Jan. 22, 2026
How Clean Energy Companies Can Get Real ROI from AI #330
Most businesses are stuck in AI demo mode, watching impressive one-off showcases that never become daily operations. In this episode, Tim Montague welcomes Josh Huston from Agents Anywhere to break down the three categories of AI opportunity: apps, agents, and automations. Josh has spent over three years helping companies turn AI from buzzword into working business infrastructure.
Key Discussion Points:
- Why AI is an accelerator for working workflows, not a fix for broken processes
- The three AI opportunity categories: apps (custom interfaces), agents (goal-driven LLMs with tool access), and automations (background workflows)
- How data ownership shifts when you build custom applications instead of relying on third-party tools
- Real-world example: proposal generation workflow reduced from hours to minutes using AI agents
- Pilot project framework: $15K-$25K investments targeting high-impact workflows with measurable ROI
- Cost structure for AI agents: service fees, token usage (as low as 2 cents per process), and platform licensing
This episode gives clean energy operators a practical framework for AI adoption. The message is clear: stop accumulating tools and start building strategic workflows. Josh and Tim walk through the entire process from identifying friction points to scaling successful pilots across your organization.
If you run a solar EPC, energy storage company, or any cleantech operation feeling pressure to "do something with AI," this conversation provides the roadmap. The approach is simple: find what works, accelerate it with AI, measure ROI, then expand intentionally.
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The Clean Power Hour is produced by the Clean Power Consulting Group and created by Tim Montague. Please subscribe on your favorite audio platform and on Youtube: bit.ly/cph-sub | www.CleanPowerHour.com | contact us by email: CleanPowerHour@gmail.com | Speeding the energy transition!
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We've been working with companies in all shapes and sizes to figure out, how is AI going to make a difference in your business? And from the very early days to now, there's been a lot of progress in the space.
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There's a ton of noise. And so what we do, like Tim said, is make it super simple to figure out what are the practical applications that can change your daily operations, whether that's on your people side of your company, or in the technology side of your company.
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Are you speeding the energy transition? Here at the Clean Power Hour, our host, Tim Montague, bring you the best in solar, batteries and clean technologies every week. Want to go deeper into decarbonization.
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We do too. We're here to help you understand and command the commercial, residential and utility, solar, wind and storage industries. So let's get to it together. We can speed the energy transition.
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Welcome to future proof, your clean energy business with AI. I'm Tim Montague, thank you so much for joining us today. This is a clean power hour live. I'm Tim Montague, your host, and I host the Clean Power Hour podcast.
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Check out all of our content at clean power.com I had the pleasure of becoming a AI certified business consultant this summer, and it just dovetails really nicely with everything else that I do in clean energy. I work with solar EPCs on helping them grow into large CNI and battery storage, and I am a super connector in the clean tech industry around energy storage and technology, and whether you're an EPC, a developer, an asset owner or other kind of operator, you know you're feeling the pressure to do something with AI, and we want to break through the hype and avoid science projects and help you get some clarity On How can you actually leverage AI in your business? So I'm joined today by Josh Houston, who is a fantastic colleague of mine, and as brought to us, I should mention through the WSI network, I got certified through a company called WSI worldwide this summer, and Josh helps companies turn AI from buzzword into something that actually runs inside the business through agents anywhere he works hands on with teams to design, build and deploy AI agents, apps and automations directly inside the tools where work already happens with that. I'll let you introduce yourself, Josh.
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Thanks so much, Tim. And hello everyone. It is nice to meet you all today.
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Yeah, happy to be here. And just a quick background. For the past three and a half, four years, we've been working with companies in all shapes and sizes to figure out, how is AI going to make a difference in your business? And from the very early days to now, there's been a lot of progress in the space.
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There's a ton of noise. And so what we do, like Tim said, is make it super simple to figure out what are the practical applications that can change your daily operations, whether that's on your the people side of your company, or in the technology side of your company.
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So here's our agenda today, understanding AI challenges and opportunities, leveraging AI apps, agents and automations, building and executing your AI strategy, key takeaways and next steps, and we will reserve at least 10 minutes for Q and A at the end of the hour. And we may take questions on the fly so understanding AI challenges and opportunities.
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Why does AI matter in clean energy? It matters for every business truly in the world today. I liken AI to electricity. Once civilization has electricity, it doesn't spontaneously go back to not having electricity. We call that camping, and it's fun for a vacation to do that, but to run a modern society without without electricity would be very foolhardy. And so while AI is now widespread and everyone is using large language models like chat GPT or Google Gemini, very few lasting impacts are being made in small and medium sized businesses today. So we're here to help you really see some success and identify workflows that you can leverage and accelerate. AI is an accelerator. It doesn't fix a broken workflow, but it does speed up existing workflows that are working well. Some of the pitfalls that you know you should be aware of is. Is this possibility of overload and when you have too many tools at your disposal. So you'll see throughout the day that we really want you to focus and try to shed tools if you can, on the lack of ownership front, you know, if you're not in control of your information, your data or your workflows, you risk the benefit of AI fading away and or just never getting traction. And while we are a fan of doing pilot projects, standing on the on the sidelines and watching demonstrations, you know, these impressive one off demos will rarely become routine operations and really drive ROI in your business. Where are the potentials for operational drag in clean energy? We see three key places. Broken processes is a big one, okay? If your workflows are not working well, are not well designed, or you don't have the right combination of tools or technologies that can cause bottlenecks, and so think of AI as a great way to accelerate where you have manual workflows that have a lot of repetitive tasks involved, and you also have to be aware of disconnected tools when when we're working in silos, that is never going to be the most effective way to create efficiency. And so sometimes when you add AI tools into your business, it does lead to this kind of siloification. And so you just have to be cautious about integrating tools into your workflows and and then scatter data limits AI impact.
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So if you have fragmented information across CRM different drives, whether that's a cloud service like Dropbox or Google Drive, it can prevent you and your AI tools from really getting the full picture. So now we're going to switch into a new section, leveraging AI apps, agents and automations. I'll hand it to you, Josh.
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So when we think about AI, there's three main types of opportunity in the technology side of things.
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There's apps, agents and automation. When we say tools, what we mean is the tools that use every day. These could be apps, the agents that you're starting to get to see in the world. These are being launched inside of the apps that you use.
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And an example of that might be your bank offering a chat with your finances agent that you can now access and communicate with.
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And it might be, you know, an agent that lives inside of your business processes that you host locally. And then automations are the connectivity between the apps that you use. So when we think about where does AI show up? It shows up in the apps that we pay for and subscribe. It shows up in the as interactions within the tools that we use.
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And then automations happen behind the surface. Now from our perspective, and this is a big shift from what technology has been and the tools that we have had for the last few decades to the AI, opportunity at hand is we're no longer bound and limited by budgets and complexity to shipping apps for your own business, shipping agents inside of your own company, and building automations within your own business. The last decade, up until the AI era, these solutions were really expensive.
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They took a lot of time, they had a lot of complexity and had a low success rate. And what we found over the past few years is as the models that we use in AI have gotten better, the apps that we use, the agents that we see and the automations that are happening are improving as well, both in the tools that we buy and our ability to build custom versions of apps, agents and automations with our clients and their businesses. When we think about apps, the main thing that we're thinking about is owning the data that you have traditionally anytime you connect to a third party tool the way it works. As you wire in with integrations, your tooling, your systems, and they take your data, they work with it, they reformat that data and information and present it back to you in a helpful way. This is great. Sometimes it takes a lot of work to get there. Take a CRM, for example. When you build a CRM out for your company, you pay Salesforce or HubSpot, and you most likely hire a consultant to help you implement Salesforce or HubSpot so that you can use that tool effectively, and it can give you all the value when we think about the opportunity today, the leverage we get with the speed of engineering and AI's ability to help us customize internal applications is that you can change the direction of this data ownership and actually keep the data to yourself, own it and combine all the data that you get from the different apps that you use in a unique way that creates intellectual property.
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So what we're seeing is the ability to own your own data by building your own applications, and then once you have these data sources mapped, AI agents can go to work. There's a place for the AI to live, there's a place for it to be hosted, a place for it to leverage this information to help you move faster in time, in terms of your strategic processes. This is using data to make decisions, drawing out insights, finding answers and getting things done.
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And when we think about the automation layer, this is the work that can be happening in the background, which only happens once you've mapped your business processes, identified what can be handled with technology, and then within that workflow, where would the AI go to work to help you? Let's get a little bit more specific when we think about apps. This is going to be purpose built interfaces that are custom to what you need, and this is an opportunity we're seeing in small and medium sized businesses now to design apps that reflect your actual processes with direct integrations to the data that you already have in your business, so that you get real time insights from your operational data sources. These solutions become more scalable and adaptable than using traditional software options, because your app can grow with your business, eliminating the need for all these tool changes and reducing the amount of tools that you have to subscribe to overall as well. And at the end of the day, we're seeing a lot of businesses invest in doing this so that they can build their own IP so that their service business can last longer than them in terms of their career trajectory, or the desire, to say, maybe go through a mergers and acquisition. The second layer that we're going to talk through is AI agents. And you've probably heard a lot about AI agents. The definition that we use for an AI agent is any sort of large language model, LLM or small language model. Small language model is SLM being able to be given a goal, given a toolbox of connected applications, and having the freedom and bounds to go accomplish that goal with the given tool set. So when, when we define it that way, it opens up a lot of opportunities, because AI models are able to use the tools that you already subscribe to. And I'll use a project management tool, Asana, as an example, an AI agent that can use Asana can help me with project management. It can help me figure out who is up to what without me having to go in and look and click around all my projects. It can let me see what my team is actively working on, see if there's anything that I'm blocking, and help me unblock them. And how does it do this?
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It uses their the asana functionality to gather the tickets, organize them into the projects that they're already set into, identify what I'm assigned and how I can help with the context that it has access to. Maybe it has access to my shared drive, where I have some project notes and information from the phone calls that we've had in the meetings. This is a real world use case that's really, really accelerated from working manually through all of those steps, which takes hours every day to minutes every day.
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And these agents can be embedded both in my manual process like I just described, of me going to and talking to an agent. This could happen inside of a tool that already exists, like chat, GPT, Claude, Gemini, or it could exist in a tool that I build and wrap myself so I can talk to my agent in Slack, or I can talk to it in WhatsApp. There's a lot of opportunities to put agents wherever in the world, and that's where agents anywhere got its name so more real world use cases is taking in sales information and helping qualify and take down the notes of, is this opportunity going to be qualified and can and then what should we be doing as follow up activities? AI agents can help with some of those automation steps and the admin around, taking good sales notes, customer support triage. Perfect example is companies putting AI chatbots on their website to interface with customers and help them find answers to their questions, help them resolve issues they're having and get through those not just in the nine to five but around. Clock, wherever, whenever. And then another great example that we're seeing AI agents is chatbots that are connected to your existing file systems, which would allow it to, in this case, help you with onboarding new hires, unlocking tribal knowledge, helping people sort through all the layers of the files that you have. That's a common task that takes a lot of time in little increments, and has been a really big win for AI agents. So the next thing we'll talk about is the automations.
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Automations are static workflows, and the whole point of building automations in your business is to connect different systems to each other. We call this integration work, but also ease the manual burden of repeatable processes. Say you have sales meetings those those meetings are recorded and transcribed, and the next logical step with those meeting notes would be update the CRM so that the sales pipeline is is current. That's an automatable process that could happen in the background after every call ins, and after that transcript is ready, it fires automatically.
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You never have to touch it. And so automations help with the efficiency and the scalability of the admin tasks in between all the work that's getting done. So we're going to talk about next building and executing your AI strategy.
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We're giving you this overarching frame of apps, agents, automations, start building some internal tools you know, tailored to your workflows and connect your data across the organization, okay, empower your teams to use agents, and this can, as Josh mentioned, affect the sales, sales and sales support, as well as customer onboarding, but really, any department That involves knowledge work can leverage any of these aspects of AI, and I'm a huge fan of unlocking tribal knowledge with agents. You can think of custom gpts as one form of this. And then ultimately we're talking about boosting efficiency with automation. And this definitely has to be accomplished by integrating systems and eliminating manual handoffs. And your platforms have to be friendly to interacting with other platforms, and that can be a bottleneck. And you know, what we're trying to do here is reduce the administrative burden on knowledge workers, plain and simple. So when it comes to pilot projects these, you can think of these as the low hanging fruit. You want to identify projects that are doable in a relatively small amount of time but have high potential ROI and you can think of these as 15 to $25,000 investments on key workflows focus on high impact right things like proposal generation or lead capture project knowledge and management, turning meeting notes into valuable outcomes. And you know, before you embark on a larger project, you want to demonstrate that these pilots are producing return on investment. And then once you have a successful pilot that you know is proven to improve efficiency of the organization and generate ROI, then you can scale that to other areas. Yeah.
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So just to share a very practical use case, Tim mentioned a great project opportunity that's often low hanging fruit is the proposal side of things. Now, when we think about the types of solutions, AI, agents, apps, automations, they're going to create value for your business in a couple different ways. One way we think about this is creating leverage in the operations. The other is unlocking revenue opportunity.
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This one is closer to the revenue line, which is great. So the process flow happens like this. The meeting ends, the transcript is created. The file gets added to your drive of the transcript, that file triggers the next step in the workflow, which is an AI agent that's going to look at the document, look at the context, see your use case examples, and see those files in your drive, and then look at your proposal template, see the sections, see how you would incorporate the use cases that are most relevant given the context. The AI agent is going to help sort through all these things, and it happens in the background, then it's going to use your template to draft a new template that's customized for this opportunity, that was boilerplate, but it's now customized, and this is going to allow your team to then come in, see the choices that have been made, look at this document that's in its draft final form, provide human oversight and what we call human in the loop. Group quality assurance to make sure that the sales team is reviewing and finalizing this draft it's accurate. The judgment calls were right, the right. Case studies and details have been added, and this is going to help you save time from hours to minutes. And when we think about, how do we go about planning out a project and implementing it, what we're going to do is talk with you and your team about the high impact opportunities. This is a conversation where we figure out what's working right and has clear friction points. Now those two pieces are really important, because trying to use AI as a magic wand to remove a problem from a process isn't going to work out the best. And there's a lot of case studies out there in the world that you'll see people riding up though, you know, we had a failed project. It didn't work. Ai couldn't solve. And a lot of those projects come down to using attempting to use AI to solve something that hadn't already been working well. And in order to tackle something that, you know, process that's not been fully optimized, first, you need to step back and reconfigure, redesign the workflow step so that the process is smooth. From there, we can accelerate it with AI, and that's generally the approach we'll take. And the second step is going to be looking for these quick wins, right? Once we've identified what's working well, what has clear friction points, can we take away parts of the project, especially the bottlenecks that are taking the most time in the proposal writing process? As an example. These are the steps in between everything. This is the having to have someone come in and be the note taker in the call. And so you have to have an extra person attending, then making sure that that person spends the time, effort and energy to write up a good follow up email and relay all of their notes into the team as context.
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Then the team has to brainstorm.
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They have to go to all the documents, right? We identify, what are the time sucks here.
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It's looking at files and information and reformatting that information into a different shape in a different document. That's how we would identify, where are the quick wins and where are the biggest bottlenecks? From there, we decide, okay, if we use a different process, how much time can we save? Maybe you're spending four hours on the proposal before we think we can get it down to two by removing the extra step of having the note taker and then reformatting the documents and picking up the case studies so we measure it, we make sure that we're actually getting the value that we think we will. And then from there, we'll document it so that we have a pattern of a win. And once we stack up these patterns of wins, whether it's one quick win, one case study, or one project, or a bunch of ideas that end up fitting these criteria. Now you have a pattern that can go to the team, and when you start to look at how can we help the whole organization make progress, one department or one team that's focused on building an agent in automation or an automation can go and share their success.
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Here's a win that we found in the proposal writing process.
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Maybe marketing sees that and says, Oh, we actually have a lot of conversations with the product team, and they're telling us all this to context about what their decisions are.
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And we're trying to make this into messaging, and we're going to use that meeting transcript for our messaging documents instead, or our say it's going to be for the content that we write. This is how the pattern can start to spread across the team, and then the scale to the next bottleneck stage. This is the last step we're in. We're trying to figure out where else can this same type of solution be applied in the business, and this is by presenting it internally, helping you with the communication plan, so that from leadership, top down and bottom up, from the staff, all the ideas around how we could leverage an AI automation or an AI agent could be shared and then go through this same cycle for others in the company. Yeah.
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So key takeaways and next steps for clean energy operators, we have a simple frame leverage AI for business impact, okay, and your success comes from strategic application, not necessarily an accumulation of tools. In fact, we want you to use fewer tools at the end of an engagement with us or your favorite AI consultant, focus on three lanes, apps, agents and automations to drive efficiency and then pilot, measure and scale. Right? Start with targeted pilots that document so you can document ROI or kill a project, right? If it's not producing ROI, it's okay to have false starts. And with those success stories, then you're going to expand intentionally into other aspects of your business. The Clean Power Hour is brought to you by CPS America, maker of North America's number one three phase string inverter with over 10 gigawatts shipped in the US. The CPS product lineup includes string inverters ranging from 25 kW to 350 kW, their flagship inverter, the CPS 350 KW is designed to work with solar plants ranging from two megawatts to two gigawatts. CPS is the world's most bankable inverter brand, and is America's number one choice for solar plants, now offering solutions for commercial utility ESS and balance of system requirements go to Chint power systems.com or call 855-584-7168, to find out more. I like Tyler's question, how do you connect the agent to core processes within your business that may not offer MCP out of the box?
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Yeah, so if there's not MCP out of the box, and this goes with term that engineers are very familiar with. You guys probably are
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familiar with define MCP for us, if you would.
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So MCP is a protocol. It's called model context protocol, and it was basically the in its most simple format, the solution that they open sourced anthropic, the one of the big AI model providers open source this protocol to because they figured out how to give your AI, the LLM, chatgpt or Claude or Gemini, access to tools that have APIs. So an MCP is simply the AI version of an of an API. If a tool that you're using has an API but doesn't have an MCP, we can build an MCP for it, basically giving the AI agents that you would want to use access to that. So the ability with MCP, the way it's a protocol and it's open source, makes it so that any application can technically build and launch their own MCP centered around their API. Now, without getting into the technical weeds, there's a lot of companies who are leaning into that and offering their own like an example, we were just working with. HubSpot has an MCP, and they did this because they wanted to make it easy for anybody using an AI tool to connect to HubSpot, because maybe their CRM isn't and that's a value prop, right? So that's one way. Now the other way. And this is more interesting and definitely more experimental, is what's being called browser agents, or use computer agents, where instead of looking at the back end of your application system, there are models that are capable of taking over its own little computer so it can launch a browser of its own, go look at a tool, take screenshots and or stream the actual like screen that it's Looking at and then use a mouse and a keyboard virtually chat. GPT has a feature like this that you can test out and see it working, but that's, I would say, it's still experimental in that it doesn't feel very reliable yet. So hopefully that that helps. But yeah, the used computer and the browser agents that go on the front end solve a much more open ended variety of problems, because they've been most, most apps, I would say there's going to be a good number of apps that still will never have APIs that are public.
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And then we have a question about automation. What are we using for automation?
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Would there? Oh, and then this user also, or this listener also has a question about any recurring payment after building solutions? So, yeah, I mean, that's a that's a big question, but I'm curious what your thoughts are about that
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there's three categories of how what AI agents cost. The Category One is the service of building the agent, or the the time it takes right to set it up some if you're going from scratch, it's going to take a lot of engineering time. That's going to be the service cost. If it's taking a platform that already exists, it's going to just be the configuration cost. And that's what we're building into agents anywhere is the configuration cost is significantly less than if you had to go from nothing, because most of the pieces are already here. So then the second recurring payment type, so there's the service fee to build it, and then there's the hosting of what is this connected to?
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And this kind of is more relevant for the app side of it, but the usage is the other factor. How much does this agent use in terms of tokens? Is going to be factored into an estimate and any project, and you should have with any engineering company or anybody you work with that's building an agent that's going to be in the wild, or an automation that's going to live, we should be able, they should be able to calculate, here's how much it's going to cost to run every month the operational workflow. We typically try to boil it down to how much does it cost to run this process, end to end with an estimated number of tokens in and an estimated number of tokens inside, and then the operation actual, like script costs. So it becomes like a monthly estimate, where we say, hey, every time this thing runs, maybe it costs two cents, and we're going to run it 1000 times. So your usage cost, that's variable, if you run it 1000 times, is 20 bucks. If that's the right math, and that's easy to predict based off of just measuring during the testing process and the build process. And then the third would just be the software, like license generally, of having access to a management dashboard, a management platform that lets you see everything happening, that's very common.
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So like in Nan, you pay Nan or Zapier or make.com a monthly fee. And that gives you a certain amount of usage and the access to the app. So those three and then that third category, the service of getting everything set up.
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Yeah, I guess this is one reason why the DIY approach is going to be relatively painful, because there are multiple options for achieving any particular for example, automation and working with more seasoned AI consultants like me and Josh will give you a leg up, because we can, we can guide you to a cost effective solution so you're not surprised by these gotcha recurring fees that that may happen when you DIY it. And of course, you can reach out to me at clean power hour.com check out my agent. We built an agent on a platform called Voice flow at Clean Power Hour, we trained it with content from hundreds of interviews that I've conducted over the years, as well as my book wired for Sun, the commercial solar playbook. And so the Chatbot is quite knowledgeable about the industry, and then it also will go out and query via a LLM, hey guys, are you a residential solar installer doing light commercial but wanting to scale into large CNI solar? I'm Tim Montague. I've developed over 150 megawatts of commercial solar, and I've solved the problem that you're having you don't know what tools and technologies you need in order to successfully close 100 KW to megawatt scale projects. I've developed a commercial solar accelerator to help installers exactly like you. Just go to clean power hour.com click on strategy and book a call today.
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It's totally free with no obligation. Thanks for being a listener. I really appreciate you listening to the pod, and I'm Tim Montague, let's grow solar and storage. Go to clean power hour and click strategy today. Thanks so much. I think we'll wrap up there. I want to thank everybody for being here today. Really appreciate your time. I look forward to hearing from you, and if you need to reach Josh, I can put you in touch with Josh as well. Again, thank you, Josh. Houston, with agents anywhere, everywhere, anywhere or everywhere.
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Josh, anywhere, anywhere. Agents, anywhere.ai.
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We have to launch our landing page.
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I apologize for the time. Thank you so much. And with that, I'll say, let's grow solar and storage. I'm Tim Montague, Take care everybody.
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If the session sparked ideas, but left you wondering what this looks like in your business, let's find out, book a 30 minute working session with me. We'll identify one workflow proposals, project management, whatever's eating your time, and identify where AI creates measurable ROI in 2026 for you more clarity and a specific next step that you can execute. Go to clean power hour.com, and hit AI to book a call today. Let's work through your next AI workflow together.
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