Key Shifts Shaping AI Leadership

In the past two weeks, I’ve met with leadership at NiCE, Amazon Web Services (AWS), Zoom, and 8x8. There’s a pattern - here are some reflections on these conversations. AI is eating the enterprise communications playbook for breakfast. If you're not paying attention, you're already behind. Key assumptions that have guided the industry for decades are rapidly becoming obsolete in the age of AI. Here are SIX critical shifts occurring: ONE: Voice is the New UI. Remember “My voice is my passport” as a security phrase, now it’s my voice is my keyboard. APIs are old school. Enterprise-wide, applications and integrations will be voice-enabled from meetings (with AI scribes) to customer service. The future is frictionless, voice-first interactions and integrations, multilingual, and without code. TWO: Mind Your Data: AI without contextual data is like a kiss without a squeeze. Every data repository is a treasure trove, and new moats protect the repositories instead of business practices. Examples include Microsoft putting up CAPTCHA to access Teams meetings and Slack locking down its customers’ data. The new browser wars are unconcerned about eyeballs. THREE: Workflows are the New Apps. Forget simple automation. We're entering the era of AI-native automation, where AI handles complex workflows that require judgment. An AI that doesn't just listen to a customer call but understands the intent, updates the backend systems, and routes the follow-up autonomously. #GameChanger. FOUR: Bottlenecks Be Gone: The modern workplace has largely been throttled by human bottlenecks, and these bottlenecks will disappear. We see this first with code generation; developing new code is becoming the fastest part of a project. Other bottlenecks are various barriers to decisions, such as data collection and analysis. We are moving from concept to code to scale in days, not months or years. FIVE: Soon This Will Matter: As disruptive and consuming as AI has become, none of this matters, yet! That’s because AI isn’t that useful, yet. We are in the Scantron era again. Scantron bubbles revolutionized paper scoring. A good step, but digitization is what mattered. AI is automating existing workflows. The real stuff comes in the reimagination of work. The first glimpse of this is in agentic AI. Focus on outcomes, not processes. SIX: The Barriers to Entry are Changing: The comms sector has enjoyed numerous barriers to entry over the decades, and most of them are disappearing. AI is simultaneously commoditizing and enabling competitive advantage. Giants may or may not fall, but their businesses will radically change. The Giants Cometh. #AI #FutureOfWork #EnterpriseCommunications #VoiceAI #Automation #Tech  #UCaaS #CCaaS Tanya (Blackburn) Shuckhart John Sun Megan Donaldson Schevone Johnson

For the last two years, the conversation about AI's impact has been dominated by the visible, the tactical, and the immediate. We are focused on new tools, evolving job descriptions, and the race to upskill our teams to use them. This is the "Surface Wave." It is real, it is important, and it is consuming all of our attention. But the real story, the one that will determine the winners and losers of the next decade, is happening beneath the surface. The "Undercurrent" is the deeper, strategic, and often invisible re-architecting of the organization itself. It's the shift in power structures, the creation of new data ecosystems, and the fundamental change in how decisions are made. This integration of human and machine intelligence is creating a new organizational physics, and most leaders are still using an old map. Think about it: A company builds a strategic intelligence unit designed to be "AI-native". The "Surface Wave" is giving the human analysts a suite of powerful AI tools for market research and data synthesis. But the "Undercurrent" emerges when the AI is integrated not as a tool, but as a de facto member of the team. Suddenly, the org chart is no longer a simple 2D hierarchy. You have a hybrid entity where the AI directly feeds insights to every team member, bypassing the traditional top-down flow of information from a human manager. The AI might even be given a "voice" in strategic meetings, presenting conclusions that directly contradict the team leader's intuition. The challenge is no longer about adopting a tool. It becomes a profound question of organizational design and leadership. What is the role of a human leader when the AI can provide more comprehensive data-driven direction? How do you manage a "team" that is a fluid network of human and machine cognition? And how do you measure performance when the most valuable output is a collaborative insight that is impossible to attribute to any single human or algorithm? This is the real transformation, and it requires leaders to move from being managers of people to being conductors of a complex, hybrid intelligence. I strongly feel that leaders who cannot distinguish between the two waves will be pulled under. But will organizations invest in the foresight this requires? #FutureOfWork #AIStrategy #TwoWaveTransformation

𝗕𝗲𝘆𝗼𝗻𝗱 𝗠𝗟 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝗧𝗿𝘂𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗿𝗲𝗮𝘁𝗲𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲 Two years ago, I witnessed a pivotal moment. Two competitors in the same industry launched AI initiatives with nearly identical budgets. Today, one has transformed its market position while the other quietly disbanded its AI team. The difference wasn't talent, technology, or timing. It was the presence of true AI leadership. After guiding AI transformations across multiple sectors, I've observed a clear pattern: organizations conflate technical implementation with strategic leadership — a costly misconception in the algorithmic age. 𝗧𝗵𝗲 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗶𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Most executives approach AI through a traditional technology lens: selecting vendors, implementing solutions, and measuring ROI. However, organizations creating asymmetric returns operate from a fundamentally different framework. When I joined a life sciences company's transformation, they had invested $15M in ML capabilities with minimal impact. Within 18 months of shifting to an AI leadership approach, those same technical assets drove a 28% market share increase in their core business line. 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 True AI dominance emerges at the intersection of three capabilities most organizations develop in isolation: 𝟭. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Redesigning core business processes around algorithmic decision-making, not just augmenting existing workflows. One healthcare organization restructured its entire patient journey based on predictive insights, creating a competitive moat its technology-focused competitors couldn't replicate. 𝟮. 𝗗𝗮𝘁𝗮 𝗦𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Moving beyond data volume to data uniqueness. The market leaders I've worked with systematically identify and capture proprietary data assets that create algorithmic advantages that are impossible for competitors to match, regardless of their AI investment. 𝟯. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆: Implementing governance models built for algorithmic speed. One financial services firm reduced model deployment from months to days, allowing them to capture temporary market inefficiencies before competitors could respond. The organizations achieving market dominance are those with leadership capable of orchestrating these dimensions simultaneously. Have you observed this leadership gap in your industry? 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.