Latest news with #IanGotts
Forbes
23-07-2025
- Business
- Forbes
Reinventing Your Business For Agents
Ian Gotts, founder and CEO at While it seems like every industry will be affected in some way by agents, for the first time, the scale of a business is not a defense. Startups can disrupt the incumbents with a relatively small team when they look at a problem from an agentic perspective and design their business to exploit AI. And AI is helping them build these AI-first businesses. Agentic-First Businesses Jason Lemkin built a business that generates $25 million ARR with just five employees. And it could be bigger, by his own admission, if he were more ambitious. That business is SaaStr, a community of SaaS executives and entrepreneurs, and Lemkin completely redesigned every aspect of it to use AI. For his annual conference, he gets thousands of presentation submissions. AI analyzes them, selects the top ones and builds a conference schedule. It also builds the session blurb, promo artwork and bio of each speaker for the website. At the event, each session is recorded, and AI analyzes the talk and writes the summary. All of this is faster, cheaper and more accurate than the teams he used to outsource to. Agentic Processes Are Not People Processes There is a risk that people see agentic as "Your mess for less." They take an existing business process and put an agent on top of it. It may be cheaper and faster. It may be more accurate. But those business processes were designed with the limitations of a human: time, brain power, interest: • They can't read 200 pages of background material and absorb it. • They can't take data from four disparate systems and make sense of it. • They won't keep looking at the problem 100 different ways to get the best answer without getting bored. • They haven't read the latest email on updated policies or pricing. • They apply natural biases based on emotion, relationships or because they are "having a bad hair day." Redesign To Lean Into AI AI is not perfect, nor is it magic. The results are based on the quality of the prompting, the accuracy of the data and how you evaluate the results. Notice I said "evaluate." You can get AI to tell you how confident it is in the answer. So you need to step away and then come back to redesigning your business processes so that you can exploit the massive analysis power that AI has to offer. In that same podcast, Jason says that he's given all his writing, podcasts and presentations to an AI engine not because it gives better answers than Jason would but because it remembers everything. How can you apply this to customer success, HR, legal and marketing processes? Where can agents have the greatest impact? In a recent presentation at Salesforce's London DX conference, one session showed how AI can look at a Salesforce configuration, draw the process diagrams for an area and then identify agentic use cases based on a detailed framework. It even provided the reasoning and confidence in its recommendations. Not Everything Is An Agent In the rush to agentify businesses, it is easy to see everything as an opportunity to create an agent. Most agents are a combination of AI and workflows. So it is the relative balance. Some processes are better, faster, simpler and easier for the user as a workflow (e.g., a button that opens a form and a workflow that processes it). When we first started building agents, we took a well-established HR process: booking paid time off (PTO). We agentified it. It was great learning to establish best practices for building agents. But it was better for the user as a simple form rather than a protracted discussion with the agent. Here are some criteria for an agent: • Understanding User Language: Do your users struggle with your system's specific terms? AI agents can understand natural language, even if it's casual or varied. This means users can express themselves comfortably, and the agent will translate their meaning into the format your system needs. • Working With Unstructured Data: Is your team spending too much time digging through free-form text or conversations? AI agents are great at processing messy data to find important insights. This makes your workflows smoother by automatically understanding complex information that isn't neatly organized. • Automating Tricky Logic: Do you have complex rules or processes that are hard to program traditionally? AI agents can perform advanced reasoning that would be very difficult to code. • Managing Complex Validation: Do your forms or processes have complicated rules that depend on many different pieces of information? AI agents can handle these tricky checks easily. Just tell the agent what the final result should look like and how to get there, and it will manage the detailed validations, making development and upkeep much simpler than writing lots of conditional code. • Assisting With Flexible Planning: Are there situations that need strategic thinking, negotiation or adaptable planning that can't be put into fixed rules? AI agents can help with dynamic planning. For example, in sales, if a customer wants a certain number of licenses but has a strict budget, an agent can help figure out the best deal by looking at things like license count, budget limits and different contract lengths (like one-, two- or three-year terms). This involves flexible decision making beyond simple automation. • Improving Data Accuracy And User Experience: Is getting accurate data crucial, and do users sometimes make mistakes or skip fields? AI agents can significantly boost data quality and make things easier for users. They can guide users to the right answers, even if their initial input is unclear. Plus, agents can reduce manual effort by using context to intelligently pre-fill information, making the process smoother and less error-prone. Final Word Not every problem is solved by an agent. Not every agent is 100% AI. And every problem should be considered through an agent's perspective, not a human's. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
10-04-2025
- Business
- Forbes
Not Everything Is An Agent
Ian Gotts, founder and CEO at getty It is easy to be caught up in the excitement of this new, exciting, agentic world. Everything you look at could be an agent, replacing boring form-based workflows. Recently, Microsoft CEO Satya Nadella, talking to Bill Gurley and Brad Gerstner on their BG2 podcast, suggested that business applications could 'collapse' in the agentic AI era. His view was SaaS would be CRUD database with agents on top. But not everything is an agent. Some of the earliest agents we built didn't necessarily need to be, but we wanted to deploy them just to get the experience of building an agent. For example, an employee booking their PTO. What was interesting was thinking about the process showed us the existing workflow we'd built had flaws. And while it would work as a workflow, it is much nicer as an agent. We also discovered that building the agent was far easier than coding it and that it could be built, tested and deployed by a junior business analyst. This got us thinking about the criteria to determine whether to build an agent or simply create a workflow linked to a button: • Do you want to shield/isolate the user from your terminology/notation/process? In our PTO example, an employee can say, "I want to take time off / I need to book PTO / I need to schedule vacation / I was off last week," and the agent understands. • Is the input unstructured data? Agents are great at making sense of this. An example would be pulling information from a call transcript and suggesting updates to the related opportunity. Another example is our agent that provides coaching roleplay to our sales teams based on a customer call transcript. • Do you need to perform complex reasoning that would be complex to code? In our PTO example, we don't want employees to book time on a weekend or public holiday. We used a simple prompt template that has access to weekends and public holidays in the UK and U.S., and the agent understands them. • Do you have complex validation rules where a rule is based on multiple field values? Again, an agent handles these if you give it the target output and think through the process. The PTO example requires you to provide a start date and length of PTO that is not on a weekend/public holiday and does not span a calendar year, and you need enough left in your balance for the year based on the policies for your country and seniority. And you cannot book PTO in less than 1/2 increments. This is complex logic, but it's just a few agent instructions. Easy to write, review, test and debug. • Do you need some form of planning that cannot be coded? For example, constructing a quote based on criteria: A customer wants X licenses and has a maximum budget of $Y but is prepared to do a one-, two- or three-year deal, so what is the best way to structure it? • Is it critical that the data is correct? Form filling can be overridden by users selecting the first dropdown or putting "..." in a mandatory field. The agent can guide them to the correct answer but also reduce the effort because it can use context to pre-fill information. As we get more patterns for agents, and the pricing is more transparent and realistic, then many automations could cost-effectively be replaced with agents. But at the moment, ROI and cost shouldn't be the gating factor. This is a major disruption, and you need to start building agents to ensure you have the right foundations in place: well-understood business processes, strong data governance and data quality, documented systems metadata. And all this must be underpinned by a rapid but governed implementation lifecycle because agents will iterate fast at the beginning. Every organization will find use cases for agents that will provide a huge competitive advantage. But this is only going to happen if you start experimenting. It's only when you get started that you will be able to uncover even more valuable use cases. Let's look at process configuration mining, for example. We've built an agent that can take a system's metadata and document how it works as a process diagram. Not how you think it works. Not how you remembered it working. Not how the consultants said it worked. Not how the design document said it should work. How it actually works. The process configuration mining agent is a combination of code and agent actions. It wouldn't have been possible without the AI capabilities, which are an enabler. So not everything is an agent. But unless you start using simpler use cases, you may never discover a step change example. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
