How to Maximize Economic Value From AI Agents

A big question when building AI Agents for the enterprise is where the greatest amount of economic value is in AI Agents, which often ties directly to how differentiated your AI Agent is and your ability to monetize it. 1. For the most basic AI query or assistant experiences, the economic potential will mostly correlate to how proprietary the data is that your Agents are working off of. For pure public data this is harder to differentiate on and the productivity can be squishier; but the value can be expanded when the Agent has access to domain specific information, data from tools, or corporate knowledge, and especially where there are direct productivity gains that can be measured. 2. As AI Agents can execute narrow tasks, like reading documents and extracting data, typing ahead as you generate code for a project, or generating new content, the economic potential goes up quite a bit. These AI Agents will often need access to corporate data, have access to tools, and be able to work across multiple platforms. These Agents start to approximate the value of a discrete task inside of a business process, and thus their productivity can be directly measured. 3. Then, we'll have AI Agents that can execute entire workflows, like helping with client onboarding processes, reviewing and approving invoices, and more. The potential for economic value creation here is much higher as these agents will have access to critical corporate knowledge to do their work, often will be line of business and industry specific, contain proprietary context about their specific workflow, and tie into other existing software and agentic platforms. 4. Finally, when AI Agents act effectively as autonomous workers, this leaves the greatest room for economic value. Imagine an AI Agent that can complete an entire FDA submission process, or review and negotiate a legal contract for you, or code an entire application. These agents will be tuned to custom business processes, contain industry-specific knowledge, have access to proprietary data, often autonomously be able to use tools, and more. You'll be able to very directly measure their productivity in a business process. Ultimately, when AI Agents become near perfect over time (we still have a ways to go!), there’s almost no upper limit on their economic value. As models improve, and as Agents get more context, have proprietary data to work with, can access tools, and become more industry specific, they’ll become insanely powerful.

Assuming your firm still follows the practice of billing for time, you can run the calculations that will chart the eventual demise of your revenue model. If you’re like most firms, Generative Artificial Intelligence currently shaves somewhere between 20 and 30 percent off the time it takes to deliver work to your clients. What do you think that figure will be next year, or five years from now? Consider what kind of revenue stream will you have when time-tracking humans are doing only 5 or 10 percent of the work. Even the most hard-core defenders of hourly billing can see this compensation model is wholly unsustainable in the world of the AI-optimized agency. There is simply no way to monetize the value of AI within the framework of hourly billing. The solution to this dilemma requires agency professionals to remove the blinders that have them trapped in the illusion that they are selling time, efforts and activities to their clients. That’s not what clients buy; they buy solutions to their business problems. So the way to capture the value you create for your clients is to stop charging for the cost of your services and start charging for the value of your solutions. Every firm of every size can make this change much easier than they think. Instead of a chart of hourly rates, develop a chart of deliverables — a “pricing guide” that indicates the price (market value) of every deliverable your agency produces, and base your pricing on the work or solution delivered instead of the hours worked. In context of an output/outcome driven compensation model, it should be of no consequence to your clients that AI-powered tools are helping you create and produce your work. Again, they’re buying the outputs, not the inputs. So as AI helps you deliver your work faster and better, both parties benefit. Your clients get better quality work faster and the agency incurs lower costs — a win/win. Even if clients insist on slightly lower pricing (because they assume AI lowers the costs of your human capital), agencies can provide lower prices and still make a healthy margin on their work. In fact, agencies should be able to earn a much higher profit, even if they agree to lower prices, because AI is such a powerful force multiplier. It’s not inevitable that agency revenues will decline, because as AI continues to enable faster work, clients are assigning higher volumes of work to their agency partners. The result can be the best of both worlds: higher revenues from a higher volume of work, and stronger margins because AI is such an efficient virtual knowledge worker.

AI Agents Are Reshaping the Economy AI agents are driving massive efficiencies and unlocking new business opportunities today. These intelligent systems are cutting costs, boosting productivity, and accelerating decision-making. 🔹1. AI Agents in Content Creation Example: AI agents now write blogs for <$0.01, as seen with AgentStack & AgentOps, or even curate newsletters, like Jelani Abdus-Salaam’s AI-powered Best of AI newsletter. Economic Impact: Companies can cut content creation costs by 60-80%, scale output 10x faster, and grow their digital presence without hiring more writers. 🔹 2. AI Agents in Legal Lead Qualification Example: Dench(.)com by Mark Rachapoom is an AI-powered legal secretary that pre-qualifies leads for law firms. Economic Impact: Lawyers save 20-30% of their time by automating lead intake, boosting revenue by 15-25% and reducing intake costs significantly. 🔹 3. AI Agents in Web Research Example: Gumloop’s AI Web Research scours the web for answers, while Perplexity AI’s Deep Research Agent analyzes market trends like a McKinsey analyst. Economic Impact: Businesses can cut research costs by up to 90%, process 100x more data, and make faster, data-driven decisions. 🔹 4. AI Agents in E-commerce Optimization Example: AI agents now manage Shopify stores, optimizing product listings, customer support, and inventory. Hertwill even posted the first AI Agent job on LinkedIn. Economic Impact: AI can increase e-commerce revenue by 20-30%, optimize inventory management, and cut customer support costs by 50%. What's more in the future of agents?

Everyone seems to be talking about AI agents. The biggest problem not enough people are talking about: how to monetize & capture the value of them. Manny Medina, the founder of Outreach (last valued at $4.4B), just took his new startup (called Paid) out of stealth — and announced €10M in pre-seed funding — to tackle this very problem. After analyzing patterns from 60+ AI agent companies, Manny has put together a new framework for AI agent pricing. It's 🔥 — and here's the TL;DR. There are 4 AI agent pricing models dominating the market. Many companies are using just 1 of the 4; others take a hybrid approach. 1️⃣ Price per agent, aka the FTE replacement model See: 11x, Harvey, Vivun Why folks like it: You get to draw from the headcount budget which is at least 10x larger than the tech tools budget. Biggest challenge: Low competitive differentiation. This pricing leaves you exposed to “I-do-the-same-but-cheaper” competitors. 2️⃣ Price per agent action, aka the consumption model See: Bland, Parloa, HappyRobot Why folks like it: It is fairly easy to go after the BPO budget as well as other freelancing agencies with a higher performing offer with better SLAs and lower costs. Biggest challenge: Pricing per activity essentially makes you a commodity and prices only go down. 3️⃣ Price per agent workflow, aka the process automation model See: Rox, Salesforce, Artisan Why folks like it: It strikes a balance between consumption-based and outcome-based pricing, making it ideal for complex but standardized processes. Biggest challenge: If the workflow is complex, it will be hard to price and you may end up upside down with negative margin for a workflow that ran longer and you couldn’t charge for it. 4️⃣ Price per agent outcome, aka the results-based model See: Zendesk, Intercom, Airhelp, Chargeflow Why folks like it: This model creates the clearest value proposition for customers, as they only pay when they receive tangible results. Biggest challenge:  Outcomes may be highly customized which may lead to proliferation of bespoke contracts. And you need a clear path to attribute results to your agent. --- Get the full framework, including an epic decision tree, in today's Growth Unhinged newsletter here: https://lnkd.in/ed8g68wh Can't wait to hear what you think 🙏 #ai #aiagent #monetization