How AI Transforms Communication Strategies

Harvard research confirms CEOs waste 11 hours weekly on low-value communications: that’s 572 hours/year lost to CC chains, meeting spam, and FYI emails that should never have reached you. One thing I’ve observed coaching Fortune 500 leaders: your overflowing inbox isn’t just annoying, it’s systematically destroying your company’s strategic potential. When Shopify implemented their radical "No Meetings + AI Email" policy, the results shocked even skeptics: -87% decrease in internal emails (replaced by AI-summarized Slack threads) -23% faster decision cycles (by eliminating reply-all paralysis) -$18M saved in recovered productivity (The Wall Street Journal) Now, tools like Microsoft’s Copilot and Notion AI are taking this further by: - Auto-classifying emails into "Action/Archive/Delegate" (saving 2.1 hours/day) - Drafting context-aware responses using your writing style (tested at Deloitte with 91% approval from recipients) - Predicting which emails actually need your eyes (87% accuracy per MIT AI Lab) 3 Immediate Actions to Reclaim Your Time: 1️⃣ Implement the "3-Second Rule" Contains "FYI" or "for your awareness" CC’d unnecessarily (based on past interaction data) From senders you consistently ignore 2️⃣ Create a "CEO Whisper" System Like Amazon’s controversial "6-Pager Meetings", require all non-urgent communications to be: Summarized in 90 words or less by AI Formatted as yes/no/maybe decisions Submitted via dedicated Slack channel (not email) 3️⃣ Adopt "Voice-to-Command" Tools like Fireflies.ai now let you: Dictate responses while walking (transcribed + polished by AI) Auto-extract action items from rambling threads Generate meeting minutes before the meeting ends 70% of executive communications will be AI-mediated by 2025. The leaders who resist will drown in digital noise while competitors like Shopify reinvest those 11 weekly hours into strategy and innovation. The future belongs to executives who leverage AI not just for efficiency but for cognitive liberation. Your inbox shouldn’t be a to-do list; it should be a strategic filter. #meetings #communication #leadership #executivecoach #csuite

Your role changes when your AI teammates start working together. Most teams brief each Custom GPT teammate separately and lose context in handoffs. Trailblazing teams connect AI teammates so expertise flows seamlessly from positioning to content to campaigns. One conversation, full context. Harvard research with P&G professionals shows that when people work with AI, "you stop caring as much about the normal boundaries of your job." Connected AI teammates speed up this transformation. Your positioning expert's GPT works directly with your content expert's GPT. Knowledge flows where customers need it, not where org charts say it should. This week's newsletter explores: ► Why most teams get stuck using AI individually instead of as connected systems ► How to build your first AI chain that combines multiple areas of expertise ► The three phases of AI adoption and why Phase 3 transforms your role from doing tasks to strategic work and orchestrating AI systems ► Real examples of teams rethinking work around customer outcomes, not org charts ► How a GPT Navigator helps you pick the right teammates for any project Teams connecting their AI are working differently. They eliminate handoffs instead of managing them while discovering their jobs are evolving in the process. Work is shifting around what customers need, not what our org charts say. Chained GPTs show you what that future looks like. Read the full issue below. There's also a 16-minute AI podcast version in the comments for those who prefer to listen while multitasking. See link in the comments. Huge thanks to Angie Hill (SVP of Growth and Integrated Marketing at Procore Technologies) and Maggie Miller (Senior Director of Corporate Marketing at HackerOne) for sharing how chaining AI teammates is changing their approach to collaboration and strategic work. The infrastructure is here. Will you keep working with AI teammates individually or start chaining them together? Share this with your team and others to inspire them with this vision and approach to transformation.

If I were at a bar with a CIO or CEO, talking about how to prepare for an AI-first world, here’s what I’d say after a decade in communications and serving on an AI Council: The #1 barrier to AI adoption? Hands down.. behavior change. Most AI initiatives fail because they require employees to change how they work (the catch-22). But communications is already a workflow. People talk, message, meet, and collaborate.. it’s work as usual. This makes it an incredible foundation for AI that actually gets adopted. For decades, businesses have struggled with the same operational challenges.. >CRM records are always out of date. >Data hygiene is a constant uphill battle. >Teams rarely have full context. >Business intelligence is not trusted. Why? Because these processes depend on manual human effort. They require people to log calls, take notes, update fields, remember next steps… and they will always be a step behind reality. Instead of seeing communication as just calls and messages, treat it as an AI engine… a source of truth, never ending data source, that continuously feeds intelligence into your business. In genAI… the data you can feed it is what makes it purpose-built for your business. Sooo… >> Unify communications wherever you can, ideally into one or two governed platform. (Versus 5-10… which is very much the norm) >>Capture every multimodal interaction (voice, chat, video) with AI to build a living memory bank.. one that isn’t limited by human error, forgetfulness, or manual updates. >> Enable agentic workflows that trigger at the speed of conversations. This literally means.. Sales teams don’t have to update the CRM. AI captures the call, extracts insights, and updates records automatically. Customer support doesn’t scramble for context. AI surfaces past interactions, past tickets, and suggested responses instantly. Business intelligence isn’t lagging behind. AI transforms human conversations into structured, real-time insights. This is automation + augmentation. Communications isn’t just a pipeline connecting employees. It’s data. It’s intelligence. It’s action. Leaders who get this will operate on an entirely different level. The ones that don’t will be stuck in the past… moving too slow, always feeling like they don’t have enough budget for headcount, and never fully trusting the charts and graphs in their PPTs.

Earlier in my career, an executive coach helped me create a "Guide to Working with Carol-Lyn" document that dramatically improved how my teams understood my working style, motivations, and stress responses. (Lynn Rousseau, you changed my life 🙏 .) When I was asked to lead marketing for Dice, I wanted to share an updated guide with my team. I had a strong foundation from the deep assessments my previous experience provided, so to update the guide I turned to generative AI. Not only did I use ChatGPT to refresh my leadership guide, but I took it a step further: 1️⃣ I built a custom GPT that walks each member of my leadership team through creating their own leadership guide 2️⃣ This same GPT helps team members explore effective communication approaches with their colleagues by simply providing basic information about their teammates 3️⃣ I created a "Carol-Lyn Simulator" GPT that my team can interact with to prepare for 1:1s, get presentation feedback, or learn how to pitch ideas to me Building these custom GPTs was a good exercise, but all of this would have been a waste of time if I hadn't taken the next step: I introduced these tools to my team on a team call. I shared my leadership guide with them, and then asked them to leverage the tool to build their own leadership guide as pre-work for a team-building workshop. In the workshop, we shared what we learned about ourselves in the creation of the guides, experiences that illustrated our strengths and preferences. And most importantly as a newly forming leadership team, we talked about how we tend to show up when we're under stress. The AI helped me structure this experience, but the humans made it impactful. The feedback? One team member said it was like "getting a decoder ring along with a new boss." 😄 As marketing leaders, we talk about AI innovation constantly - but are we actually implementing it in our day-to-day leadership? This simple experiment has opened up new communication channels and given my team unique insights into working effectively together. I'd love to hear how you all are connecting humans and AI! #LeadershipInnovation #AIinMarketing #TeamCommunication #FutureOfWork

Prediction for how AI will be used to better reach and engage employees: ✨Hyper-Personalized AI Communicators✨ Imagine you’re a new employee at a large corporation. Part of your onboarding process will be taking an assessment to determine your personality style, communication preferences, learning style and cultural/social affinities. That assessment will create a Hyper-Personalized AI Communicator specifically for you. A super realistic AI character who will look, speak and sound like a real human being. Your Personal Communicator will interact with you via video and chat - not just sharing one-way messages but capable of live conversation. Every employee’s Communicator will be a bit different in appearance, style, energy and tone. All based around what will cultivate trust, make the employee feel comfortable, and convey authority without being demeaning. Imagine the variability in reaching and engaging a 25 year old woman working at an entry level desk based job in marketing vs reaching and engaging a 50 year old frontline manager who spends most of his day on the factory floor. The former may be matched with a Communicator who looks and sounds like her older sister. The latter may be matched with a Communicator who looks and sounds like my blue collar dad. When company communications, HR announcements or leader messages need to go out, they’ll be passed through these Personal Communicators so that every employee receives the message in a unique way that works for them. AI Communicators will highlight key points relevant to that employee’s specific role, answer questions and talk them through things. They’ll leverage a database of already approved core messaging to ensure everything shared perfectly aligns with the company mission, vision, values and strategic priorities. Over time, AI Communicators will learn about their employee partner personally as well. If that employee loves baking, the Communicator may occasionally share highly rated recipes. If the employee has a heart for animals, the Communicator may prompt the employee to take 5 minute breaks here and there to watch funny cat videos. These personal touches will only strengthen the feeling of connection between employee and Communicator, which will ultimately drive greater engagement and retention (the same way having friends at work keeps people in their jobs longer). And where does all this leave our communications leaders and teams? We’ll still be desperately needed. We’ll steer and shape the technology behind the scenes, adding human perspective and nuance along the way. There will still be a role for us in this new reality, but the role will be significantly different than it is today.

The rapid development of artificial intelligence (AI) is outpacing the awareness of many companies, yet the potential these AI tools hold is enormous. The nexus of AI and emotional intelligence (EQ) is emerging as a revolutionary game-changer. Here’s why this intersection is crucial and how you can leverage it: 🔍 AI can handle data analysis and repetitive tasks, allowing humans to focus on empathetic, creative, and strategic work. This synergy enhances both productivity and the quality of interactions. Imagine a retail company struggling with high customer churn due to poor customer service experiences. By integrating AI tools like IBM Watson's Tone Analyzer into their customer service process, they could identify emotional triggers and tailor responses accordingly. This proactive approach could transform dissatisfied customers into loyal advocates. Practical Application: AI-driven sentiment analysis tools can help businesses understand customer emotions in real-time, tailoring responses to improve customer satisfaction. For example, using AI chatbots for initial customer service interactions can free up human agents to handle more complex, emotionally charged issues. Strategy Tip: Integrate AI tools that provide real-time sentiment analysis into your customer service processes. This allows your team to quickly identify and address customer emotions, leading to more personalized and effective interactions. By integrating AI with EQ, businesses can create a more responsive and human-centric experience, driving both loyalty and innovation. Embracing the combination of AI and EQ is not just a trend but a strategic move towards future-proofing your business. We’d love to hear from you: How is your organization leveraging AI to enhance emotional intelligence? Share your thoughts and experiences in the comments below! #AI #EmotionalIntelligence #CustomerExperience #Innovation #ImpactLab

comms leaders: if you’re not already having these conversations with your executive team, you will be. ai isn’t just changing how we show up. it’s changing who gets seen. your press hits, your ceo’s posts, your landing pages, none of it matters if you’re not showing up in the systems that shape perception. this is what execs are starting to ask: – we landed great coverage. why didn’t it move the needle? – our founder is active on linkedin. why aren’t they showing up as a category leader? – how do we show up before someone even searches for us? they’re not asking for vanity metrics. they’re asking for relevance. and that means comms teams need a new playbook (and one that pivots quickly as technology changes). the model has changed: – traffic is being replaced by trust signals – reach is being replaced by retrievability – media hits are being replaced by multi-channel authority – keywords are being replaced by quotability you need to see what the internet sees. so here’s one thing to do this week: run a clean visibility check → open an incognito browser window → go to Perplexity.ai (do not use ChatGPT—it’s personalized) → type: "Who are the top companies in [your industry]?" "Who are the top thought leaders in [your space]?" "What is [your brand] known for?" "What are the best [solutions] for [problem]?" take screenshots. save what shows up, or doesn’t. this is what reporters, investors, analysts, and AI models are pulling from. not your social feed. not your media hits. not your internal positioning doc. if you’re not in those results, then you know where you need to start. and that’s what you bring to the next exec sync. #communications #artificialintelligence #prstrategy #aivisibility #executivecommunications #aiagency

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

Conversational AI platforms provide today's benchmark for self-service and AI-driven customer engagement. The core capabilities of these platforms span 4 areas: • Integrations with back-end systems, communication channels, and knowledge sources. • AI technologies for speech and natural language processing, understanding, and generation (NLP/NLU/NLG). • No-code conversation design environment. • Toolsets for defining, testing, and refining intents and entities. In just 18 months, GenAI has reshaped the conversational AI market. Platforms have undergone two rounds of evolution—sometimes requiring a complete rebuild of functions—and must keep pace with relentless innovation. A new generation of platforms is emerging, driven by key trends and evolving needs: 1) Proprietary NLP/U is no longer the differentiator—platforms must orchestrate best-of-breed AI models and enable the combination of multiple specialized models. 2) GenAI simplifies intent management, but a new toolset is needed to customize and optimize models beyond basic prompting and RAG. 3) Voice AI requires best-in-class speech-to-text, text-to-speech, and speech-to-speech to meet performance and experience demands. 4) Platforms need to support both transactional and informational interactions. 5) Deterministic workflows will dominate CX and sales in the short term, but autonomous agents will redefine application development. 6) Integration capabilities will evolve into orchestrated, agent-driven ecosystems with robust governance. 7) Platforms must manage context over longer conversations. 8) Orchestration must extend beyond interactions and AI to enable sophisticated AI-human collaboration. 9) Platforms need to enable faster iterations and continuous expansion of use cases The tension between disaggregating functions for independent evolution and assembling an expanding set of technologies makes it difficult to predict what platforms will look like in a few years. Not all providers will successfully transition—some, burdened by technical debt, will be forced to pivot toward specialized solutions. When evaluating platforms, the key is to define the flexibility you truly need and make tradeoffs accordingly. A purpose-built solution may be a better fit than a broad platform, allowing you to leverage the vendor’s deep domain expertise. But that doesn’t eliminate the need for rigorous validation of their technology stack and architecture. Given that 'platform' is a catch-all term in vendor messaging, it’s essential to cut through the noise and classify offerings accurately. As conversational AI evolves toward the orchestration of conversations, technologies, and human-AI collaboration, use these trends as strategic lenses to guide your decisions. Above all, prioritize openness to navigate this evolving landscape. I trimmed the article to fit this post; the full version is linked in the first comment. #conversationalai #ai #cx #salestech

When your most capable (and most expensive) minds spend half their day in meetings, but outcomes rely on memory, you’re essentially running your business on Snapchat, where communication is ephemeral and actionability hinges on recall. Agentic AI transforms ephemeral conversations into persistent, strategic assets that compound in value over time. Leaders can now extract insights automatically, generate agendas from past discussions, and invite only the necessary people, while ensuring the output is broadly accessible. Instead of vanishing into thin air, meetings become a searchable, mineable system of record that fuels organizational intelligence — whether or not you were in the room. https://lnkd.in/g5F4prwH via GeekWire, Mark Briggs, Read AI