How Agentic AI Drives Business Profitability

Revenue Management in the Age of Agentic AI When software becomes an autonomous decision-maker, pricing can no longer be static. Agentic AI is turning SaaS into a real-time revenue optimization engine. Traditional SaaS pricing: • Fixed subscriptions • Annual contracts • Pre-set tiers Agentic SaaS changes everything. AI agents now: Observe customer usage Predict willingness to pay Optimize price, bundles, and entitlements Adapt in real time This is Revenue Management meets AI autonomy. Instead of: “Which plan should we sell?” The question becomes: “What price maximizes value right now for this customer?” Agentic AI enables: Dynamic usage pricing Outcome-based fees Personalized bundles Real-time upsell & throttling Automated discounting & churn prevention Just like airlines and hotels moved from flat fares to yield management, SaaS also needs to move from licenses to AI-driven revenue optimization. In the Agentic era: Pricing is no longer a policy. It is an algorithm. And the firms that master it will own the profit pools of SaaS 2.0. #AgenticAI #DynamicPricing #RevenueManagement #SaaS #AIinBusiness #PricingStrategy

Operational excellence is a backbone of retail success. Agentic AI bolsters operational efficiency by bringing adaptive automation to everything from supply chains to store operations. Traditional automation follows predefined rules, but agentic AI is different- it adapts on the fly, learning from each interaction and outcome. This adaptability is vital in retail, where conditions change rapidly (think sudden supply disruptions or viral social media trends). We’re already seeing efficiency gains in AI-enabled operations. A recent industry study found that AI-driven “connected retail” solutions dramatically increase operational efficiency, in turn boosting profits while even reducing carbon footprint. For example, AI-driven route optimization in delivery can save fuel and ensure faster deliveries, while AI-based inventory management cuts down overstock and waste. Grocery retailers using AI to fine-tune ordering of fresh products have significantly reduced costly food waste even as they increase profit margins, a double win for business and sustainability. The power of agentic AI is that it doesn’t stop at insights- it sees tasks through to execution. In operations, this means an AI agent might detect an incoming snowstorm (perceive), infer that store foot traffic will drop and online orders will surge (reason), automatically reallocate inventory to the online warehouse and reroute delivery trucks (act), then observe the outcomes to update its storm-response playbook (learn). Each of these steps happens with minimal manual input. In fact, Boston Consulting Group reports that automation with AI can increase revenues by up to 5% in less than a year by finding these kinds of efficiency tweaks across the operation. When every percentage point of margin counts, AI’s ability to continuously fine-tune operations is revolutionary. #artificialintelligence #retailAI #agenticAI

𝟱𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗮𝗹𝗹𝘀 𝗼𝘂𝗿 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗺𝗮𝗸𝗲 𝘁𝗼𝗱𝗮𝘆 𝗮𝗿𝗲 𝗼𝘂𝘁𝗯𝗼𝘂𝗻𝗱. It turns out the real "Agentic" frontier isn’t just defensive support - it’s driving revenue. We’ve been surprised to see AI agents consistently outperform human benchmarks across several high-stakes metrics. Here is what we’re seeing on the ground: 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴): For an F500 home care provider, booking and confirming appointments is now a zero-touch workflow. By removing human coordination friction, they are saving millions by solving the "no-show" problem at scale. 𝗘𝗱𝗧𝗲𝗰𝗵 (𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗘𝗻𝗿𝗼𝗹𝗹𝗺𝗲𝗻𝘁): AI is moving beyond simple reminders to handle high-context enrollment conversations. One leading EdTech brand is now adding $1M+ in pipeline every week using AI Agents. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 (𝗧𝗵𝗲 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁-𝗙𝗿𝗲𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻): A counter-intuitive truth—many customers actually prefer talking to an AI about debt. It removes the social pressure and judgment, leading to higher recovery rates. One concierge healthcare provider is seeing 2X the collections compared to their human-led teams. The narrative on AI Agents is shifting. It’s no longer just about saving money on support; it’s about leveraging Agentic CX to drive top-line growth and revenue.

I recently had the opportunity to join Bain & Company's Winning with AI podcast to discuss how agentic AI is reshaping enterprise software and, more broadly, the economics of value creation. Over the past 25 years at Vista Equity Partners, we have navigated several structural shifts in technology, from on-premise to cloud to SaaS. Each required not only technical change but business model evolution. Agentic AI represents another such inflection point, with the potential to materially expand productivity, total addressable markets and long-term enterprise value. Today, across our portfolio of more than 90 enterprise software companies, we are focused on embedding AI into complex workflows, redesigning operating models and evolving pricing frameworks to reflect value delivered, not simply seats provisioned. Through our Agentic AI Factory, we are applying a disciplined, process-driven approach to help companies deploy AI agents responsibly and at scale. Sustainable advantage will not come from experimentation alone. It will come from leadership, organizational design and the willingness to evolve ahead of the market. I appreciated the thoughtful discussion and the chance to share our perspective. Listen to the full episode here: https://bit.ly/40GUQxI

Having worked on Agentic systems and open source multi agent deployments for last 2.5+ years (Starting with Langchain and moving to Langgraph, Autogen, Google ADK and many others) two common theme that has come across from deploying Agentic solutions and building using Agentic ecosystems in large scale production systems are - Theme 1 -> Agentic AI is shifting from experiment to enterprise backbone—but its ROI hinges on mastering cost containment and correctness at scale. In multi‑agent ecosystems, uncontrolled reasoning calls lead to spiraling spend with unpredictable accuracy (hence its very important to balance the choice of models, fault tolerant modes and accuracy metrics being defined both at Agent, Tool and Orchestrator levels). The winning CIO strategy is layered orchestration: lightweight models for routing, specialized LLMs for high‑value reasoning, and evaluator agents for correctness. This approach converts raw compute consumption into governed, auditable AI “throughput,” reshaping AI from an R&D cost center into a measurable productivity engine. Theme 2- > Standards like Google’s ADK and the Agent‑to‑Agent (A2A) protocol now provide the backbone for this transformation. By enabling a common language for agent communication, context‑sharing, and validation, they allow enterprises to modularize their AI stacks—aligning model choice with both business logic and budget. The value add: reduced infrastructure burn, accelerated time‑to‑market, and AI systems with built‑in accountability. For CIOs, this means a direct path to higher ROI curves where innovation can also take into account the true business value and cost of running these multi-agentic systems in production. #AgentAI #AgentOrchestration #CIOInsights #AIfrastructure #ADK #A2AProtocol

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.

Agentic AI is rewriting the rules of travel. Last week, McKinsey & Company and Skift released a report on AI in the travel industry. The core message? The value doesn’t show up when AI chats. It shows up when AI acts. Here’s what stood out: ✅90% of travelers trust AI for travel planning. ✅But only 22% say Gen-AI is used widely. ✅Agentic AI? Just 2% adoption. Why? Because agentic AI isn’t a chatbot. It plans, remembers, and acts across tools, data, and channels. Think doer, not suggester. Where the ROI shows up: ✅Dynamic bundling uplift jumps from 5–7% to 20–30% with real-time offers. ✅Load factor improves 3–4% with intelligent pricing. ✅Loyalty revenue jumps to 15–25% with better personalization, and analyst time drops 40–50%. Hotels win with faster maintenance, smarter check-in, and better triage. Airlines win on disruption recovery and proactive rebooking. For travelers, it means: ✅A concierge that actually resolves issues. ✅Context that follows you across every leg of the journey. Why it’s hard: Travel tech is fragmented. Legacy code, siloed data, no single identity, and brittle integrations slow everything down. The playbook: ✅Start with one high-ROI workflow. ✅Modernize just enough to make it work. ✅Pilot internally before going customer-facing. ✅Build cross-functional squads. ✅Instrument everything. ✅Govern from day one. ✅Train for the shift, humans move up the value chain. This isn’t a tech experiment. It’s a workflow rewrite. The travel industry doesn’t need more Gen-AI demos. It needs business-owned, outcome-driven deployments that move the needle. So here’s the question: What’s one workflow in your org that, if an agent could run it end to end, would unlock serious revenue or retention?

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐌𝐞𝐬𝐡: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐌𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐭𝐨𝐝𝐚𝐲 𝐚𝐝𝐨𝐩𝐭 𝐆𝐞𝐧𝐀𝐈, 𝐛𝐮𝐭 𝐦𝐚𝐧𝐲 𝐬𝐭𝐢𝐥𝐥 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐭𝐨 𝐬𝐞𝐞 𝐫𝐞𝐚𝐥 𝐢𝐦𝐩𝐚𝐜𝐭. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐆𝐞𝐧-𝐀𝐈 𝐩𝐚𝐫𝐚𝐝𝐨𝐱 𝐡𝐢𝐠𝐡 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐛𝐮𝐭 𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐑𝐎𝐈. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐢𝐬 𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐚𝐬 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐟𝐨𝐫𝐰𝐚𝐫𝐝. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐢𝐝𝐞𝐚𝐬: 𝟏. 𝐓𝐡𝐞 𝐆𝐞𝐧-𝐀𝐈 𝐏𝐚𝐫𝐚𝐝𝐨𝐱 - Widespread adoption, yet productivity gains remain elusive. - Horizontal tools are easy to deploy but benefits are diffuse. - Vertical use cases show high impact, but less than 10 percent of pilots reach production. 𝟐. 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 - Agents can plan, act, remember, and integrate across multiple systems. - Moves beyond reactive prompts into autonomous, goal-driven operations. - Minimizes human input while maximizing adaptability and execution. 𝟑. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 - Automates end-to-end tasks across systems. - Reduces manual intervention with proactive problem-solving. - Creates seamless workflows that adapt in real time. 𝟒. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭 - Productivity improvements between 20 and 60 percent. - Faster decision-making drives agility. - Opens new revenue streams through automation. 𝟓. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐑𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐞𝐝 - Finance: Faster credit decisioning. - Call centers: Streamlined service resolution. - Complex processes simplified into adaptive systems. 𝟔. 𝐓𝐡𝐞 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐌𝐞𝐬𝐡 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 - Enables large-scale collaboration between agents. - Designed for flexibility, scalability, and interoperability. - Built-in governance and orchestration ensure safety and adaptability. 𝟕. 𝐂𝐨𝐫𝐞 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 - Composability: Easy integration of diverse agents. - Distributed intelligence: Cooperative multi-agent problem-solving. - Governed autonomy: Freedom balanced with embedded safety controls. 𝟖. 𝐄𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 - Memory, planning, orchestration, and integration. - Human-AI interfaces for alignment. - Vendor-neutral standards to future-proof systems. 𝟗. 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐏𝐢𝐥𝐨𝐭𝐬 𝐭𝐨 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 - Requires infrastructure, governance, and workforce readiness. - Cross-functional squads, not isolated AI teams, must drive adoption. 𝟏𝟎. 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐈𝐦𝐩𝐞𝐫𝐚𝐭𝐢𝐯𝐞 - CEOs must align technology, strategy, and workforce. - Competitive advantage now depends on embedding AI as process co-architects. In simple terms: Agentic AI turns GenAI from isolated experiments into enterprise-scale transformation by making workflows intelligent, adaptive, and revenue-generating. If you were leading adoption in your company, would you prioritize productivity gains, agility, or revenue growth first?

This piece argues that the earnings “paradox” of enterprise AI—ubiquitous pilots, minimal P&L—won’t be solved by prettier prompts but by agentic AI: autonomous, multi-step systems that execute end-to-end business processes. The path forward is an Agentic AI Mesh—a governed architecture for orchestrating agents with clear identity, access, audit, and context—so companies can reinvent workflows rather than merely optimize legacy ones. Success hinges on trust-by-design, human–AI collaboration, and hard guardrails to prevent shadow agents, with adoption measured by business KPIs (cycle time, cost-to-serve, error rates) instead of demo dazzles. The mandate for CEOs: move from experiments to process-level transformation under governed autonomy. #AgenticAI #GenerativeAI #AIInnovation #DigitalTransformation #CEO #FutureOfWork #AIaaS

Why AI ROI Is Elusive—and How Agentic AI Will Change That: If your AI investments aren’t paying off, it’s not because AI is overhyped. It’s because you are not going far enough. A lot of peers that I meet talk about how AI investments are not paying off…that the ROI is just not there. More often than not the use of AI in their organizations is primarily via Copilot and ChatGPT. These are great tools to save time and produce better content but these personal productivity gains plateau and are not harvestable…and the tools are expensive. They improve the quality of life for employees but don’t drive real ROI. The same is true for coding tools. They are good accelerators for junior developers and for generating boiler plate code but are not driving meaningful gains beyond that (at least not yet). AI Agents are better at driving measurable value. They move beyond personal productivity to helping shoulder real parts of the employee workload. Self-service customer chatbots are a great example of this. Still, these too plateau because of their limited scope. What will truly drive value is Agentic AI. It is a coordinated system of AI agents that can execute a business process end to end autonomously. Instead of lone worker bee, think of it as a hive that can work towards a shared objective. For example, a Claims processing Agentic AI system that can ingest and process documents, perform damage assessment/reserving, update internal systems and issue payouts. That is where the value lies.