How to Use AI in Sales and Service

Successful companies deploy AI to help their people create more value. Companies that fail deploy AI to avoid paying people to create value. Clients expect AI’s ROI to come from cost reductions, but bigger wins come from turning cost centers into revenue generators. A large airline client expected AI to reduce its customer service costs. We implemented AI to detect customer intent and deliver outcomes faster. Productivity improved, but instead of laying people off, we deployed a sales coach into select agents’ workflow. One model gives every customer a rating based on how likely they are to buy an upgrade and predicts the top upgrades to recommend. A second model generates a personalized pitch for the customer service agent to use. We ran a 3-sided experiment: 1️⃣ One group of customer service agents kept working on the AI intent-outcome augmented workflow. 2️⃣ A second group was given a generic script and discretion to pitch upgrades without the AI coach. 3️⃣ A third group was given the AI sales coach and discretion to decide when to accept its recommendations and which upgrade to pitch. After 3 months, the second group had an 8% upgrade pitch success rate, and the third group had a 31% success rate. In the first month, the second group pitched more upgrades than the third, but that switched in months 2 and 3. People do not immediately trust AI. They need to see it function reliably before they truly integrate it into their workflows and trust its output. Giving customer service agents discretion was critical for adoption. As the initiative scales to the entire customer service team, the airline expects to make significantly more money from upsells than it would have saved with layoffs. We reclaimed time with the AI intent-outcome agent and used the opportunity to create a new revenue stream for customer service. We found that when customers quickly go from “I have a serious problem,” to “Hello, thanks for calling support, how can I help?” to “Wow, that was an easy fix,” they’re more receptive to upsells. Businesses that win with AI are reorchestrating workflows and finding new ways to create value. Others don’t see these opportunities, so their only option is cost-cutting.

I wasted $47k testing 200+ AI sales tools so you don't have to. Here's the exact stack that took us to $6M ARR: 1,300+ AI sales tools exist in 2025. Most are unnecessary. Here's what you actually need: 1/ Accurate B2B data Data quality determines campaign performance. Everything downstream depends on this foundation. Your sourcing options: - Standard databases: LinkedIn Sales Navigator, Ocean.io, Apollo - Niche targeting: Openmart for local business focus - Custom scraping: Apify, Instant Data Scraper for specific requirements - Intent signals: Clay, Common Room - prospects showing buying behavior - AI agents: Claygent, Relevance AI, Exa, Linkup - automated prospect discovery 2/ Reliable data enrichment Valid contact information is non-negotiable. You need verified emails and phone numbers. Two approaches: - Point solutions: Prospeo.io, Wiza, LeadMagic - specialized tools - Waterfall platforms: FullEnrich, Clay - multiple data sources in sequence 3/ Engagement platforms - Email solutions: Instantly.ai - LinkedIn outreach: Expandi.io, Valley - Multi-channel: lemlist - email + LinkedIn 4/ Deal execution When prospecting generates consistent pipeline, you need a system to close those deals: - CRM: Attio, Breakcold for deal tracking - Intelligence: Attention, Momentum.io - call recording, CRM enrichment, next-step recommendations The strategic advantage comes from integration, not tool quantity. What's your latest stack addition? Want weekly breakdowns of the tools that actually work? Join 10,000+ reading getting our AI sales newsletter.

650. That’s the staggering number of companies offering conversational AI solutions for sales and service. The flood isn’t slowing: each week brings new entrants or announcements. A year ago, the market was already crowded; today, the latest wave of AI technologies has further lowered barriers to entry, fueling an unsustainable proliferation. Beyond the three hyperscalers, only a handful of providers have surpassed $100M in ARR. I spent the summer making sense of the mayhem. The result: nine categories mapped to the core jobs-to-be-done. Customer service and support solutions fall into four categories: • Virtual Agents. IVAs and their AI evolution operate across digital channels, handling transactional interactions and escalating to humans when necessary. • AI Answer Engines. These retrieve and format answers from knowledge bases. Generative AI has dramatically improved precision for informational inquiries. • Conversational IVR and Voice Agents. Voice remains complex; these agents primarily handle transactional interactions. • Conversational Engagement and Outreach Agents. These manage outbound communications across voice, SMS, and messaging channels, complying with regulations. Historically transactional, they increasingly enable dynamic engagement. Sales solutions are grouped into three categories: • Conversational Commerce & Concierge Agents. Mature agents replacing traditional chat with conversational experiences across pre- and post-sales. "Concierge" reflects their versatility in guiding customers seamlessly. • Autonomous SDRs (Sales Development Reps). Focused on complex B2B scenarios, they enrich and qualify leads, route them to sellers, and schedule appointments. Among the most mature AI applications for B2B sales. • Autonomous BDRs (Business Development Reps). These drive outbound sales motions where relevance is critical. Complex to implement and scale, they work best in highly targeted scenarios where personalization is flawless. Some providers span the full spectrum of service use cases and Conversational Commerce & Concierge Agents. Rather than duplicating them across categories, I group them under Conversational AI Platforms, relying on robust capabilities to design, deploy, and continuously improve applications and agents. Customer Support Automation is an emerging platform category, tailored for handling support requests and a natural fit for GenAI. These platforms deliver full resolutions when possible, automate workflows, and assist agents with context and guidance. It’s a mature use case for Agentic AI, with many providers publicly demonstrating transformative results. The visual landscape below captures this segmentation. A few vendors will emerge as true platforms, while others will focus on niches or become embedded in broader applications. The market remains in motion, and I welcome perspectives on what I may have overlooked. #conversationalai #agenticai #cx #salestech

AI is transforming productivity across industries but sales is still a frontier waiting to be unlocked. Bain & Company’s research shows that while generative and agentic AI are already freeing up hours of work in marketing and operations, adoption in sales is lagging behind. That’s surprising, because sales is one of the most time-intensive, high-impact functions and even small conversion gains can deliver outsized business results. For CMOs, CROs, and GTM leaders, the opportunity is clear: use AI to give sellers back time, improve decision-making, and boost win rates. And here’s what often gets overlooked: buyer-group expansion and engagement are two of the most powerful drivers of those win rates. The more effectively teams can identify, engage, and influence the full buying committee- the CIO, the CFO, the head of engineering, security, procurement- the greater the likelihood of advancing and winning deals. AI can now do this at a scale and speed that simply wasn’t possible before. AI in sales isn’t about replacing people, it’s about equipping them with better tools. The organisations that act early will be the ones to capture the biggest gains. At Thoughtworks, we’ve been actively working toward this vision. Our award-winning PerformanceAI agent removes the need for sales and marketing teams to click through endless dashboards and instead delivers insights in plain English, on demand. Less time on analysis and more time on insight and action. We’re also reducing manual work through automation, from data entry to intelligence orchestration. We’ve invested in tooling that mines sellers’ conversations across voice, email, and calendar to extract key signals, map the buying group, and match insights to the right accounts and opportunities. And in the AI era, adoption in sales has become much simpler. The technology runs quietly in the background rather than becoming another system sellers need to feed. When human input is needed, we’re moving toward a voice-first experience so no navigating complex CRM interfaces, and sellers can make updates on the go right after a client meeting. How is your team approaching AI in sales today? Let me know in the comments.

The Future of Business Efficiency In today’s competitive business environment, Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems are indispensable. They help streamline operations, enhance customer engagement, and provide critical insights. But as organizations grow, the need for smarter, more dynamic solutions becomes evident. This is where OpenSource AI comes in, transforming traditional business tools into powerful, intelligent systems. The Synergy Between OpenSource AI and Business Software OpenSource AI offers unparalleled flexibility and innovation, making it a perfect complement to ERPs and CRMs. By integrating AI capabilities into these platforms, businesses can achieve: 1. Enhanced Data Insights: AI can process and analyze vast amounts of data to uncover trends, predict outcomes, and provide actionable recommendations. For instance, AI-powered predictive analytics can help forecast sales trends or inventory requirements. 2. Automated Workflows: AI can automate repetitive tasks like data entry, report generation, or customer follow-ups, freeing up valuable time for employees to focus on strategic goals. 3. Personalized Customer Engagement: AI algorithms can analyze customer behavior to deliver hyper-personalized experiences, such as tailored marketing campaigns or product recommendations. 4. Improved Decision-Making: By integrating AI into ERP and CRM systems, businesses can make real-time, data-driven decisions, ensuring agility and accuracy. 5. Cost Efficiency: OpenSource AI solutions are often more cost-effective compared to proprietary alternatives, reducing barriers to entry for small and medium-sized enterprises. AI-Driven Enhancements - AI Chatbots in CRM: Automatically handle customer queries 24/7, ensuring high-quality service with reduced response times. - Smart Inventory Management: AI in ERP systems can predict stock requirements based on historical data, seasonal trends, and market dynamics. - Sales Forecasting: Leverage AI to predict sales patterns and allocate resources effectively. Getting Started with OpenSource AI 1. Choose the Right Tools: Explore OpenSource AI platforms like TensorFlow, PyTorch, or OpenAI’s APIs that align with your business needs. 2. Focus on Integration: Work with developers to seamlessly embed AI capabilities into existing ERP or CRM systems. 3. Iterate and Improve: Start small, measure the impact, and refine AI implementations to maximize benefits. The Road Ahead By embracing OpenSource AI, businesses can future-proof their ERP and CRM systems, ensuring they remain competitive in a rapidly evolving market. The combination of flexibility, cost-efficiency, and cutting-edge capabilities makes OpenSource AI an indispensable ally in driving business growth. Feel free to share your thoughts or ask questions in the comments below. Let’s explore the future of AI-driven business solutions! Subodh

GENAI + B2B = Five Key Lessons for Deploying Gen AI in B2B Sales 1. Start with the Problem, Not the Technology The decision to adopt #GenAI should be driven by specific business challenges, not by the allure of the technology itself. #B2B leaders must identify areas where Gen AI can drive significant, profitable #growth — such as #lead generation, account management, or service optimization. In some cases, simple automation might be more appropriate, especially where processes are still manual or error tolerance is low. The key is understanding the core business need before choosing the best technology to address it. 2. Keep the Seller at the Center Successful #GenAI #tools are designed around the needs of the sales team. Organizations should assess current workflows and look for ways Gen AI can free up sellers’ time or deliver valuable insights. Solutions should be: a) Impactful b) Clear c) Understandable d) Prescriptive e) Reliable If a #solution fails any of these criteria, it likely needs redesign. The more aligned the solution is with seller workflows and needs, the higher the likelihood of #adoption. 3. Buy the Easy Stuff, Build for Competitive Advantage Most companies use a “buy-plus-build�� approach to #GenAI. Off-the-shelf tools can be deployed for basic functions (e.g., #meeting summaries), while high-impact, differentiating use cases (e.g., personalized offers) benefit from customized solutions. The key is knowing when to buy vs. when to invest in building for strategic #advantage. 4. Balance Quick Wins with Long-Term Capabilities A clear #AIstrategy and scalable architecture are critical. Leading companies start with minimum viable products (#MVPs), align their AI efforts across the business, and build foundational capabilities like strong data infrastructure and skilled talent. The goal is to deliver near-term impact while ensuring long-term sustainability and #scalability. 5. Invest in Seller Adoption from Day One Technology alone isn’t enough—seller adoption determines impact. Organizations must prioritize change management, continuous #feedback loops, training, and communication. Involving sellers early, recognizing their successes, and encouraging experimentation can accelerate adoption. AI Centers of Excellence can help drive scale and responsible use across the organization. With these five lessons in mind, B2B sales leaders can turn Gen AI from a promising #concept into a transformative force for growth, #productivity, and competitive advantage - with Thiago F Silva - Inteligência Artificial e Gamificação e Herick Ferreira:

🤖 B2B Sales Just Got Smarter The rules of selling are shifting. AI is rewriting how buyers search, filter, and decide. The Internet is full of information. What it lacks is interpretation. For years, sales was about effort. More calls, more emails, more pipeline. But AI now does “more” faster than humans ever could. What it cannot do is connect. That is where sales professionals rise. The next era of selling belongs to those who build trust at scale. Buyers are no longer asking who has the best product. They are asking who they can believe. AI may open doors, but trust keeps them open. This is the age of Augmented Intelligence. It is not artificial. It is additive. It expands what sellers see, sense, and say. It gives context before contact. It helps us understand intent, not just behavior. Augmented Intelligence allows sales teams to prepare deeper and respond faster. It lets managers coach with clarity instead of instinct. They can see where conversations lose energy, where opportunities stall, and where buyers hesitate. Coaching becomes data-informed and human-delivered. Great sales managers will stop asking, “How many calls did you make?” and start asking, “What did you learn from the last one?” AI reveals patterns, but the manager’s job is to turn those insights into growth. That is the difference between tracking activity and developing ability. Client interactions are also evolving. Buyers come to the table informed, sometimes misinformed. Augmented Intelligence equips sellers to meet them with precision and empathy. It surfaces relevance in real time, guiding sellers to ask better questions, tell stronger stories, and build credibility faster. When sellers use AI wisely, conversations shift from transactional to transformational. - Instead of pitching, they co-create. - Instead of persuading, they clarify. - Instead of chasing, they lead. This is not the end of human selling. It is the beginning of intelligent selling. The human touch still closes deals, but now it is powered by sharper insight, faster learning, and better coaching. The most successful sales organizations will blend technology and psychology. They will train their people to think like strategists and speak like trusted advisors. They will measure depth of trust as carefully as revenue. AI gives us reach. Augmented Intelligence gives us resonance. Together they turn selling into a smarter, more human art. ✨ Follow me to explore how B2B sales and AI are converging, and how to stay ahead with clarity, confidence, and purpose.

Most sales teams fail with AI. They pick the wrong tool for the job. AI mastery is about matching the model to the moment. Let’s break it down. 1. Research AI and Reasoning AI are not the same. Top sales teams know the difference. They use each for what it does best. → Research AI is for facts. It finds the data you need, fast. Funding rounds. Hiring spikes. Leadership changes. New partnerships. Competitor moves. It delivers verified information. No guesswork. No wasted time. → Reasoning AI is for depth. It helps you understand context. Builds your ICP. Crafts personalized messages. Handles objections. Shapes your narrative and strategy. This is where insight and creativity win. 2. Mixing both is how you win more deals. Here’s the real playbook: • Use research-focused AI to gather signals. • Use reasoning-focused AI to turn those signals into messages that land. • Combine both to create relevance at scale, without sounding robotic. Examples: • Research AI finds a company’s new funding round. • Reasoning AI helps you write a message that connects that news to your prospect’s pain. • Research AI tracks competitor moves. • Reasoning AI helps you position your offer as the better choice. 3. The best teams orchestrate, not just automate. They map every step of their outbound. They pick the right AI for each task. They move faster, stay accurate, and book more meetings. Average teams stick to one model and stall out. Winning teams build a stack that fits every step. Mastering AI for sales is not about picking sides. It’s about building the perfect workflow for every job.

What Most Businesses Use AI For vs What They Should Be Using It For Let’s be real. Most businesses are using AI right now for small tasks: • Writing emails • Making social media posts • Summarizing meeting notes • Creating blog content • Answering basic customer questions That’s a start. But it’s just scratching the surface. If you want to grow, lead, and stay ahead, here’s what you should be using AI for: 1. Better Decision Making AI can help you look at data and find patterns. It can tell you what’s working, what’s not, and what to do next. Use AI to spot trends in customer behavior, marketing results, or sales performance. This saves time and leads to smarter choices. 2. Fixing Slow or Broken Systems Look at your day-to-day operations. What tasks take too long? What steps keep breaking? AI can help you speed things up, remove mistakes, and build stronger workflows. 3. Personalizing the Customer Experience People don’t want generic emails or messages. Use AI to understand what your customers really want and deliver it at the right time. That means better timing, better offers, and better service. 4. Helping Sales Teams Win AI can give your sales team the right words, answers, and ideas during calls and meetings. You can train AI with your best scripts and let it support your team with fast insights. It helps your team focus on people, not paperwork. 5. Creating New Ideas and Offers AI is great for brainstorming, testing new ideas, and helping you build faster. Want to try a new product or service? Use AI to explore the market, test messaging, and even build first drafts or prototypes. Here’s the real shift: Stop using AI just to get things done. Start using it to grow, lead, and innovate. AI is not here to replace you. It is here to help you think better, move faster, and focus on what matters. But it only works if you use it with intention. Play with it. Learn it. Break it. Improve it. Then bring it into your systems, your strategy, and your leadership. That’s how you win.

AI is no longer just a buzzword��it's transforming how dealerships engage customers, streamline service, and boost sales. But not all AI is created equal. The key to unlocking its potential lies in understanding agentic AI and its levels of autonomy. Think of it like autonomous vehicles (SAE Levels 0-5), but for intelligent systems driving your business forward. Here’s a breakdown to help you choose the right AI for your dealership: Level 1: Basic Automation (Rule-Based) Think automated email reminders for service due dates. These systems follow fixed scripts, offering efficiency for repetitive tasks but no flexibility. Great for starters, but limited in impact. Level 2: Partial Autonomy (AI-Augmented) A step up, these systems make basic decisions. Picture a voice agent handling inbound calls, routing them based on keywords like “service” or “sales.” It’s a time-saver for your team but still needs human oversight. Level 3: Conditional Autonomy (Agentic Assistant) Now we’re getting agentic! These systems plan and act with some independence, like qualifying leads by analyzing intent and suggesting test drives, though complex tasks (e.g., negotiations) may escalate to staff. Level 4: High Autonomy (Plan and Reflect) Dynamic and adaptive, these agents predict customer needs—like scheduling proactive maintenance based on vehicle data—and optimize operations, such as technician assignments. This is where revenue and efficiency soar. Level 5: Full Autonomy (Self-Refining, AGI-like) The future is near! These systems solve novel problems independently, learning in real-time. Think predictive inventory pricing that adjusts to market trends. Rare in 2025, but emerging fast. Where Are We Now? Most dealerships in Q1 2025 use Levels 1-3, with Level 4 gaining traction for customer-facing roles. Early adopters of Level 4 are seeing up to 30% higher appointment bookings and 20% reduced downtime. Why It Matters for Dealers Agentic AI can handle 24/7 inquiries, personalize customer interactions, and free your team for high-value tasks. Start with Level 2 for quick wins or leap to Level 4 to lead the market. The right choice depends on your CRM integration, budget, and goals. #AutomotiveAI #DealershipInnovation #AgenticAI #CustomerExperience