Digital Transformation Strategies for Enterprises

According to the 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐂𝐈𝐎 𝐒𝐮𝐫𝐯𝐞𝐲 by Foundry, 𝟕𝟓% of CIOs find it challenging to strike the right balance between these two critical areas. This difficulty is notably higher in sectors such as education (𝟖𝟐%) and manufacturing (𝟕𝟖%), and less so in retail (𝟓𝟒%). (Source: https://lnkd.in/ebsed9i7) 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐄𝐱𝐢𝐬𝐭𝐬: The increasing emphasis on digital transformation and artificial intelligence (AI) is driving the need for innovation. In 2024, 28% of CIOs reported that their primary CEO-driven objective was to lead digital business initiatives, a significant increase from the previous year. This push towards innovation often competes with the imperative to maintain operational excellence, including upgrading IT and data security and enhancing IT-business collaboration. 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: The tension between innovation and operational excellence can lead to a misallocation of resources if not managed correctly. It can result in either stifling innovation due to overemphasis on day-to-day operations or risking operational integrity by over-prioritizing disruptive technological advancements. For instance, sectors with a high focus on operational challenges, such as education and healthcare, particularly emphasize IT security and business alignment over aggressive innovation. 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐂𝐈𝐎𝐬: • 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐚 𝐃𝐮𝐚𝐥 𝐀𝐠𝐞𝐧𝐝𝐚: Get used to it! CIOs should advocate for an IT strategy that equally prioritizes operational excellence and innovation. This involves not only leading digital transformation projects, but also ensuring that these innovations deliver tangible business outcomes without compromising the operational integrity of the organization. • 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐈𝐓 𝐚𝐧𝐝 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Strengthening the collaboration between IT and other business units remains a top priority. CIOs should work closely with business leaders to ensure that technological initiatives are well-aligned with business goals, thereby enhancing the overall strategic impact of IT. • 𝐃𝐞𝐯𝐞𝐥𝐨𝐩 𝐚 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: To manage the dynamic demands of both innovation and operational tasks effectively, CIOs should adopt a flexible resource allocation model. This model would allow the IT department to shift resources quickly between innovation-driven projects and core IT functions, depending on the business priorities at any given time. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

This is a 𝗺𝘂𝘀𝘁 𝗿𝗲𝗮𝗱 for anyone interested in the #FutureOfWork and the role that generative #AI #agents will play. Microsoft analyzed survey data from 31,000 people across 31 countries and combined insights with LinkedIn labor market trends, trillions of Microsoft 365 productivity signals, interviews with AI startups, academics, economists, scientists, and thought leaders to develop a picture of what the future of work may look like. There's so much to unpack - some quick highlights: - they envision "frontier firms" with hybrid human-agent teams - a new role for everyone: "agent boss" - ideal human-agent team ratios - "intelligence resources" emerges to manage digital labor at scale - "work chart" replaces "org chart" I recently posted about the need to rethink "human resources" when we now have non-human resources able to perform knowledge work. Microsoft believes some companies will blend HR and IT or create new leadership roles like "Chief Resources Officer" responsible for managing the optimal balance of human and digital/AI labor. Regarding the "work chart" concept, Microsoft's report shares that "Until now, companies have been built around domain expertise siloed in functions like finance, marketing, and engineering. But with expertise on demand, the traditional org chart may be replaced by a Work Chart - a dynamic, outcome-driven model where teams form around goals, not functions, powered by agents that expand employee scope and enable faster, more impactful ways of working." So much food for thought in this excellent report. Have a read, share with others, and please let me know your thoughts! This is without a doubt the most exciting time to be anywhere within HR. At the same time it is also deeply concerning, given that 33% of companies are planning on using AI to reduce headcount, and I think that this % will only increase. https://lnkd.in/eUBCPhw4

The future of work is here, and it’s a hybrid workforce of humans and trusted agents. Companies who are deploying AI agents to augment human employees are already seeing the limitless opportunities of digital labor. Take for example reMarkable, a company dedicated to bringing distraction-free digital writing to the world. reMarkable is growing at a rapid pace, and acquiring more customers than its human workforce can handle on their own. I sat down with Nico Cormier, the company’s Chief Technology Officer, to learn how Agentforce has eased their growing pains. reMarkable augmented their customer service reps with their first agent, “Mark”, who handles a large portion of incoming service requests and has even increased the company’s NPS score. After seeing Mark’s success, the company stood up its second agent, “Saga”, integrated into Slack, to enable more streamlined internal IT support for employees. With the help of Mark and Saga, reMarkable’s human employees can focus on higher-value, more strategic work, while digital labor takes care of routine and time-consuming tasks. The result? Increased efficiency, better customer satisfaction, and a more empowered workforce. With Agentforce, deeply integrated into Slack, companies like reMarkable are not just adding another tool to their arsenal; they’re creating a smarter, more intuitive way to work.

Is your leadership's management philosophy stuck in the 1960s? Let's redefine it: Leadership by Being Engaged. The concept of "management by walking around" came from Bill Hewlett and Dave Packard (HP founders) in the 1960s, popularized by Tom Peters in 1982, and gets used today to describe what's missing in #remote work. "The expected benefit: by random sampling of events or employee discussions, managers are more likely to facilitate improvements to the morale, sense of purpose, productivity and and quality... compared to remaining in a specific office area, or the delivery of status reports." The literal concept doesn't work if your managers have people who are working in multiple locations, now the majority case. 60 to 80% of all "enterprise" company managers now have #distributed teams. 100% of Fortune 500 Execs have teams that are #distributed today, according to Atlassian (kudos Molly Sands, PhD). #RTO mandates rooted in this philosophy are trying to return to a world that no longer exists. Leaders need a both/and approach. Get employees together to jump-start #belonging, and build better #culture and #performance by being involved in the digital #collaboration tools that your teams use every day. Let's redefine a philosophy rooted in co-location into one for the #digital age. Four starting points for leaders looking to get digitally engaged: 🔸 Increase transparency. Internal transparency around clear goals and realistic progress against them drives focus on outcomes, and builds trust. 🔸 Get engaged in the work. Execs need to stop saying "Teams/Slack etc are for the kids; you'll find me in email" and get into the tools people use every day to work through account issues, project updates, and problem solving. 🔸 Participate in digital communities. Social forums at work build belonging. That cuts across everything from an Abilities ERG to Sneakerheads. Finding community at work boosts retention; even leaders need to find that. 🔸 Get a reverse mentor. Being available and engaged digitally can feel foreign as a leader, and initially scary to a team. Find a digital native in your organization who can coach you! What's your take? Retire the phrase, or revive an important concept?

When you ask a consultant to “Help us discover our AI use cases,” you’ve handed them a blank check with an account the size of your AI budget. I say this as someone who runs an AI consulting firm: Don’t do that. When all you have is one hammer (AI), every opportunity and challenge looks like a nail. AI is a very expensive hammer that isn’t reliable enough for every use case. Instead, talk about your most significant opportunities and challenges. Then ask, “Where does AI create the most value in ways that align with our opportunities and challenges?” Businesses have several technology tools, and consultants should help them choose the best one for the job. Ask consultants to find AI use cases, and they’ll find excuses to buy and use that shiny new hammer as much as possible. A feasible, pragmatic approach to AI use case selection starts with the current business and operating models, but introduces a third strategic pillar: the technology model. Opportunity discovery begins with the questions: What parts of the business and operating model should we move into the technology model? (Business and operating model modernization) What parts of the business and operating model should we build for the first time in the technology model? (Business and operating model innovation/transformation) This approach is technology agnostic. C-level leaders don’t prescribe the technology to be used. Let feasibility and ROI determine that. Select the simplest technology that can support the opportunity and use case. Technical strategy is an evaluation of trade-offs. AI use case selection is a process of elimination. When the business has a high-value opportunity that no other technology can support, it’s time to evaluate AI’s feasibility.

Pairing teams and AI effectively is one of the most critical leadership opportunities of the next decade. Success won’t come from layering AI tools on top of existing systems. It will require thoughtful integration into real workflows, with a clear goal: empowering people to move faster, think more strategically, and deliver lasting impact. The future of work is one where AI and humans co-drive performance. Here are three strategies I believe will be key: ✔️ Let AI do the heavy lifting: Use it to triage, summarize, and surface patterns, then pass insights to humans for high-context decisions that move the business forward. ✔️ Start with high-impact roles: Embedding AI into areas like customer experience and sales creates fast feedback loops, measurable outcomes, and early wins that build crucial momentum. ✔️ Build smart handoffs: Design workflows where AI and humans stay connected through shared context, creating a loop that keeps people engaged, informed, and in control. The future of AI isn’t just technical. It’s organizational. And the leaders who get collaboration right will unlock unprecedented levels of clarity, velocity, and value. #AI #Collaboration #FutureOfWork

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

I've been diving into the latest AI Jobs Barometer from PwC, along with several recent articles. One thing is clear: AI is no longer just automating low-value tasks. 👉 𝐈𝐭'𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐯𝐚𝐥𝐮𝐞 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 𝐛𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐣𝐨𝐛 𝐝𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬. Some numbers that really stand out: • 3x faster revenue growth per employee in industries adopting AI. • +56% salary premium for workers with AI-related skills. • Required skills are evolving 66% faster than just a year ago. • All industries, even mining and agriculture, are now adopting AI. • "Automatable" jobs are not disappearing. They are evolving into higher-value roles. • Demand for degrees is falling, while demand for fundamental, current skills continues to rise. As Ilya Sutskever said, "AI will keep getting better, and the day will come when AI will do all the things that we can do." 👉 𝐖𝐞 𝐦𝐚𝐲 𝐧𝐨𝐭 𝐤𝐧𝐨𝐰 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐰𝐡𝐞𝐧 𝐭𝐡𝐚𝐭 𝐝𝐚𝐲 𝐰𝐢𝐥𝐥 𝐚𝐫𝐫𝐢𝐯𝐞. 𝐁𝐮𝐭 𝐭𝐡𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐜𝐥𝐞𝐚𝐫. Here are some key reflections for business and talent leaders: • The priority is no longer automating the past. It is rethinking how value is created in the age of AI. • Organizations that build trust in AI and take a strategic approach will lead. • AI is a powerful driver of productivity. But without strong investment in skills and role redesign, it risks increasing inequality and internal tensions. • Continuous learning is now a must to stay competitive. • Core skills need to be refreshed every 12 to 18 months to remain relevant. The future of work will not be managed. It will be fought for. Professionals and companies waiting for someone to hand them a playbook will miss the moment. 👉 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. 𝐈𝐭 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭 𝐬𝐮𝐫𝐯𝐢𝐯𝐚𝐥. And AI is not going to wait. P.S. If you're interested, here’s the link to the full PwC report: 🔗 https://lnkd.in/emeTQPVA #FutureOfWork #AI #TechTrends

Companies can miss the mark when they focus too much on technology and cost savings while ignoring user interface and experience. I went into a popular fast food restaurant which, like so many others, has opted for touch-screen kiosks in lieu of human order takers. There was not a single person at the front counter. Just a few people working appliances in the kitchen. A couple minutes later, an elderly couple entered through the front door. They walked toward the counter to order. One of the staff in the back called to them “you have to use the touch screen.” The couple went to the screen, looked at it for about ten seconds completely perplexed, and walked out the door without ordering. I speculate fast food restaurants will continue to invest in autonomous ordering. Soon it will be the kitchens too. What about automotive? I’ve been a loyal buyer of a certain foreign car-maker for 12 years. But the overwhelming focus on tech, automation, and proprietary computing has actually made the driving experience worse. So much so that I won’t be buying from this company again. Across food service, automobiles and elsewhere, revenue will be up and costs will be down. And customers who just want to transact — eating a meal or driving a car — may find somewhere else to buy. User interface matters. When tech improves capability and margins, but diminishes the user experience, companies risk losing those customers forever.

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange