AI-Powered Virtual Assistants

The anatomy of a sales call has changed dramatically. Last week, I shadowed some of HubSpot’s top reps and what struck me was how differently the best sellers work today. They’re using AI at every stage: before, during, and after the call. And the results are real. The brain: before the call. AI does the heavy research — scanning 10Ks, news, emails, and past calls to surface the insights that matter most. Tools like Breeze Assistant can prep a full company overview in seconds. According to our State of Sales Report, 74% of sellers say buyers are showing up to calls more informed than ever before. Salespeople need to be just as ready. The heart: during the call. AI notetakers capture everything: next steps, budget mentions, open questions, so reps can focus on listening, not typing or scribbling notes on the side.  Also, AI assistants surface the right case study or testimonial in real time, making every answer sharper and every example more relevant. That means as a sales rep you are more engaged and relevant. The muscle: after the call. AI follows through fast. It drafts personalized follow-up emails in your own voice, outlines next steps, and flags what needs attention. More time with customers and less time writing emails. The result: sellers who prepare better, connect deeper, and close faster. The anatomy of a great sales call used to be manual effort and hustle. Now, it’s human connection powered by intelligence.

Interesting. This is how McKinsey & Co and Accenture are using AI agents internally, according to a Bloomberg article on the acceleration of corporate agent deployment. McKinsey & Co ➡️ Client onboarding McKinsey has deployed an AI agent to simplify and accelerate the client onboarding process. The agent manages tasks such as coordinating paperwork, sharing contact details, affirming project scope, and securing sign-offs from legal, risk, finance, and staffing departments. Previously a complex, time-consuming web of email threads requiring tens of hours, onboarding now takes roughly 30% of the time. By automating standardized yet intricate tasks, the AI agent ensures efficiency, reduces delays, and allows human employees to focus on higher-value work. ➡️ Data migration McKinsey is testing “squads” of AI agents that collaborate like human teams to streamline data migration from mainframes to the cloud—a traditionally laborious and costly process. The agent squad is trained to mimic the roles of key project staff, such as designers and data engineers, working together under human supervision. Early results show significant improvements, with migration timelines cut by more than half, demonstrating the potential for faster, more efficient, and cost-effective cloud transitions. ➡️ All-hands custom agent development McKinsey has launched an internal platform allowing employees to design their own AI agents using natural-language instructions, enabling tailored solutions for individual productivity needs. While this offers significant efficiency gains, safeguards are essential to prevent unintended risks. A central team ensures all agents comply with cyber, legal, risk, and data policies before deployment. This approach not only enhances productivity but may also shift work from offshored teams to smaller, highly skilled in-house teams empowered by AI agents, delivering greater value through expertise amplified by automation. Accenture ➡️ Assisting marketing department Accenture has deployed AI agents to assist its marketing team by enhancing efficiency and decision-making. Utility agents act as junior researchers, analyzing historical data and competitor insights to craft marketing plans or project event ROI. Strategic agents operate as team leaders, coordinating utility agents and sharing information like employees in regular check-ins. These agents not only streamline complex tasks but also support junior marketers by providing context-driven coaching and orchestration, reducing reliance on supervisors and enabling faster, higher-quality outcomes. There are some other interesting use cases and context in the article, gift link in comments.

Most people use Claude completely wrong. They’re treating every conversation like day 1 with a brand new hire. Unless you’ve set up memory, every time you open a new conversation, Claude starts completely fresh. Literally zero memory of what you told it yesterday. You're basically re-onboarding your AI assistant every single morning. And that time adds up fast. But you can avoid this just by creating a Claude Skill. You write instructions in the Skill once, and Claude already knows the exact steps, tone, format, and sources it needs to follow every time you run that task. Here's how to build your own in ~10 minutes: 1/ Create your Skills folder. Create a folder on your desktop. Then add a subfolder named after your task, like "newsletter-skill" or "research-skill." I have a skill for editing my newsletter. 2/ Create the brain of your Skill. Inside that folder, create a file called SKILL.md. This is what Claude reads every time it runs your Skill. 3/ Add a name and description at the top. This is how Claude knows when to use it. Keep it simple and specific. 4/ Write your instructions in plain English. Below the name, add everything Claude needs to know. Workflow, format, where to pull data from, tone, length. All of it. 5/ Test it on a real task and refine. Run it in a chat, check if the output matches what you actually wanted, and keep tweaking until it's right. 6/ Get started with one task you already repeat all the time. Summarizing meeting notes, writing in a specific format, prioritizing tasks. Build your first skill today, and let me know how it goes! 📌 Want a high-res PDF of this sheet? Get it here: https://lnkd.in/gKzZUq-b ♻️ Repost to help your network master Claude Skills ➕ Follow me (@Will McTighe) for more like this

Hey Salespeople: Here is a collection of current use cases for AI in sales & CS: ** GenAI in Sales ** --> Draft messaging for personalized email outreach --> Generate post-call summaries with action items; draft call follow ups --> Provide real-time, in-call guidance (case studies; objection handling; technical answers; competitive response) --> Auto-populate and clean up CRM --> Generate & update competitive battlecards --> Draft RFP responses --> Draft proposals & contracts --> Accelerate legal review & red-lining (incl. risk identification) --> Research accounts --> Research market trends --> Generate engagement triggers (press releases; job postings; industry news; social listening; etc.) --> Conduct role-play --> Enable continuous, customized learning --> Generate customized sales collateral --> Conduct win-loss analysis --> Automate outbound prospecting -->Automate inbound response --> Run product demos --> Coordinate & schedule meetings --> Handle initial customer inquiries (chatbot; voice-bot / avatar) --> Generate questions for deal reviews --> Draft account plans ** Predictive AI in Sales ** --> Score leads & contacts --> Score /segment accounts (new logo) --> Automate cross-sell & upsell recommendations --> Optimize pricing & discounting --> Surface deal gaps / identify at-risk prospects --> Optimize sales engagement cadences (touch type; frequency) --> Optimize territory building (account assignment) --> Streamline forecasting (incl. opportunity probabilities; stage; close date) --> Analyze AE performance --> Optimize sales process --> Optimize resource allocation (incl. capacity planning) --> Automate lead assignment --> A/B test sales messaging --> Priortize sales activities ** GenAI in CS ** --> Analyze customer sentiment --> Provide customer support (chatbot; voice-bot / avatar; email-bot) --> Draft proactive success messaging --> Update & expand knowledge base (incl. tutorials, guides, FAQs, etc.) --> Provide multilingual support --> Analyze customer feedback to inform product development, support, and success strategies --> Summarize customer meetings; draft follow-ups --> Develop customer training content and orchestrate customized training --> Provide real-time, in-call guidance to CSMs and support agents --> Create, distribute, and analyze customer surveys --> Update CRM with customer insights --> Generate personalized onboarding --> Automate customer success touch-points --> Generate customer QBR presentations --> Summarize lengthy or complex support tickets --> Create customer success plans --> Generate interactive troubleshooting guides --> Automate renewal reminders --> Analyze and action CSAT & NPS ** Predictive AI in CS ** --> Predict churn; score customer health; detect usage anomalies, decision maker turnover, etc. --> Analyze CSM and support agent performance --> Optimize CS and support resource allocation --> Prioritize support tickets --> Automate & optimize support ticket routing --> Monitor SLA compliance

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

Many healthcare organizations are trying to optimize their workflows without a clear strategy, and that’s where things can go wrong. While serving as the US Navy's chief medical informatics officer (CMIO), I learned important lessons about workflow optimization, strategy, and technology integration. Here’s the truth: Healthcare workflows are intricate and multifaceted. Without the right approach, there’s a risk of: ⏳ Wasting valuable time on redundant tasks 💸 Incurring unnecessary costs 😟 Compromising patient experiences But it doesn’t have to be this way. 🔍 Here’s what you need to know to streamline and optimize your healthcare workflows with AI: 1️⃣ Identify Bottlenecks. First, not all workflow issues are created equally. Some are more critical than others. → Start by pinpointing the areas where inefficiencies are costing you the most. 2️⃣ Leverage AI for Automation. AI can handle routine tasks like appointment scheduling and data entry. → Free up your staff to focus on patient care and complex decision-making. 3️⃣ Enhance Decision-Making with AI. Insights AI can quickly analyze vast amounts of data, offering insights that improve patient outcomes. → Use AI to support clinical decisions and personalize treatment plans. 4️⃣ Improve Communication Channels. AI-driven tools can streamline communication between departments and with patients. → Ensure everyone is on the same page, reducing errors and enhancing patient satisfaction. 5️⃣ Monitor and Adjust Regularly. AI is powerful, but it is not set and forgotten. Continuous monitoring and adjustments are key. → Regularly review your workflows and tweak AI tools for ongoing optimization. Healthcare is challenging enough. Don’t let outdated workflows add to the stress. With a strategic approach, AI can transform your healthcare operations, making them more efficient, cost-effective, and patient-centered. 👉 Are you ready to explore how AI can elevate your healthcare workflows? Let’s discuss the possibilities.

Are you finding exploring generative AI tools daunting? Sharing your successes – and stumbles – with others can help it feel less so. That’s why we gathered our global Mars Corporate Affairs function last week for the latest in our practical GenAI series, this time on a very important topic - improving the quality of Gen AI prompts. From adapting communication across channels or audience styles to team haikus, it was great to hear how our teams are already experimenting with these emerging tools creatively and, importantly, safely – and we rolled up our sleeves and tried different prompting techniques together on the call. I thought I'd share a few of our key takeaways as they may be useful for others:  * Prompt quality drives AI value: Crafting clear, specific prompts significantly improves AI output quality, reduces rewrites, and increases trust in results. Investing time in prompt creation upfront is a smart way to maximize efficiency.  * There are different advanced prompting techniques: We learned about shot-based prompting (zero, one, few-shot), chain-of-thought prompting (breaking down complex tasks), and prompt-priming (setting context and tone at the start) to enhance AI performance. * Consider a ‘prompt library’: There’s an art and science to developing great prompts. Consider banking reusable prompts across teams to save time and share best practices.  * Troubleshooting: Expect issues like hallucinated data, token limits and slow responses. Consider providing ‘escape routes’ in prompts (e.g. instructing the AI to say "I don't know" if unsure).  * Last but not least, keep the human in the loop: Today AI should augment, not replace, human judgment to review, refine, and validate AI outputs for accuracy, bias, and ethical considerations. Prompting by nature is an iterative process - it's normal not to get the perfect output on the first try; iterating and refining prompts through conversation with the AI leads to better results. But our best tip by far – just get stuck in.  Experimenting and sharing your learnings (in accordance with your company's safe Gen AI guidelines) is the best way to build these new muscles more quickly. Got a favourite prompt? Or other great tips in building capabilities in this area, I’d love to hear it. Big thanks to Camilla Vasquez, Katherine Horrocks, Ishtar Schneider and many others for being a driving force in helping to build our capabilities in this important area. #GenAI #CorporateAffairs

The era of AI tools is over. Welcome to AI teammates. We’re now building autonomous agents that operate like team members. These agents are more than personas. They're modular, trained, role-specific assistants that can: - Execute repeatable workflows - Interpret and adapt based on uploaded data - Hold persistent memory of your style, tone, or SOPs - Integrate with APIs, tools, and automation stacks Here’s how to leverage them strategically — not just play with them: ✅ 1. Treat your agent like you're hiring an ops lead Think in terms of delegation, not automation. Write a role description. Define its scope. Explain what “done well” looks like. The clearer the initial “onboarding,” the better the performance. ✅ 2. Build with process, not just prompts Upload reference documents (templates, decks, SOPs). Guide it through your systems and workflows. Remember: AI needs context to become competent. ✅ 3. Anchor it to a specific business function General assistants give general outputs. But an “Investor Memo GPT” or “Weekly Analytics GPT” gets to business faster. Function > title. ✅ 4. Use feedback loops aggressively Agents improve with structured input. Keep a running log of breakdowns, weak spots, and edge cases. Update your instructions like you would a knowledge base or playbook. ✅ 5. Operationalize with real stakes Move beyond play. Deploy agents where they reduce real friction: Client onboarding, lead follow-ups, performance reports, etc. Start with low-risk, high-frequency tasks. Then scale. This isn’t another toy. This is the beginning of a new interface between leadership and execution. 💡 Want to see the full framework I use to deploy GPT agents across sales, content, and research ops? 📩 Subscribe here to get it → https://lnkd.in/gCV3_Raw