How Companies can Adapt to AI Innovations

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

Most companies aren’t failing at AI adoption because of the tech. They’re failing because employees are afraid to use it. Tools are rolling out fast. But usage? Still stuck in pilot mode. 52% of employees using AI are afraid to admit it. And when managers don’t model usage themselves, team adoption stalls. One thing is clear: AI adoption doesn’t just happen. You have to design for it. Here are 10 strategies that actually work: 1. Track adoption and set goals. Measure usage patterns and benchmark performance across teams. Make AI part of your performance conversations, like Shopify does. 2. Engage managers. If they use AI, their teams are 2 to 5x more likely to follow. Enable them, train them, and let them lead by example. 3. Normalize usage. More than half of AI users hide it. Reframe the narrative. AI isn’t cheating, it’s table stakes. 4. Clarify policies. Without clear guidelines, people freeze. Spell out what’s allowed and what’s not. 5. Promote early wins. A great prompt that saves hours? Share it. Celebrate it. Build momentum. 6. Share best practices. Run prompt-a-thons. Create internal libraries. Make experimentation part of the culture. 7. Deploy AI agents strategically. Use ONA to spot high-friction workflows. Insert agents where they’ll have the biggest impact. 8. Balance experimentation with safe tooling. Watch what tools employees are adopting organically. Then invest in enterprise-grade tools your teams already want. 9. Customize by role and domain. Sales, HR, engineering, each needs a tailored strategy. Design workflows that reflect the reality of each team. 10. Benchmark yourself. How does your AI usage compare to peers? Track maturity, share progress, and stay competitive. From our work at Worklytics, these are the tactics that move organizations from pilot mode to performance. You can find the full AI Adoption report in the comments below. Which of these 10 is your org already doing and what’s next on your roadmap? #FutureOfWork #PeopleAnalytics #AI #Leadership #WorkplaceInnovation

I've watched companies crash and burn. Duolingo is a prime example. The company thought AI was the answer. But they got it all wrong. Their "AI-first" strategy blew up in their faces. They lost 6.7 million TikTok followers and 4.1 million on Instagram. That's a $7 billion lesson in what happens when you replace people instead of partnering with them. CEO Luis von Ahn decided to cut contractors. He claimed they would only hire if teams couldn't automate their work. Predictably, this led to chaos. Employees revolted. Users were furious. Social media went silent. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: • They tossed out human expertise instead of building on it. • They saw AI as a way to save money, not as a partner. • They spread fear, not hope. • They ignored that culture and creativity can't be replaced by machines. 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀: AI is rewriting the rules of business, but it should only be harnessed when it is integrated with human skills, not when it replaces them. They tackle biases in AI to make sure their systems serve everyone. Microsoft found that teams using AI perform better than those that don't. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘀𝗺𝗮𝗿𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗔𝗜 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆: • Treat AI agents like new team members, onboard them, assign ownership, measure performance. • Set clear human-agent ratios for each function. • Invest in AI literacy training across all levels. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 • Use AI for 24/7 availability and processing power, things humans can't provide • Keep humans in charge of judgment, creativity, and high-stakes decisions • Create "thought partner" relationships where AI challenges thinking leads to ideas 𝗦𝗰𝗮𝗹𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 • Move beyond pilots to organization-wide adoption • Start with functions farthest from your competitive edge • Continuously evaluate and adjust your AI tools The truth is clear. Companies that fail to integrate AI smartly will be left behind. This concerns how AI will change your workforce and how you will lead that change. Will you lift your team up with AI, or will you create fear like Duolingo did? What's your experience with AI integration? Are you seeing partnership or replacement in your industry? The future belongs to those who master human-AI collaboration. Those who don't risk becoming the next cautionary tale. #AIvsEI #BetterTogetherAgency #Duolingo #HumanCentric  

Research with front-line workers, team leaders, and executives shows how they are using #AI and perceive it's usage across their organization. The excerpt in this #ChangeSuccessInsights newsletter provides an analysis across nearly 100 pages of research to distill out the 10 critical insights about how AI is being adopted in organizations. It identifies the key patterns in the data you can leverage, or need to address, to drive greater #AIAdoption and outcomes. 10 Critical Insights About Individual AI Adoption from Prosci Research: 1. Mind the AI Perception Gap - Leaders trust AI, frontline workers hesitate—bridging this gap builds confidence. 2. Prioritize Human Factors in AI Adoption - Technical issues are the minority; trust and training drive true AI success. 3. Balance AI Access Control with Innovation - Excessive restrictions stifle progress; smart access fuels responsible creativity. 4. Distribute AI Expertise Throughout the Organization - AI thrives when knowledge is shared, not concentrated in a few hands. 5. Maintain Transparency in AI Decision-Making - Clear communication about AI choices transforms suspicion into trust. 6. Focus on Role-Specific AI Value Creation - Executives seek efficiency, frontline teams find creativity—tailored AI delivers impact. 7. Build Trust Through Ethical AI Framework - Strong ethical foundations stabilize AI adoption amid evolving expectations. 8. Enable Transformative AI Change - Incremental steps won’t unlock AI’s potential—think boldly, act intentionally. 9. Balance Leadership Vision with Bottom-Up AI Innovation - A strong strategy from above thrives with creativity from below. 10. Prepare for Continuous AI Evolution - AI won’t stand still—adaptive plans outperform rigid strategies. These are the goalposts for better AI implementation and adoption, derived from the experiences and perspectives of over 1000 respondents. The full report will be added to Research Hub soon, and Prosci advisors always here to help you activate the research in your context. Reach out or tune in. And huge thank you to Scott Anderson, PhD for the amazing work on the research.

How do you actually get AI adoption over 90% across the organization? At Credal.ai, we’ve seen how companies succeed and how companies fail in making this adoption happen. 6 lessons lessons from our experience so far: 1. Success in adoption starts with clear ownership to push the initiative to completion. While AI is still new and having dedicated teams is not yet mainstream, for smaller companies (<1000), IT usually leads the charge, while larger enterprises typically assign it to dedicated AI/ML Platform teams. 2. Give your people access & license to experiment (with guardrails)! AI adoption works best when top down meets bottom up. We encounter many organizations who want to “drive AI adoption”, but aren’t willing to let users experiment early on. Around 10% of employees will be early adopters, and then evangelists as they ultimately share production-ready AI applications. Fun fact: Credal users are 16% more likely to be promoted and 27% less likely to face lay-offs vs. their peers. 3. Create a governance program, and make sure people know about it. It’s counterintuitive, but in practice, users who are not sure what they are allowed to do will bias towards doing nothing at all, for fear of breaking rules. Announcing a governance program actually empowers employees. 4. Meet users where they are. One thing will never change - users hate learning new tools. The more that users are given access to AI tooling inside platforms they already use - like Slack, Microsoft Teams, Salesforce, etc, the faster adoption will be. (For Credal, we make it seamless to deploy into Slack). These existing platforms will act as a “gateway” to realizing how useful AI can be, but the key is helping them discover the tooling organically. 5. Pick AI-first partners/vendors. Since the technology is going to move fast, you want your partners and solution providers to move fast as well. Legacy enterprises or tech companies that pivoted into AI products are stuck maintaining legacy codebases and unable to ship basic features that users want. Take Glean for example, which still doesn't allow users to switch between models for their use case. Meanwhile, AI-first companies like Credal support new models on the day they come out. 6. Teach your employees, and even better: let them teach each other. Ultimately, a decentralized education system that lets your employees discover new use cases and teach each other drives much more real world value, much faster. One hackathon hosted by our customer almost single-handedly converted 32% of the invitees into builders and an additional 50% into users of AI. There’s a *lot* more in the blog (link in comments). As ever, please send this to any of your colleagues and friends who are thinking of deploying generative AI in the enterprise, and feel free to email us if we can help at founders@credal.ai. We’re also curious to hear YOUR lessons and takeaways. Comment below if you’re using gen AI in your company, and tell us how!

AI adoption isn’t a ‘yes’ or ‘no’ decision—it’s a curve. If you don’t know where your company is on it, you’re already behind. AI adoption doesn’t start with picking tools—it starts with diagnosing where you are and knowing how to push forward. 👇 Where companies get stuck & how to move forward: 🚀 Stage 1: Awareness & Exploration ✅ Leadership is discussing AI, but there’s no plan. ✅ Teams experiment with AI, but there’s no structure. 🔥 Challenges: ❌ AI feels like hype, not strategy. ❌ Employees don’t trust or understand it. ❌ No alignment on AI tools. 👉 How to move forward: 📝 Run AI training—Show practical use cases. 📝 Pick one impactful AI use case—Start small. 📝 Set early guardrails—Define AI dos & don’ts. ⚡ Stage 2: Experimentation & Adoption ✅ Teams (RevOps, Finance, IT) run AI pilots. ✅ Early adopters emerge, but adoption is messy. 🔥 Challenges: ❌ No clear path to scale. ❌ AI tool sprawl—teams using different tools. ❌ No governance—security & compliance gaps. 👉 How to move forward: 📝 Empower Ops teams to lead AI initiatives. 📝 Standardize workflows—Centralize AI automation. 📝 Fix bad data first—AI is only as good as its inputs. 📈 Stage 3: Scaling AI & Automation ✅ AI moves from pilots to real workflows. ✅ Teams rely on AI for decision-making. 🔥 Challenges: ❌ Scaling AI across departments is HARD. ❌ Employees lack AI fluency. ❌ AI needs structured, high-quality inputs. 👉 How to move forward: 📝 Centralize AI workflows—Avoid silos. 📝 Train teams—Make AI practical for their roles. 📝 Use human-in-the-loop safeguards—Prevent automation mishaps. 🏆 Stage 4: Institutionalization ✅ AI is embedded across departments. ✅ Automation drives real-time decisions. 🔥 Challenges: ❌ Too much governance kills agility. ❌ Unclear when AI vs. humans should decide. ❌ AI evolves fast—hard to keep up. 👉 How to move forward: 📝 Balance automation & control—Define ownership. 📝 Monitor AI bias—Use AI observability tools. 🦾 Stage 5: AI as a Competitive Advantage ✅ AI is fully integrated into operations. ✅ The company operates with an AI-first mindset. 🔥 Challenges: ❌ Complacency—AI strategy must evolve. ❌ AI compliance is a moving target. ❌ Not everything should be automated. 👉 How to move forward: 📝 Continuously audit AI workflows. 📝 Keep humans in the loop for critical decisions. 💡 So… where is your company on this curve?

The AI shift no one’s ready for is coming fast. While digesting Sam Altman’s “Three Observations” essay over the weekend, I realized there’s a shift ahead that most business leaders aren’t prepared for. AI is already transforming how we work, but the real challenge isn’t what people think. Stage 1: Integration and Mastery Right now, we are in the phase of learning how to effectively integrate AI into workflows, products, and decision-making. The challenge isn’t access to AI. It’s execution. AI only creates value when it is seamlessly embedded into the way teams work. Otherwise, it’s just another tool sitting on the shelf. Most companies are in this phase. Experimenting with AI, adapting, and figuring out how to make it useful. This is what we’re working on at Dropbox. The hardest part isn’t adding AI features. It’s ensuring AI actually drives business impact. That means designing it to be reliable, invisible when it should be, and deeply integrated into how work gets done. Stage 2: Problem Definition and Prioritization Once we master integration, everything changes. Imagine having unlimited engineering resources. An infinite team that can flawlessly execute any well-defined problem. The constraint is no longer capacity. It’s about clarity. The winners in this next phase won’t just be the companies that adopt AI. They’ll be the ones that develop an exceptional ability to define the right problems to solve. Most organizations aren’t yet built for this shift. Today, prioritization is constrained by engineering bandwidth, forcing teams to focus on high-impact bets. But when execution becomes infinite, the ability to identify and articulate the highest-leverage problems, clearly and precisely, becomes the real competitive edge. For those of us leading product and strategy, that means: Now: Build the organizational muscle to effectively leverage AI. Next: Develop frameworks for identifying and defining the highest-value problems. Because in a world where execution is unlimited, clarity is everything. I’m curious to hear from others. Where is your company in this transition?

Traditional ML completely transformed media and advertising in the last decade; the broad applicability of generative AI will bring about even greater change at a faster pace to every industry and type of work. Here are 7 takeaways from my CNBC AI panel at Davos earlier this year with Emma Crosby, Vladimir Lukic, and Rishi Khosla: • For AI efforts to succeed, it needs to be a CEO/board priority. Leaders need to gain firsthand experience using AI and focus on high-impact use cases that solve real business pain points and opportunities. • The hardest and most important aspect of successful AI deployments is enlisting and upskilling employees. To get buy-in, crowdsource or co-create use cases with frontline employees to address their burning pain points, amplify success stories from peers, and provide employees with a way to learn and experiment with AI securely. • We expect 2024 to be a big year for AI regulation and governance frameworks to emerge globally. Productive dialogue is happening between leaders in business, government, and academia which has resulted in meaningful legislation including the EU AI Act and White House Executive Order on AI. • In the next 12 months, we expect to see enterprise adoption take off and real business impact from AI projects, though the truly transformative effects are likely still 5+ years away. This will be a year of learning what works and defining constraints. • The pace of change is unprecedented. To adapt, software development cycles at companies like Salesforce have accelerated from our traditional three product releases a year to now our AI engineering team shipping every 2-3 weeks. • The major risks of AI include data privacy, data security, bias in training data, concentration of power among a few big tech players, and business model disruption. • To mitigate risks, companies are taking steps like establishing responsible AI teams, building domain-specific models with trusted data lineage, and putting in place enterprise governance spanning technology, acceptable use policies, and employee training. While we are excited about AI's potential, much thoughtful work ahead remains to deploy it responsibly in ways that benefit workers, businesses, and all of society. An empowered workforce and smart regulation will be key enablers. Full recording: https://lnkd.in/g2iT9J6j

Adopting the latest technology alone won’t build an effective AI roadmap. Leaders need a thoughtful approach—one that empowers their teams and stays true to their values. Over the past few years, we’ve seen AI’s incredible potential, but also its complexity. Crafting effective AI strategies can challenge even the most seasoned tech leaders. To truly unlock AI’s value, we need to put people at the core of our roadmap. At RingCentral, we’ve made it a priority to envision AI in ways that benefit our teams, partners, and customers. Here are a few strategies my team has found essential for building human-centered AI: 1. Emphasize the “why” behind AI adoption: Start by identifying the specific needs AI will address. Help your team see the value of AI as a tool to enhance their work—not replace it. 2. Start with small, targeted wins: Choose use cases that tackle real challenges and show early success. These wins build trust in AI’s potential and create momentum for further adoption. 3. Prioritize transparency and ethics: Set clear guidelines around data privacy and responsible AI use, ensuring that team members feel they’re part of an ethical and trusted process. Guiding AI adoption with a clear, people-first approach enables us to create a workplace where innovation truly serves the people behind it, paving the way for meaningful growth. 💡 How are you approaching AI within your teams?

AI is hurtling towards us, and it's breaking our traditional approach to digital change. How do you think about building your company's AI muscle when the path forward isn’t clear? I got a chance to sit down with Richard Cotton to talk about just this on the DataCamp podcast. Here are some of the topics we covered: ▶ AI adoption represents a major cultural and mindset shift for organizations, not just a technological change. We need to think about how we respond with organizational mechanisms that support decentralized innovation and new approaches to learning and experimentation. ▶ Organizations should focus on accelerating their "speed to learn" rather than "speed to deliver" when it comes to AI adoption. This involves experimentation, iteration, and sharing learnings across the organization. ▶ One great place to start building AI muscle is use cases that have high potential value but still allow for significant human oversight. Areas like HR (internal support) and marketing are often good starting points. ▶ Data readiness is a critical and often underestimated challenge for AI adoption. Many organizations' unstructured data is not properly curated or governed for AI use. Some orgs will soon realize they are facing a new kind of data crisis as they are caught unable to really leverage the technology because their data just isn’t ready. ▶ Effective communication and storytelling about AI successes and learnings within the organization is crucial, and many valuable use cases go unshared. Find them and celebrate them. If you find problems that surfaced through the work, it gives you a chance to educate how to work through these and model responsible AI behavior. ▶ Top-down support from leadership is important, but grassroots innovation from employees is also key. The trick is balance of both approaches, and organizational mechanisms that support feedback, and education on and adherence to responsible AI principles. ▶ Organizations need to raise AI fluency across all levels, not just technical teams. This includes understanding responsible AI practices. *****I’ll also be sharing more about the opportunity ahead to Think with AI, so follow along for more, and join the conversation!*******