AI in Sales Transformation

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.

Just a year ago it was all about GenAI. Today the spotlight is on Agentic AI. What is driving the shift? 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 -  GenAI: Models that create or transform content in response to prompts. - Agentic AI: Systems that can pursue goals, plan tasks, and take action with minimal human input. GenAI helps generate ideas; Agentic AI takes action and gets things done. 𝗚𝗲𝗻𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 - Rapid content & pattern creation - Natural‑language front ends for analytics Finance examples • Auto‑drafted research and client letters • Multilingual regulatory summaries • Synthetic stress‑test narratives 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • Real‑time decisioning under uncertainty • Multi‑skill chaining (retrieve → reason → act) • Continuous learning from outcomes Finance examples • Millisecond fraud-blocking on millions of card transactions • Dynamic risk-rule tuning based on issuer feedback • Automated receivables follow-up and payment posting • Autonomous treasury operations: FX hedging and overnight liquidity management 𝗪𝗵𝘆 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 Moving from GenAI to Agentic AI fundamentally changes how financial services deliver value: • Real-time revenue protection: agentic systems can reroute or block high-risk payments instantly, slashing fraud losses and chargebacks. • Seamless customer journeys: fully automated KYC and onboarding flows. • Dynamic liquidity management: treasury bots rebalance cash, execute FX hedges, and optimize funding costs overnight. • End-to-end payment orchestration from choosing the most cost-effective cross-border rail to retrying failed pay-outs. • Regulatory agility: continuous-monitoring agents track rule changes, update compliance workflows, and generate audit trails without manual intervention. 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁 • Conversational banking agents: go beyond answering questions - initiate transfers, set up recurring payments, and negotiate loan terms. • Embedded Finance at scale: agents orchestrate lending, insurance, and FX in real time within non-financial apps. • On-demand cross-border settlement: smart agents choose between CBDCs, stablecoins, or traditional rails to settle payments instantly at the lowest cost. • Predictive risk & credit scoring: continuously update merchant and counterparty scores as new data streams in. • Auto-remediating systems: agents detect and fix platform issues in real time - no human ops required. • Automated regtech: agentic workflows handle licensing, screening, reporting, and audits - cutting compliance time from weeks to hours. • AI treasury market making: bots quote and underwrite liquidity in real time, adjusting spreads dynamically to market shifts. Opinions: my own 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

The agency world has 18 months left. Maybe less. Sir Martin Sorrell's S4 Capital just reported an 11.4% drop in Q1 sales - a canary in the digital coal mine. It's not just economic headwinds. It's not just Trump's tariffs. It's that your clients are quietly bringing work in-house. With AI tools. We’ve seen this very publicly over the past few weeks with Dualingo, Shopify and Klarna all taking an AI first approach. The reality today is: • A junior with Midjourney can replace multiple £50-£70K designers • AI can draft campaigns faster than your team (who’s reinventing the wheel every time anyway) • AI analytics outperform your insights department Sorrell, ever the optimist at 80, promises things will turn around in H2. But the trend is clear: brands don't need agencies for tasks they can do themselves with AI. So what's an agency to do? 1. Be brutally honest about what clients can now do in-house Don't sell services they can easily replicate with AI. 2. Double down on strategic thinking AI can't replace genuine business understanding. 3. Use AI in an advanced way Create demand by using AI tools to deliver more value. We've been teaching teams how to use AI effectively for 18 months. The results? They still need agencies - just for different things. The old model is dying: Creative concept → Design → Media plan → Traffic → Report The new model is emerging: Strategic insight → AI workflow design → Internal capability building → Outcomes Agencies need to go from fighting this shift to leading it. Follow me for more on navigating the changes in the agency and job market created by AI.

The more I spend time building HUMAIN, the more convinced I become that the age of traditional enterprise sales is coming to an end. Relationship selling alone is no longer enough. In the AI era, value realization and solution selling matter far more than simply pushing products, licenses, or features. Most enterprises today are struggling with one fundamental challenge: they know AI is important, but they do not know how to operationalize it or realize measurable business value from it. Many organizations are still trying to apply AI on top of broken workflows, fragmented data, outdated operating models, and heavy bureaucracy. The future sales organization must look very different. The next generation of enterprise sellers must become: - deeply technical, - operationally aware, - capable of workflow redesign, - capable of discovering hidden inefficiencies, - and able to connect AI to real business outcomes. The conversation can no longer start with technology. It must start with: - What business problem are we solving? - What operational friction exists? - What workflow should disappear? - What can become autonomous? - How do we redesign the enterprise around intelligence and AI agents? In many cases, customers themselves may not even fully understand the root cause of their inefficiencies. This is why the future seller is evolving into something entirely different: part technologist, part operator, part strategist, part transformation architect. At HUMAIN, this transformation has honestly been one of the hardest challenges for me personally. Building AI products is difficult. Building AI infrastructure is difficult. But transforming the mindset of enterprise go-to-market teams may be even harder. I spend a surprising amount of time reading messages that come to me on LinkedIn because I am constantly searching for people who think differently: builders, systems thinkers, operators, problem discoverers, AI-native minds, people obsessed with solving hard problems rather than simply closing deals. The future AI field organization will not look like the traditional sales teams of the past. And I believe the companies that figure this out first will define the next decade of enterprise AI.

There’s a secret trap MANY people fall into when using AI to create their presentations. After years of studying what makes presentations succeed or fail, I'm noticing a concerning pattern as leaders rush to adopt AI for their high-stakes communications. In 1964, media theorist Marshall McLuhan said, "The medium is the message." His framework helps us understand what happens when a new technology enters our lives. When I applied his Tetrad of Media Effects to AI in presentations, the pattern became clear . Here's what AI is doing to your presentation process: AI gives you a 24/7 thinking partner. Need headline variations for your product launch? Want to test different story angles for your board presentation? AI accelerates all of that exploration. You're no longer building in isolation. Like the ancient oral traditions, you can shape ideas through dialogue before they're polished. It's collaborative, iterative, and fast. This transforms your role from slide creator to story architect. Your job isn't to fill slides, but to shape the logical and emotional journey your audience experiences. But there's a dangerous trade-off emerging. I've watched brilliant leaders deliver AI-generated presentations that looked perfect on paper, yet completely failed to move their audiences to action. Their messages were efficient... but empty. Here's the trap: When your presentation arrives instantly through AI, you skip the mental friction that creates genuine breakthrough thinking. The quiet walk. The reflective pause. The deep consideration of your audience's specific needs. Without realizing it, you become reactive rather than purposeful. Your thinking is outsourced rather than enhanced. The most devastating consequence? Your audience feels it immediately. They detect the generic thinking. They sense the lack of true empathy for their situation. And they don't take action. The very tool that makes you faster can undermine what makes you persuasive. The solution isn't avoiding AI. It's using it while preserving four essential human capabilities: 1. Empathy: Deeply understanding your audience's context 2. Message: Testing for clarity and resonance 3. Visuals: Creating memorable images that guide understanding 4. Delivery: Bringing it to life through authentic presence Because every presentation that moves people to action still starts with human empathy, not algorithmic efficiency.

I spent the week trying to answer the question: How can I build a real estate development firm with zero human employees? After studying every AI tool in real estate, I found something surprising. Here's what would happen if machines did a developer's job: I can’t stop thinking about how close we are to this reality. From deal sourcing to cost estimation, there's now an AI tool for almost every step. So I designed a hypothetical real estate development company with zero employees: Asimov Partners. The vision: a real estate development firm that builds ground-up multifamily but has zero human employees doing any actual work. For this to work, we’ll use AI to cover: • Deal sourcing • Zoning analysis & test fit • Underwriting • Cost analysis • Buying the site • Raising capital • Permitting and construction • Leasing, management, and sale While the tech isn’t 100% there yet, here’s what I learned: What’s already possible: → AI can analyze thousands of sites simultaneously  → Tools generate floor plans and unit mixes automatically → Financial models build themselves from market data → Cost estimates update in real-time Where we’re stuck: → Lender negotiations still need humans → Construction coordination requires real relationships → Trade management can't be automated → Complex engineering decisions need human oversight The reality: • The most valuable application isn't replacing developers. • It's giving them superpowers to evaluate 100x more opportunities. Here's what this means for development: • Analysts focus on validation, not data entry • Best opportunities go to firms with best algorithms • Automation handles volume, humans handle judgment • Engineers focus on complex decisions, not routine tasks As Suffolk Construction's AI Director told me: “We’re nearly there with automation, but it’s not yet sufficient.” Melek is charged with researching and implementing AI at one of the largest construction contractors in the country, so he sees a lot. The future isn't automated development. It's augmented development. What parts of real estate do you think AI will transform first? Full letter on how I sketched out the build for Asimov Partners here: https://lnkd.in/ed4_gE9k

🤝 How Do We Build Trust Between Humans and Agents? Everyone is talking about AI agents. Autonomous systems that can decide, act, and deliver value at scale. Analysts estimate they could unlock $450B in economic impact by 2028. And yet… Most organizations are still struggling to scale them. Why? Because the challenge isn’t technical. It’s trust. 📉 Trust in AI has plummeted from 43% to just 27%. The paradox: AI’s potential is skyrocketing, while our confidence in it is collapsing. 🔑 So how do we fix it? My research and practice point to clear strategies: Transparency → Agents can’t be black boxes. Users must understand why a decision was made. Human Oversight → Think co-pilot, not unsupervised driver. Strategic oversight keeps AI aligned with values and goals. Gradual Adoption → Earn trust step by step: first verify everything, then verify selectively, and only at maturity allow full autonomy—with checkpoints and audits. Control → Configurable guardrails, real-time intervention, and human handoffs ensure accountability. Monitoring → Dashboards, anomaly detection, and continuous audits keep systems predictable. Culture & Skills → Upskilled teams who see agents as partners, not threats, drive adoption. Done right, this creates what I call Human-Agent Chemistry — the engine of innovation and growth. According to research, the results are measurable: 📈 65% more engagement in high-value tasks 🎨 53% increase in creativity 💡 49% boost in employee satisfaction 👉 The future of agents isn’t about full autonomy. It’s about calibrated trust — a new model where humans provide judgment, empathy, and context, and agents bring speed, precision, and scale. The question is: will leaders treat trust as an afterthought, or as the foundation for the next wave of growth? What do you think — are we moving too fast on autonomy, or too slow on trust? #AI #AIagents #HumanAICollaboration #FutureOfWork #AIethics #ResponsibleAI

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

🛠️🤖 How to build AI agents from scratch (Even if you've never done it before.) 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟵 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝘁𝗮𝗸𝗲, 𝗳𝗿𝗼𝗺 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝘁𝗼 𝗨𝗜. - - - - 𝗦𝗧𝗘�� 𝟭 - 𝗦𝗘𝗧 𝗬𝗢𝗨𝗥 𝗔𝗚𝗘𝗡𝗧'𝗦 𝗣𝗨𝗥𝗣𝗢𝗦𝗘 𝗔𝗡𝗗 𝗦𝗖𝗢𝗣𝗘 • Identify your target users • Clarify what deliverable it will produce • Define the specific task your agent will handle    → Example: Sales assistant that qualifies leads, researches prospects, and drafts outreach emails 𝗦𝗧𝗘𝗣 𝟮 - 𝗖𝗥𝗘𝗔𝗧𝗘 𝗖𝗟𝗘𝗔𝗥 𝗜𝗡𝗣𝗨𝗧/𝗢𝗨𝗧𝗣𝗨𝗧 𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 • Build structured data schemas using tools like Pydantic • Design API-like inputs and outputs • Avoid unstructured text responses    → Tools: Pydantic, JSON Schema, TypeScript interfaces 𝗦𝗧𝗘𝗣 𝟯 - 𝗪𝗥𝗜𝗧𝗘 𝗧𝗛𝗘 𝗦𝗬𝗦𝗧𝗘𝗠 𝗜𝗡𝗦𝗧𝗥𝗨𝗖𝗧𝗜𝗢𝗡𝗦 • Create detailed role descrip & behavioral guidelines • Use prompting techniques for reliable responses • Test different instruction formats    → Tools: Claude, GPT-4, custom prompt libraries 𝗦𝗧𝗘𝗣 𝟰 - 𝗘𝗡𝗔𝗕𝗟𝗘 𝗥𝗘𝗔𝗦𝗢𝗡𝗜𝗡𝗚 𝗔𝗡𝗗 𝗘𝗫𝗧𝗘𝗥𝗡𝗔𝗟 𝗔𝗖𝗧𝗜𝗢𝗡𝗦 • Implement decision-making frameworks like ReAct • Connect to external APIs and databases • Add web browsing, calc, file processing    → Tools: OpenAI Functions, Anthropic Tools, custom APIs 𝗦𝗧𝗘𝗣 𝟱 - 𝗢𝗥𝗖𝗛𝗘𝗦𝗧𝗥𝗔𝗧𝗘 𝗠𝗨𝗟𝗧𝗜𝗣𝗟𝗘 𝗔𝗚𝗘𝗡𝗧𝗦 (𝗪𝗛𝗘𝗡 𝗡𝗘𝗘𝗗𝗘𝗗) • Design agent teams with specialized roles • Build coordination logic between different agents • Create workflows for complex multi-step processes    → Tools: AutoGen, CrewAI, custom orchestration 𝗦𝗧𝗘𝗣 𝟲 - 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧 𝗠𝗘𝗠𝗢𝗥𝗬 𝗔𝗡𝗗 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 • Add conversation history tracking • Build knowledge retrieval systems • Store and recall relevant past interactions    → Tools: Vector databases, conversation buffers, RAG systems 𝗦𝗧𝗘𝗣 𝟳 - 𝗜𝗡𝗧𝗘𝗚𝗥𝗔𝗧𝗘 𝗠𝗨𝗟𝗧𝗜𝗠𝗘𝗗𝗜𝗔 𝗖𝗔𝗣𝗔𝗕𝗜𝗟𝗜𝗧𝗜𝗘𝗦 • Add speech processing for voice interactions • Support document and video understanding • Enable image analysis and generation    → Tools: Whisper, DALL-E, GPT-4 Vision 𝗦𝗧𝗘𝗣 𝟴 - 𝗙𝗢𝗥𝗠𝗔𝗧 𝗔𝗡𝗗 𝗗𝗘𝗟𝗜𝗩𝗘𝗥 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 • Structure outputs for both humans and systems • Generate reports, summaries, actionable items • Ensure results are properly formatted    → Tools: Markdown processors, PDF generators, structured data formats 𝗦𝗧𝗘𝗣 𝟵 - 𝗕𝗨𝗜𝗟𝗗 𝗨𝗦𝗘𝗥 𝗜𝗡𝗧𝗘𝗥𝗙𝗔𝗖𝗘 𝗢𝗥 𝗔𝗣𝗜 • Create web interfaces for user interaction • Expose functionality through REST APIs • Deploy as chatbots or integrated tools    → Tools: React, FastAPI, Streamlit, Gradio - - - - Ready to go further? I cover 10x more detail here: https://lnkd.in/eeey5Cxr 2025 is the year of AI agents. You got this 💪, Aakash P.S. What agents are you building?