Making AI Work: Marvin Martinez’s Playbook for Real Results

Most leaders don’t need another AI tool. They need a way to make AI work inside the enterprise without the projects drifting into a quiet “proof of concept” graveyard. We get practical with Marvin J. Martinez, co-founder of Bandsaw.ai, on why AI initiatives stall and what it actually takes to operationalize AI with speed, clarity, and measurable business impact.

We talk through Marvin’s “one problem, one win” rule for AI implementation, plus a grounded view on data readiness: you don’t need perfect data, you need useful data. Emails, CRM notes, spreadsheets, and support replies already contain the patterns your team follows every day. The key is to start with what you have, build a simple MVP quickly, iterate, and retrain based on real feedback instead of waiting months for an overbuilt system.

We also dig into the human side of AI adoption, including how leaders can reduce fear, involve frontline experts, and keep humans in the loop so AI augments work instead of threatening it. You’ll hear concrete workflow automation examples from staffing and service businesses, plus a locksmith story that shows how one missed call per week can quietly add up to thousands in lost revenue and how a lightweight AI agent can help capture it.

If you’re serious about enterprise AI, customer service AI, and AI workflow automation that improves KPIs, response times, and ROI, this conversation gives you a repeatable playbook you can apply immediately. Subscribe for more practical AI and digital transformation insights, share this with a leader who’s stuck in pilot mode, and leave a review with the workflow you want to improve next.

After the episode, please visit Bandsaw.ai 

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Transcript

Why Enterprise AI Really Fails

Jim 0:05

Welcome back to the digital revolution. And today we're cutting straight to the uncomfortable truth about AI and the enterprise. Because here's the reality: most AI initiatives don't fail because the technology isn't ready. They fail because the organization isn't ready. Unclear goals, data that's scattered, siloed, or simply not usable. You have teams that aren't aligned on what success even looks like. These are silent killers. The reason so many AI projects stall, so many drift or quietly disappear into the corporate graveyard of good ideas that never delivered. But here's the good news it doesn't have to be that way. Today's guest has built a career proving that AI can work and work fast when companies approach it with clarity, with discipline, and a focus on measurable outcomes. Marvin J. Martinez is a co-founder of Bansall.ai, a company dedicated to one mission, helping organizations operationalize AI with clarity, with speed, and real business impact. Marvin doesn't just uh deal in hype, he deals in results. And his playbook is refreshingly practical. It's a roadmap any organization can follow to turn AI from a buzzword into a competitive advantage. In this episode, we're going to unpack that playbook, why AI initiatives stall, what successful teams do differently, and how you can build AI systems that actually deliver value, not someday, but right now. So settle in. This is an episode for leaders who are done experimenting and they're ready to start winning with AI. Let's get into this. Marvin, welcome. I appreciate you being on the podcast today.

SPEAKER_00 2:00

Thank you so much, Dean, for having me. It's a pleasure.

unknown 2:04

Thank you.

Jim 2:05

Yes. For the audience, if you could do a favor, you know, I mentioned about uh your background and everything like that. Uh, is there a little bit more uh detail that you could provide about yourself and also too uh about your company?

SPEAKER_00 2:17

Yes. I have been in the operations side of the businesses for over 13 years, working with multiple teams from recruiting, QA, training, HR. And I was in charge of just putting together the workflows, the SOPs, the step-by-step guides to make the business successful. That's what I used to do, and I've been doing that for over 13 years. And then when this all AI boom came on, I realized that I could just put AI into all that workflow, into all those processes. So I decided to uh co-found Banso AI. And what we do is to help US businesses in the service industry where you provide customer care or customer service to just save time, 10 up to 30 hours per week, just by implementing AI in those workflows. And we do that in just 10 days. So that's my background. I have a lot of experience, you know, the ins and outs for service companies.

Jim 3:32

Yes, yes, you definitely do. And I had the opportunity in doing, you know, a per professional background uh research on yourself and also related to Bansall, you know, taking a look. And the one thing that kind of really uh resonated with me is one of your core principles. And that principle was that AI must solve a real business problem. Because I think, Marvin, you and I recognize that AI is not a technology project. It's really about business transformation. And AI is that lever. And in what it does, it really helps to kind of bring out companies, and especially when it comes to them for the customer experience and even solving some of the problems that they have. Um, let's talk a little bit about a concept that, you know, that I noticed related to band solve. It says, you know, one problem, one win in that rule, when it comes to delivering, you know, let's say small, you know, deliver value fast and scale type of solutions. Can you uh provide some background related to you know dealing with problems and then that one-win rule?

SPEAKER_00 4:39

Yeah. Uh when I first started uh getting into AI and how AI fits into operations, um, one of the things that I noticed is that most companies, most businesses, they just try to implement AI using tools. Just go in there, and you will hear a lot about cloud code, codecs, Gemini, anti-gravity, and everything looks cool. But now the problem is how do we tie that into your company? And for me, AI really needs to resolve a problem that your company has. So think about this. AI only works when it is tied to something that the business already cares about. Let me give you some examples that could be revenue. Wouldn't you like to have AI help you to increase your revenue without you adding more headcount to your company? Or let's talk about time. Maybe the repetitive task that you do every week takes you four hours of admin time. How about reducing that two hours down to 30 minutes? That is a real win. Uh, maybe customer experience, maybe you are getting a lot of customer questions about your product and you are losing the opportunity to serve them because you are not uh good enough at answering on time. And maybe you have a lead that came to your website, filled out a form, and they never heard back from you but until two days after. At that moment, that customer already looked for another option for another company. So instead of instead of the company asking, hey, what can I what can AI do, right? The better question is where are we losing time or money every day? That is where the opportunity lies. And I follow a simple rule. When you think about all your problems, think about the problem that takes you most time and that it's easier to resolve when you add the AI to it. One problem, one win, one thing at a time. That's my core principle.

Useful Data Beats Perfect Data

Jim 6:59

That is a great core principle. And another area, too, and really looking at you know, your philosophy is dealing in data readiness without over-engineering. And one of your practical views on data that um I discovered is that you know, you don't need perfect data. Your philosophy is that you really don't need perfect data. You need useful data. And when it comes to really assessing data, you know, what are some of the things that you know companies should do to assess their data, but also to look at how they should, I guess, position their data through, let's say maybe through Bansall AI, on the approach on how they should utilize that data to be able to improve the right, you know, their foundation on AI.

SPEAKER_00 7:49

When I speak to business owners, uh, one of the patterns that I see is that they think they need perfect data. Um, what does data even mean in some cases? I work with a lot of service business uh owners, and they already have data that they don't even know they have. Let me give you some examples. Every company, they have emails. Emails from customers that are asking questions and the responses they send back. So you see a pattern on the way that they respond. You have to acknowledge the issue, you have to refer to the knowledge base, then you have to provide a solution or an alternative if the solution doesn't exist. But there's a pattern and there's data out there. Another example every company should have a CRM system where they keep track of their customer database. Some companies use Hotspot, some other ones use Salesforce, Salesforce, uh, maybe Airtable, or even simpler, maybe just a Google Sheet or an Excel file with the uh database of your customers. That's valuable data out there. One last example you have spreadsheets that contain information about uh what you do, the transactions that you've done in the past week or in the past month. So all that data, it's there. Maybe it's messy, but it is usable. So the key when you start implementing AI is using that data that you have available. It doesn't have to be perfect. Think about a customer service AI solution that answers your customers. How do we use that data? So we look at your patterns, the way that you respond, and you have to feed the AI system with that pattern. And that's how you use data. And you start little by little. Once you start doing that implementation, if you don't like the answers that the AI solution is providing, then you retrain it. But you already have a like a base of things that you can use to train your AI, and that's why it is important. Again, my recommendation for the audience and business owners is just to start with what you have. You have to build something small, and then you improve the data as you go, right? You have to start taking action. That's that's my advice for the audience.

Reducing Fear And Winning Adoption

Jim 10:22

That is a that's great advice. It it is so true. And you really look at data, you're never gonna have perfect data, but you use what you got, and then you improve the quality as you move along because you're gonna be able to do it, but you're also gonna be able to fund your business through that process. Now, we we talked a lot about you know data, we talked a lot a little bit about AI, obviously. But the real question that a lot of people have is related to the human element. And you're gonna have different teams that are in a in a business. It could be a large business or a small or medium-sized business. And a lot of the times you need to have that alignment before you start to deploy uh technology or even deploying AI. And when you really look at the success of AI, it is really successful, and it's dependent really more on the people in these teams and not, let's say, the models or the platforms that the AI is on. Now, how would you advise business leaders to communicate the AI's purpose and especially in reducing fear and resistance that might be from you know members of those different teams?

SPEAKER_00 11:34

That's an interesting one of you, uh, because at social media, for instance, if I go to LinkedIn, you will see a lot of people saying, Hey, I replaced my entire marketing team using this AI technology, service tool, whatever, right? And I can bet that there will be a lot of people in the marketing side that will say, Oh my God, AI is gonna replace me. I have a golden rule before we start about or before we talk about implementation. Uh, AI will never do 100% of the role. There must be a human involved at some point during the entire process. My ideal distribution is 60% done by AI or 60% process, 20% AI, and the rest is gonna be human intervention. A human will always be uh needed. And when I when I have implemented systems uh or AI systems in companies, the way that the leader conveys this information to the employees, it's really critical. Uh again, most people would would say, hey, is this gonna replace me? Uh, or maybe the opposite, you know, is this gonna create more work? So if that's the case, adoption will be very, very hard. So for me, the fix is is really simple. Number one, um, if you are a business owner listening to this, you have to be clear about the goal. What will this AI system give me? Will this reduce my response time to my customers? And that's the end goal. Will this ensure that I don't lose any appointments? Because this is a system reminder or a reminder system for my customers and they get a text message one day before. Or maybe, you know, I use this system to qualify my leads, but my system doesn't close the sale. So you have to be very clear as to what your goal is gonna be. Two, show your people how this is going to help them. Uh, people don't realize, but using AI in their day-to-day can reduce their job workload in about 50, 60, 70 percent, depending on how good you use AI. So you have to be clear and how they will be uh or how it will be beneficial to them. But the most important, and this is key, involve your team. They are the experts. I really like mapping the process before you implement AI. So my recommendation is go to your team and ask them, hey, uh, can you show me how you do the process?

Jim 14:28

Yeah.

SPEAKER_00 14:29

Can you tell me what's the first step, the second step? And then when you start implementing the system, show it to them. Hey, this is the AI system or the AI automation workflow. How is this looking for you? Is this resolving your issue? What do you think of the answer that the AI provided? If you involve your team soon in the process, they will be more likely to implement, uh, or not to implement, but at least to be positive about implementation. So talk to your people. AI is not, it's not going to replace anyone. It's just gonna make your work easier and it's gonna amplify what you already have.

The Practical Implementation Playbook

Jim 15:10

Yeah, I love that approach, and because it's really about your work will be augmented with AI, and it's it's that collaboration between the human and the AI, obviously. And then to your point, it can free up uh a company, it could free up an employee uh to maybe have some new skills developed. And I think when you look at all of the different types of industrial revolutions we have, you know, at one time you had artisans who made, they made things. And then you had an industrial revolution where you had machines making things. And we've seen it also now that we're moving into through a digital transformation where AI is in itself, it's a new kind of an industrial revolution, but more of a data revolution when it comes to utilizing, let's say, an AI revolution. But what I liked also, too, what you talked about was the importance of involving the teams, but also in a way, you're creating AI champions that are within inside that business. And then by getting those frontline workers involved early, they can also be part of that process for implementation and improving, uh, as you mentioned earlier, it could be the data or even the processes and procedures where AI and humans work together. Let's go and talk about your practical playbook and on the let's say an implementation strategy. And what I, through the research I did on your practical playbook, is that you're looking at really issuing or establishing a clear, and it's a repeatable uh sequence that companies can follow. Now, uh, the first thing I noticed on the playbook was you know, to define the business outcome and not the model. What do you mean by define the business outcome?

SPEAKER_00 17:00

That's an interesting question. Uh, think about this. What is my first step in implementing an AI solution? And when you talk about an AI solution, it could be a workflow, an automation, an AI agent, depending on your needs. So the first question is what exactly are we trying to improve? What exactly do we need to eliminate? And let me give you an example. I was speaking with a staffing firm uh like a couple of months ago, and they were telling me, hey, when we hire someone, what we have to do is we have to take their information, we have to put it in our ATS system, then we have to put the same information into our CRM system, and then we have to put that information into these other systems. So it was the same action three times in three different systems. And they were telling me, hey, we we have made mistakes in the past because you know, we have a human copy and pasting the information, and it is taking me about 15 minutes to do all that, right? So when I was talking to them, we were thinking about, you know, we want to save time and we want to reduce the errors. And that's the first uh step, thinking about what you want to accomplish. And once we determine that, we move on to the next step, right? Uh, what is the second step in the playbook? Map the process. So what is step number one? Where does the data come from? How does the data come in, and how is the data getting transformed? Through which steps and who does each step? Uh so I sat down with that staffing company, and we starting, you know, just like little kids, drawing boxes and doing a diagram and saying, hey, step one, step two, step three. And you will be surprised about this. In this entire workflow, 90% of the entire process was already mapped. Meaning, uh like we didn't have to use AI to do that. We only had to connect the different systems and CRMs. We only used AI in 10% of the entire workflow. So that's why it is critical to map your workflows before implementing any AI solutions. And I go back to the previous question: how do you map your workflow? Talk to your team. They are your experts. If you are a business owner, you will have the vision, right? That is 100% true. But your operators, the people that are doing the job, they will do the job better than you. And they will know exactly what steps to follow for each incoming question from every customer, for instance. So mapping your process is the step number two. Now, after mapping your process and understanding your outcome, the third step is to build a quick version. We call it MVP. Just build your MVP, make it simple, and start iterating. Uh, one of the things that we do at Vanso AI is that we can help you implement your first AI solution in just 10 days. From the moment that we get into a call and we understand your requirements, your tools, then we implement your first MVP in just 10 days so that you can start iterating and trying it and see if it works, see the first outcomes. So start early. Don't make it complicated. Don't try to build the entire application in just in just one build. Just do one feature at a time. That's the best way of building uh AI solutions. Fourth, uh, measure your results. So if you were taking 15 minutes to process one candidate through the three different systems, then how much time is it being invested now that we implemented the solution? Uh is it, you know, five minutes, two minutes, and that different is gonna be your return of investment. And lastly, when you feel that everything is going in a good track, then you're good to move on and scaling it. Maybe adding an additional feature, maybe adding some edge cases, because you will notice, like in every company, if you go to the procedures and processes or knowledge base, there will be always exceptions to the processes. And that's what we know as edge cases in the AI world. So you will not know those like at the very start. So that's gonna be your last step. If you follow this every time that you want to implement an AI solution, an automation, a workflow. AI agent, that's how you're going to be successful.

Common Pitfalls That Kill Projects

Jim 22:04

That is a that is a great plan. That is an excellent plan. Let me ask you in this in this next segment, let's talk about some of the common pitfalls that you routinely see out there. Uh in I I think you know, one you you threw out early was talking about, you know, kind of buying into some type of an AI tool or platform before you really defined what your goal is. What are some of the routine or common pitfalls that you find um businesses falling into?

SPEAKER_00 22:33

Oh, I've seen a lot. And I have also made some big mistakes when I was studying and good learnings from it. Uh so the first one is trying to resolve a lot of things at once. I have spoken with people and they say, with with business owners, I'm sorry, and they say, you know, I want to automate my social media, I want to automate my LinkedIn posting, I also want to automate uh my sales outreach, I also want to have a customer service agent in my website. They have so many things and so many good ideas, don't get me wrong, in mind, but you cannot do everything at the same time. You have to pick the smallest win or the one that you know it's it's keeping you up at night, the one that is taking you the most time every week. The process that repeats a lot every week. That's what you should pick first. But if you are trying to, you know, uh uh like save the world in just one project, you know, that's not gonna work and that's gonna be a huge mistake. I have seen people that they have been told that they need six months for the first AI implementation because they want to do so many things. And you know, advanced OAI, we just take 10 days because we start by you know making it simple for you as the owner of the business. Um a second pitfall that I see is buying tools before defining the problem. You will see a lot of tools out there in social media, and if you do a Google search or if you do uh an AI search, you will see a lot of tools to do uh email outreach. You will, you know, find a lot of tools to create social media, uh to AI voice agents. So there's a lot of tools out there, but before you decide to buy anything, just define your problem again, and I go back to it. What is keeping you up all night? I was speaking with a real estate company just the other day, and they were telling me, hey, you know, we we we have two hires, two people that are working full time just to answer questions from our customers. And that's crazy because you know, you cannot have someone 24-7 to answer questions unless you use AI. And they were telling me, you know, sometimes we take more than 10 minutes to reply back to the customer, and by that time they are no longer interested because it wasn't a real estate company and it was about renting places in Airbnb, and that's what we're working on, but they were aware of what was keeping them up and what is that repetitive action that they can automate? And the last uh pitfall is just not measuring results. So, what that means is you implement an AI solution. How do you know that worked? Like, uh, how do you know that it was successful? Is it just because you can say, hey, I have AI in my business? Is that what makes it successful? If that's what it is, then you're doing it wrong. You have to make sure that everything that you implement with AI, any AI agent, AI workflow resolves your issue, and you have to measure the before and the after. For instance, you can say, hey, before it used to take me one hour or two to reach out to a customer that asked for my service, and now it's taking me 60 minutes. I mean 60 seconds. That's your ROI right there. So if you don't measure results, then there's no way that you will see how valuable AI is in your business. So those are three, which are the most common ones that I have come across.

Jim 26:30

Excellent, excellent. The go back to the real estate agency you talked about, that that's a great example related to how bandsaw AI works. And you know, you talked about, you know, they're getting these phone calls. So, you know, a lot of repetitive workflow there that you're able to tackle with AI. But also, too, in the response, a quicker response that's going to enhance the customer support by using intelligent assistance. Um, and it it could be for actual, you know, potential clients, or maybe it's just answering questions to get to potential uh clients and opportunities, but also you're improving operational efficiency by that feedback system, that that data that's going into the AI for predictive insights. So, what I like the approach with Bansall is that you said start small, don't do these lofty, long-winded scopes that take six months, a year or more to move forward. You know, start small, have a faster validation, and then scale it as you're going along. That is a great approach for success.

SPEAKER_00 27:39

Yes. And my most successful clients, that's what they have done. Let's try it, let's see how it works, let's see how people react, uh, and then take it from there. And if we talk about real uh use case scenarios that I have built for my customers, I can give you more examples. Got it.

Jim 27:59

And right now uh we're into the second quarter of 2026. Uh, you know, you're talking to a lot of business leaders out there. What's uh what type of advice are you giving uh leaders in uh this uh time of 2026 when it comes to AI?

SPEAKER_00 28:16

Well, when you think about AI, uh stop thinking about AI capability and start thinking of it as an operating model. The question is not what can AI do? The question is how should workflow in this business and where should AI sit inside that? Think about your business as a workflow, and and and I like to make it simple for business owners. Step one, step two, step three. Are any of these steps um like an opportunity to have AI take care of that? If the answer is yes, let's go for it. If the answer is no, then just don't do it. There's no need for you to do it, right? So for me, um, when speaking with a lot of business owners, uh, they have a hard time sometimes explaining uh how they do things because most of the processes, it's inside their heads. They know how to do the job right, they are experts at what they do, but they don't have it written down. So when we want to do an AI implementation, it fails because they only know it for for themselves, right? And that's that's my recommendation. Just think about AI as uh, you know, something like an additional operating model that can feed into your current workflow.

Jim 29:44

Yes, definitely, definitely. And and to kind of close out our conversation, you know, for me when it comes to an AI implementation checklist, you know, based on our conversation and also to my research on Bansall, you know, the first thing I wrote down was, you know, start with a real business problem. And to what you said is, you know, just don't tackle everything, tackle maybe a singular business problem. The important thing that you stressed early in our conversation was use the data you already have, and then move into once you have everything kind of defined out as what you want to do, you're gonna prototype that fast and then you're gonna validate that fast. You're gonna involve the people on the teams to make them uh champions, to make them included, involved in the process and the planning of it. KPIs or measurements, those are very important for you. So you can look at what value is. And you also obviously have to be able to define what value means. It could be gains in efficiency, uh generating revenue or whatever it might be. And then I think the most important thing is scale only after proving impact. And I think that is the perfect checklist. Is there anything that my checklist I kind of marked down that maybe I missed or misinterpreted, let's say, in looking at what your practical guidance is?

Locksmith Case Study And Missed Revenue

SPEAKER_00 31:07

Well, I think that you have done a great job summarizing it, Jim. Um, I just want to give you like an example of a successful client that we had. Uh, is it was um a locksmith company located in the United States. And what they were telling me is that sometimes uh they were out in the field and people would call to get a quote on the service, and they could not pick up the phone call. So they missed that client. So they have that miss call. Uh when I asked them, hey, how many missed calls are are you are you having like per week? So they were saying, well, you know, it may be one or two. It doesn't sound like much, right? And then I asked him, hey, and how much is your is your service call? Let's imagine that you answer that phone call and then you arrange a visit with that customer. So he was telling me that his service call fee was$150. So if you do$150 one time per week, so that means that you are losing$600 per month just by not answering one phone call. And if you extrapolate that into a year, we're talking about$7,200 that he's missing just for not answering one phone call, uh you know, when he was on duty. So what we did was to build an AI system that would answer the phone call only if he does not answer it himself. One of his concerns was I don't want AI to take over the way I serve my customers and I speak with my customers. So I told him, that's fair, right? So we can activate this AI agent when you lose the call or where when you miss the call, that's fine. And the other thing that we did is the AI will only answer the phone call and will tell the person, hey, our owner or our uh locksmith is currently on a job. Let me take your information so that we can call you back. And the AI would only take the information from the person's name, last name, uh phone number, and issue. And that information goes to the owner via text message, and the owner can give a call back as soon as he's done. Right? So that's an exact example where AI will not replace you. You can use AI based on your needs and your preference as your business as a business owner. Um, and we implemented it really easy because this person knew exactly what he wanted. He knew his process. And without knowing it, he's now saving$150 per week, which it doesn't sound much when, but when you do it per year, it's a lot of money that he's not leaving in the table.

Action Steps And Closing Resources

Jim 34:04

Excellent, excellent. So, for everyone listening, this is a great call to action. Um, what I would like everyone to do is to reflect on one workflow in your business that could benefit from AI, and especially with this episode, what Marvin covered regarding his playbook and also what he's recommending with Bansall AI, is to come up with one of those workflows and you know, maybe even have that conversation with Marvin or Bansall. And I'll make sure definitely in the description for the episode, I will have uh Bansall's site address and also a uh link uh so that you can connect uh to Marvin as well professionally. And I highly encourage everyone to check out uh bandsaw.ai. Uh, Marvin, in closing, is there anything uh that you would want to leave uh to the listeners and followers of this podcast?

SPEAKER_00 34:56

My last message to you guys is if you are feeling overwhelmed with this AI hype, you are not alone. I hear this from a lot of people that I talk to every day. And uh my advice is just think about your business uh and the processes that you have. You don't have to implement AI because you want to sound cool. You want to implement AI because you have a lot of pain in doing the same thing every day, and it takes and it's taking you a lot of time. Think about that, reflect that. And if you don't know where to start, well, uh that's why we're here for. I already did 13 years of in operation, so I know exactly how you feel. And I've done over a thousand hours implementing and trying AI solutions so that you don't have to.

Jim 35:49

Excellent, excellent. Um, everyone, please make sure you do check out uh bandsaw.ai and also uh connect uh with Marvin as well. Um, please uh share this episode with anyone who will get value out of listening to it. And please make sure that you are following the digital revolution with Jim Kunkel, uh the podcast. It's on all the major podcasting platforms because with this podcast series, you're gonna get more expert-driven insights. And also, too, like I said, make sure you check out bandsaw.ai. Marvin, thank you so much. Thank you, Jim. It has been a pleasure.