is failing quietly inside most enterprises. Not because of bad tools. Because of bad leadership. The companies that win with AI aren’t the ones with the best tech. They’re the ones creating environments where people want to use it. What actually drives AI adoption? Psychological safety. Team-led learning. Eliminating soul crushing work. Here’s what leading companies are doing differently: 1. They build real time for learning. At top companies, teams block time to experiment, test prompts together, and learn in public. Zapier went from 65% to 89% daily AI adoption by enabling this culture. At Udemy, leaders host group learning sessions before asking teams to change behavior. 2. They dismantle the 'AI equals layoffs' narrative. At Shopify and Zapier, leaders promised not to cut jobs due to AI. Instead, they raised the bar and invested in reskilling. Engagement scores rose. So did performance. 3. They empower managers as AI multipliers. BCG research shows that when managers use AI and engage their teams directly, adoption increases 4X. Not with top down mandates. With shared experiments that remove pain points, especially admin and reporting tasks. 4. They reward transparency, not perfection. Lauren Franklin at Zapier helped handle support tickets weekly to see what was working. That clarity improved process and team morale. 5. They measure what matters. Leading teams track frequency of use, shared prompts, and real workflow impact. Their AI maturity is defined by behavior, not just access. This is what works. And this is how companies are closing the gap between pilot projects and scaled transformation. Thanks to Brian Elliott for this phenomenal breakdown. Check the comments for the full piece and carousel. What’s the most effective team based AI strategy you’ve seen so far? #PeopleAnalytics #AIatWork #FutureOfWork #DigitalTransformation #WorkplaceInnovation

I was in London last week, and (knowing the English) what I expected was AI skepticism. What I found was intensity. Despite the economic cloud hanging over the UK, AI isn’t just a topic of conversation. It’s the focus. I met with execs at large public companies who are planning to spend 6-10 hours in AI immersion sessions – full-day workshops, not lunch-and-learns.   That level of commitment speaks volumes. And the numbers back it up. 75% of UK midsize enterprises now use AI in at least one department. So what’s driving this adoption? First, the pressure. The UK is in a budget squeeze. Everyone’s looking for leverage, and AI feels like the best option on the table. Second, Brexit. With less regulatory baggage than the EU, there's a sense that UK firms can move faster, try more, and get ahead while the rest of Europe sorts through red tape. And third, the recognition (not true in the US – yet) that AI leadership requires familiarity with the tools. You can’t lead if you don’t know the terrain. If you’re a leader who’s waiting for AI to stabilize before you engage, take a lesson from the UK. They're getting their hands dirty now.

I've had over 500 AI agency sales calls and here's what businesses actually want. (Spoiler: It's not simple chatbots or voice agents although they do sell) While everyone's building weekend ChatGPT wrappers, businesses are quietly paying $15,000+ for completely different AI solutions. After generating six figures in AI service revenue, I've discovered exactly what companies are willing to pay premium prices for. The reality check: A $2M ARR SaaS company told me they'd rather pay $20,000 for a solution that increases revenue by $50,000 monthly than pay $2,000 for a chatbot that saves 5 hours per week. (who would've thought.. 😂) That conversation changed everything about how I approach AI services. What businesses actually pay premium prices for: Sales Automation Systems - Intelligent prospect identification across multiple data sources - Automated research and enrichment for each lead - Multi-channel outreach orchestration (email, LinkedIn, phone) - Dynamic nurturing sequences that adapt to prospect behavior - Lead scoring that prioritizes highest-value opportunities Content Creation Engines - Automated market research and competitor analysis - Multi-format content generation across all platforms - Advanced SEO optimization and ranking strategies - Brand voice consistency across all channels - Performance tracking and optimization Operational Workflow Solutions - Complete client onboarding automation - Document processing and compliance monitoring - Intelligent customer support with escalation protocols - Quality control and audit trail systems - Project management and resource optimization Data Processing & Analytics - Multi-system data integration and business intelligence - Predictive modeling for forecasting and optimization - Real-time performance optimization - Competitive intelligence gathering - Custom executive dashboards The industries reaching out most: - Professional services (agencies, consulting, law, accounting) - E-commerce and retail ($500K-$10M annual revenue) - Manufacturing and distribution - Healthcare and compliance-heavy businesses Why these command premium pricing: They solve expensive problems that directly impact revenue, provide strategic advantages competitors can't replicate, and generate measurable ROI that far exceeds investment. Stop building tools and start solving business problems. When you can demonstrate $200K in additional revenue or $150K in cost savings, charging $25K becomes an easy decision. 👉 Want the complete breakdown of high-value AI solutions? 1. Connect with me 2. Comment "SOLUTIONS" I'll send you the detailed analysis. (Must be connected - prioritizing reposts first!)

Top 10 reasons why Investors like me are betting big on AI startups: 50% of my network is made up of CEOs and Founders. And every other day, I see posts about fundraising. Most of these startups are backed by AI. I kept wondering, Why AI? Why now? Here’s what I found 👇 10 solid reasons why AI startups are getting all the attention: → AI makes businesses smarter. It helps predict trends, cut costs, improve products, and make faster decisions using real data. → It turns unused data into valuable insights. Most companies sit on tons of data. AI helps turn that into information they can act on. → AI startups can grow faster without growing expenses. Once the AI system is built and trained, it can handle thousands or millions of users without needing a huge team. →  It personalizes products for every user. AI can recommend, suggest, and respond to customers in a way that feels personal, for millions of people at once. → AI is opening new industries. From AI health apps to AI marketing tools, entire business categories are being built around AI technology. → It’s quickly becoming a must-have, not a nice-to-have. Just like every business moved to the cloud, every business will need AI tools to stay competitive. → It helps startups move faster. AI tools speed up product development, testing, and customer feedback, helping startups improve and launch quickly. →  It makes businesses future-ready. AI learns and improves over time, which means your product keeps getting better without extra effort. → The more data you have, the stronger your AI gets. Startups using AI build a natural advantage over time because their system keeps learning from every customer. → Big companies are actively buying AI startups. Tech giants like Google, Microsoft, and Amazon are constantly looking to acquire AI talent, tools, and businesses. In short, AI is solving real problems, saving time, cutting costs, and creating new markets. If you’re building or investing in AI, you’re right where you need to be. I'm doing both.

ICYMI: ✨ Wharton + GBK Collective on Enterprise Gen AI Adoption: Moving Beyond the Hype in 2024 Exciting findings from our latest study "Growing Up: Navigating Gen AI’s Early Years," done with over 800 industry executives in the US, in partnership with Wharton AI & Analytics Initiative. This is one of the only studies with year over year comparison of AI adoption, use cases, spending and perceptions across major business functions. Our 2024 study finds that #GenAI has entered a new phase: Companies are shifting from excitement to action, moving from personal experimentation, and now focusing on proving ROI and understanding Gen AI’s true performance. This year, we saw a remarkable increase in usage—72% of decision-makers are using it weekly, compared to 37% last year. The fastest-growing areas? Marketing, Operations, and HR. Key findings underscore how quickly Gen AI is transforming workplace dynamics: • Usage by Marketing and Sales teams has tripled (20% to 62%), and adoption has doubled in Operations, HR, and Procurement. • AI spending is up 130% this year, with 75% of companies planning more investment in 2025. • Positive sentiment is rising—90% of leaders now agree that AI enhances employee skills, and concerns about job replacement are easing. While enthusiasm remains high, companies still face big questions around when and how Gen AI can drive the most value (and when they can trust it!) And smaller companies our outpacing larger enterprises in adoption, potentially driving new competitive dynamics. The next phase will focus on identifying impactful use cases and aligning AI investments with measurable outcomes. Many thanks and kudos to the collaborative team behind this - including my co-authors Prof. Stefano Puntoni, and Mary Purk from The Wharton School, as well as the amazing GBK Collective insights team Brian Smith, Daniel Urbina-McCarthy, Amelia Francesca Colón Executive summary is embedded in this post, with a link to the full report in the comments below. We'd love to hear your reactions and feedback! #GenAI #TechAdoption #Innovation #marketing #GenerativeAI #Wharton #AIatWharton #Enterprise