How to Use AI Agents for Business Value Creation

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

AI Agents are quickly moving from experiments to real business tools. But the difference between a cool demo and a high-impact business agent is how you build it. That’s where The Copilot Studio Agent Playbook becomes valuable. It provides a simple yet powerful framework for designing, deploying, and continuously improving AI agents that actually deliver business outcomes. Here’s the sequence that matters: 🚀 1. Define the Growth Engine Start with the business problem. What will the agent improve — time saved, revenue generated, or customer experience? 🧠 2. Choose the Right Platform Select the environment where your users and data live — whether that’s Copilot Studio, Microsoft 365 Copilot, Azure AI, or other platforms. 📋 3. Craft Clear Instructions Define the agent’s role, tone, boundaries, and decision logic. Clear instructions = consistent responses. 🔗 4. Connect the Right Tools Agents become powerful when they can take action — integrate APIs, connectors, and workflows. 💬 5. Define Topics & Conversation Logic Structure how the agent understands intent and handles different scenarios. 🌐 6. Setup Knowledge Sources Connect documents, websites, or data sources so the agent retrieves reliable information. 🧪 7. Test & Evaluate Run real scenarios and edge cases before releasing the agent to users. 📢 8. Publish to Channels Deploy where people actually work — Teams, web apps, Power Apps, or custom platforms. 📊 9. Foster Continuous Feedback Monitor usage, collect feedback, and refine the agent over time. The key insight: Great AI agents are not built in one step — they are engineered as systems. As organizations move deeper into Agentic AI, frameworks like this will separate successful deployments from failed experiments. Curious to hear your thoughts: Where do you see AI agents creating the biggest impact in your organization?

AI is everywhere. But not all AI delivers real business outcomes. At Gong, we've built dozens of AI agents that actually move the needle. Here are 10 of my favorites: 1. AI Revenue Predictor Use case: Analyzes hundreds of signals from customer interactions to forecast deals with precision. Measurable outcome: Delivers forecasts informed by 100x more data points than CRM alone. Improves forecast accuracy significantly. 2. AI Deal Monitor Use case: Proactively identifies hidden risks surfaced from actual customer interactions. Measurable outcome: Provides deal-saving guidance in real time so you can prioritize deals most likely to close and course correct before it's too late. 3. AI Composer Use case: Personalizes outreach and emails instantly using context from all customer conversations and engagement data. Measurable outcome: Boosts response rates by eliminating generic templates and ensuring every touchpoint is relevant. 4. AI Tasker Use case: Optimizes rep activity by prioritizing the next best action required to move a deal forward. Measurable outcome: Increases deal velocity by enabling sellers to execute a prioritized workflow of high-impact tasks, ensuring zero wasted effort. 5. AI Briefer Use case: Ensures full alignment across the entire customer journey by equipping every team member with complete context. Measurable outcome: Maximizes conversion by eliminating friction and ensuring smooth handoffs from SDR to AE to CS throughout the customer lifecycle. 6. AI Builder Use case: Creates battle cards, playbooks, and sales content by analyzing actual customer conversations. Measurable outcome: Accelerates content creation and building winning strategies based on what top performers are actually doing. 7. AI Trainer Use case: Provides unlimited practice for reps to master difficult conversations before facing them live. Measurable outcome: Connects enablement efforts directly to revenue metrics like win rate and pipeline velocity. 8. AI Scorecard Use case: Automatically scores sales calls against your methodology and provides instant feedback to reps. Measurable outcome: Enables managers to coach at scale by identifying skill gaps and providing specific, actionable feedback tied to revenue outcomes. 9. AI Data Extractor Use case: Automatically extracts key information from conversations and writes it back to CRM. Measurable outcome: Saves reps significant time by eliminating manual data entry. 10. Theme Spotter Use case: Analyzes thousands of conversations to surface common themes, objections, and customer feedback. Measurable outcome: Provides actionable insights that drive product decisions, competitive strategy, and win-back campaigns. Bottom line? AI should do more than summarize calls. It should drive revenue. Improve forecast accuracy. Accelerate reps. And give leaders confidence in their numbers. That's what we're building at Gong. What AI capabilities are transforming your revenue org?

If you're using AI agents just to speed things up, you're missing their real value. Working with agents isn’t about shortcuts. It’s about designing collaborative systems that think with you. And this is how it should work: → Start with context Before you ask for outputs, define your goals, your audience, and the “why” behind your initiative. Agents perform best when they understand the bigger picture. → Design the workflow together Map out how agents and humans will interact. Who leads what? What tools are involved? What feedback loops do you need? → Only then, begin prompting This is where most teams start. But if you haven’t aligned on strategy, you’ll get fragmented results. At Mchange, we learned this the hands-on way. We had no background in marketing or content creation. But our AI agent team helped us build a content workflow from the ground up. It looks like this: → We set the mission: who we want to reach and why → We share that with our agents, often including docs, data, and vision → Together, we design the content flow and assign agent roles →Only then do we prompt for drafts, visuals, and distribution plans And the best part, The more we share up front, the more strategic and creative our outputs become. AI doesn’t just support our process, it teaches us how to improve it. Because when agents understand why something matters, they help you figure out how to make it matter more. That’s the real shift. AI inot as a tool, but as a thinking partner in your system. If you want deeper insights into how agent–human collaboration should look like DM me or book a call on our website. And remember, create value, not hype.

Here's what building AI agents for my business taught me. Most people are completely wrong about this. We've invested heavily in building internal AI agents at ColdIQ using n8n, and after months of testing, I need to cut through the hype. Here's what actually works (and what's just BS): 1️⃣ Non-Agentic Workflows (where you should start) This is basic AI usage. One input -> LLM Request -> One output. Done. Examples from our daily operations: - Prospect research: Input LinkedIn profile → Output enriched contact data - Email copywriting: Input campaign brief → Output personalized sequences - Call summarization: Input transcript → Output action items 2️⃣ Agentic Workflows (the sweet spot for revenue) This is where multiple tools work together in sequence, with some basic decision-making. Real example with a workflow we built: Goal: "Turn LinkedIn engagement into qualified pipeline" The workflow: → Monitors our LinkedIn posts for engagement → Enriches engaged users with Clay → Scores them against our ICP criteria → Routes qualified leads to different sequences → Triggers personalized outreach in Instantly → Updates CRM with context This runs 24/7 without human intervention. 3️⃣ True AI Agents (overhyped, underdeveloped) Here's the reality: Most "AI agents" are just fancy workflows with better marketing. Real agents should: - Understand context and make complex decisions - Learn from outcomes and improve over time - Handle unexpected situations without breaking We're building towards this, but the technology isn't quite there yet for most business use cases. The truth about AI agents: 99% of what people call "AI agents" are just well-designed workflows. And that's actually good news - workflows are reliable, predictable, and profitable. Don't get caught up in the hype. Focus on building solid automation that actually generates revenue. Start simple. Build workflows that solve real problems. Then gradually add complexity. Most companies wanna jump straight to "AI agents" and end up with broken, unreliable systems. What automation challenge are you trying to solve right now? Drop a comment - happy to share what's actually working.

Last week, someone asked me if all AI agents were basically the same - not really. Let’s clear a few things up … Agents are promising for SMB, but risk-prone for enterprise if not careful. There are 5 types of AI agents at their core —each with their own superpower, blind spots, and impact on the workforce. First you must know … AI agents don’t all replace people— they can reassign and restructure, but in some cases they require us. Here’s how: 1. Simple Reflex Agent Think: light switch. Sees a condition. Flips the switch. No learning. No memory. Use Case: A chatbot answering “What’s your return policy?” Great for the repetitive stuff. Not great for nuance. ✅ Do: Use for low-risk, high-volume tasks ❌ Don’t: Assume it’ll handle edge cases or emotional nuance 2. Model-Based Reflex Agent It builds a map of what’s around it and reacts accordingly - it “remembers.” Use Case: Smart thermostats that adjust based on room occupancy. Train teams to interpret the models behind the machine. ✅ Do: Assign ownership to oversee model behavior ❌ Don’t: Run it hands-off and expect gold 3. Goal-Based Agent Like a GPS—it doesn’t just react, it recalculates. Use Case: Optimizing inventory for sales targets. But AI doesn’t set your goals—you do. Set them wisely. ✅ Do: Teach teams to align KPIs with agent goals ❌ Don’t: Let it optimize for business metrics at the cost of human outcomes 4. Utility-Based Agent It weighs options. Then picks the “best” one. Use Case: Dynamic pricing engines that balance profit, demand, and customer loyalty. But Ethics teams must be in the loop—because “value” isn’t always financial. ✅ Do: Make the scoring system transparent ❌ Don’t: Blindly chase profit without customer trust 5. Learning Agent It evolves. It adapts. It makes mistakes and improves. Use Case: Netflix-style recommendation engines. Treat it like a junior teammate. Coach it. Audit it. Train it. ✅ Do: Build feedback loops across marketing, product, and analytics ❌ Don’t: Assume it “knows enough” once launched AI is evolving at 1% daily compounding but if your workforce doesn’t evolve alongside it, you don’t scale AI—you scale AI risk. Comment if this was helpful! 👇 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol was the world’s first Chief AI Officer (appointed 2016), has 10 patents, is a best-selling author of ‘Your AI Survival Guide’ (Top 50 AI Books in 2024), 3x TEDx speaker and Forbes’ “AI Maverick & Visionary of the 21st Century.” Sol is a former Amazon tech executive and a C-suite leader for Fortune 100 and played a pivotal role in launching IBM’s Watson in 2011. Image below … all of us trying to keep it together 🤣

𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐭𝐨𝐨𝐥𝐬 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. They’re starting to look a lot like digital employees. They can understand goals, take actions, use tools, learn from outcomes - and operate across operations, support, engineering, and marketing at machine speed. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐲 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐫𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐭𝐡𝐢𝐧𝐠𝐬 𝐥𝐢𝐤𝐞: • Admin and operations work • Customer support • Market research and analysis • Developer assistance • Sales and marketing execution They work continuously. They scale instantly. They never get tired. But here’s the reality most teams discover later: AI agents don’t truly understand context. They don’t have judgment. They don’t own consequences. This guide breaks down three critical parts: 👉 What AI agents can do today 👉 Where they fall short (judgment, ambiguity, accountability) 👉 The real risks — over-automation, security exposure, hallucinations, and workforce impact The biggest mistake companies make? Treating agents like decision-makers instead of assistants. The right approach is simple (but often skipped): Use agents to accelerate work — not replace human ownership. Set clear boundaries and permissions. Keep humans in the loop for high-impact decisions. Monitor, audit, and log everything. Train teams to collaborate with AI, not compete with it. AI agents don’t replace people. They reshape how work gets done. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

How AI Agents Will Reshape Private-Equity Investing and Portfolio Value-Creation Sequoia recently argued that the Agent Economy could dwarf the cloud era because intelligent agents don’t just serve software—they transact, decide, and learn autonomously. (https://lnkd.in/erCxk_Uk) For private-equity (PE) investors, this shift isn’t academic: autonomous agents collapse diligence cycle-times, unlock new operational levers in portfolio companies (PortCos), and create fresh investment theses around the infrastructure powering this new economy. In everyday PE dealmaking, multi-agent teams are already transforming operations: They're proactively surfacing potential investment targets by scanning everything from permit filings to talent movements and pricing anomalies. They run rapid red-flag diligence—quality-of-earnings bots reconcile P&Ls against raw ERP data in hours, not weeks. They model complex scenarios, rapidly spinning Monte Carlo LBO cases, freeing human teams to craft compelling investment narratives. At the portfolio-company level, layering in autonomous agents accelerates proven value-creation levers: SG&A Productivity: Agents handle finance and HR tasks—closing books, reconciling AP/AR, onboarding staff—often reducing back-office headcount by 20–30%. Margin Expansion: Dynamic pricing agents absorb win-loss data, costs, and demand signals to consistently raise ASPs by 2–5% without losing customers. Working Capital Efficiency: Inventory-management agents integrate POS, supplier lead-times, and external signals like weather data, typically freeing up 5–10 days of cash. Exit Storytelling: Operational data captured by agents supports an “AI-ready” growth narrative, significantly boosting valuation multiples at exit. Practical steps GPs can take now: 1.    Map PortCo processes by data readiness, regulatory complexity, and margin potential. 2.    Launch a sandbox: Pilot orchestration tools in one high-volume, low-risk workflow—think invoice management or accounts payable. 3.    Explicitly underwrite agent-driven value creation in deal models, treating SG&A and working capital gains as concrete synergies. 4.    Codify “agentability” in diligence—evaluate targets based on data richness, API accessibility, and ease of human-agent interaction. 5.    Invest in talent early, whether hiring an AI Operating Partner or collaborating with a Venture Studio for shared technical expertise. The Agent Economy isn’t just another wave of tech innovation—it’s fundamentally reshaping how work gets done. Private equity investors who embrace agents, from infrastructure to operational implementation, will capture substantial arbitrage at entry and exit. Those who wait risk investing in businesses whose economics may soon be automated away.

Everyone's racing to build AI agents. Very few are re-architecting how value will be created. Accenture's latest report on Agentic AI ROI shifts the conversation away from "agents replacing people" to how agentic systems will reshape the fundamentals of work, value, and competition. Agentic AI isn't about automation. It's about capital. Cognitive capital. It's not a tool you use… it's an asset you own. Machines that can think, decide, and act autonomously. Just as industrial machines multiplied physical labor, agentic systems now multiply cognitive labor... analysis, planning, judgment, coordination. Productivity is moving away from human effort and toward the ownership and orchestration of AI systems themselves. In the future, productivty won't come from hiring more people or buying more software. It will come from building scalable, thinking capacity inside the enterprise. Six truths about Agentic AI: 🔹 Agentic AI is shifting economic power from labor to capital 🔹 Early wins will come from the middle and back office 🔹 Incremental automation won’t excite investors,10x value will 🔹 Winners don’t scatter pilots; they design portfolios of high-value, connected agentic initiatives that compound results 🔹 Move fast where it redefines the market; partner where it doesn’t 🔹 Value targeting + enabling capabilities = the one-two punch for ROI But the real challenge with all of this? Identifying the value of it all. Implementation is complex but doable. Determining value? That's where most AI programs stall in "pilot purgatory." Here's Accentures seven-step lens to keep teams focused on value: 1️⃣ Define the new performance frontier. Look beyond today’s processes. Imagine your industry when agents handle the cognitive work. What changes, what disappears, and how “performance” gets redefined 2️⃣ Set the value frame Anchor on must-win challenges leadership already cares about... revenue leakage, time-to-market, satisfaction, cost-to-serve 3️⃣ Identify agentic value pools Find where agents can “hack” workflows... high-volume, high-friction, data-rich areas that drive loss or delay 4️⃣ Shape your enterprise AI portfolio Balance strategic bets (reinvention), table stakes (productivity), and quick wins (agentic automation) 5️⃣ Size the prize Quantify financial (cash flow, working capital, OPEX) and non-financial (experience, speed, sustainability) impact 6️⃣ Track and communicate value Instrument KPIs in near real time. Attribute outcomes to specific agents. Keep value visible and defensible 7️⃣ Boost and sustain value Adoption doesn’t happen by memo. Design behavioral change and AI literacy into the rollout. My thoughts? Agentic AI will define how we compete, not by who deploys first, but by who extracts value, measures it, and scales it deliberately. 💾 Save this for your next AI steering discussion. ➡️ Follow Darlene Newman for frameworks that make innovation stick.