Episode 2 of 3 AI adoption is accelerating, but the price tag can be a lot bigger than the monthly subscription. From expensive model usage and infrastructure to data preparation, security, talent, and ongoing maintenance, scaling AI without a plan can quickly turn innovation into an expensive experiment. In Part 2 of this three-part conversation, host Keelin Conant welcomes back Franck Leveneur, founder of Data-Sleek, to break down the real costs of AI and explain how businesses can scale responsibly without blowing their budgets. With experience working with large-scale technology environments, including the Hulu/Disney ecosystem, Franck shares why smart AI adoption isn't about building everything at once. It's about choosing the right tools, establishing the right architecture, measuring real business value, and building systems that can grow with your organization. Because in the race to adopt AI, the goal isn't to have more AI. It's to make AI work for your business. In This Episode, We Explore The hidden costs of AI: Infrastructure, data preparation, security, talent, and ongoing maintenance that businesses often overlook. Why the wrong AI model can cost you: Learn why simple tasks don't always require expensive large language models (LLMs). AI tokens and runaway costs: How poorly configured agents, infinite loops, and a lack of monitoring can quickly inflate API bills. The importance of AI architecture: Why model routing, local models, and choosing the right technology for each task can improve efficiency. How to scale AI without breaking the budget: Why starting small and testing solutions before expanding is a smarter approach. The human cost of automation: Why expertise, decision-making, creativity, and human oversight remain essential. Measuring AI ROI: How to evaluate whether AI is delivering measurable business value or simply becoming an expensive experiment. Building trust in AI: Why adoption and user experience matter just as much as technical performance. Avoiding technical debt: How documentation, naming conventions, and standardized processes make AI applications easier to maintain and scale. Overcoming AI FOMO: Why businesses need a clear vision and implementation plan instead of rushing to adopt technology just to keep up. Why AI Can Get Expensive Fast One of Franck's biggest warnings is that companies can spend too much by using the wrong model for the task at hand. A simple content summary may not require an expensive cloud-based model. Depending on the task, a smaller model or a locally running model may be sufficient. Another risk is failing to monitor AI agents. An agent stuck in a loop can continue consuming tokens and generating charges long after it should have stopped. Frank recommends implementing safeguards such as: Defining clear boundaries and stopping conditions for AI agents. Monitoring usage and setting appropriate limits. Routing tasks to the most cost-effective model. Considering local models when they meet the task's requirements. Establishing an architecture that supports efficiency, security, and scalability. The takeaway: AI costs should be managed through deliberate design, not just by checking the bill after the damage is done. Start Small. Build for Scale. Franck emphasizes that businesses shouldn't try to build an entire AI ecosystem from day one. Instead, he recommends starting with a manageable application that addresses a specific problem, testing it with real users, gathering feedback, and improving it before expanding. He compares this process to building a house. You need a strong foundation and a flexible design that can accommodate future growth. You also need to consider the people who will use what you're building and the value it will provide them. A technically impressive application isn't a success if employees can't use it or customers don't find it useful. Helen and Frank also discuss how the pressure to keep up with AI can lead executives to rush into implementation without considering the long-term consequences. Their advice? Crawl, walk, run. Find the low-hanging fruit, prove the value, and build a repeatable process before scaling. The Hidden Costs Go Beyond Technology AI implementation involves more than model subscriptions and computing power. Businesses also need to account for: Infrastructure: Cloud resources, storage, processing, and utilization. Data preparation: Cleaning, organizing, and maintaining reliable information. Talent: People who can design, implement, monitor, and improve AI systems. Security: Protecting sensitive information and maintaining appropriate safeguards. Maintenance: Keeping applications reliable, secure, and scalable over time. Change management: Helping employees adapt to new workflows and building trust in AI-generated results. That last point is especially important. If employees don't trust an AI solution or can't incorporate it into their existing workflows, adoption may stall—even after months of development. An expensive application that nobody uses isn't delivering meaningful ROI. How to Measure the Real ROI of AI AI's value isn't limited to reducing labor costs. It can also give employees more time to think, analyze, create, and make better decisions. Frank offers a practical way to evaluate an AI investment: measure the cost of the existing process, then compare it with the time, resources, and effort required after automation. For example, if a financial modeling process takes five people working 40 hours per week over two weeks, that gives the business a baseline to measure against. If AI can reduce the initial work to an hour while allowing employees to review the model and conduct additional research, the organization can quantify the potential improvement. But the bigger benefit may be what employees can do with the time they recover. Instead of spending hours copying and pasting information, they can focus on expertise, creativity, business intelligence, and strategic decision-making. Franck also discusses the flywheel effect: as organizations generate better data, AI can use that information to produce insights and additional data that feed future improvements. Over time, this cycle can help businesses become more efficient and data-driven. Build a Repeatable AI Playbook As businesses experiment with AI, they may quickly accumulate applications built by different employees for different purposes. Without shared standards, this can create fragmented systems, duplicated work, and technical debt. Franck recommends establishing consistent practices for: Defining the business goal before development begins. Documenting applications and their intended use. Creating naming conventions and standardized workflows. Tracking how applications fit into the broader technology ecosystem. Establishing guardrails and monitoring performance. Making successful solutions easier to replicate and maintain. The objective isn't just to build one useful AI application. It's to develop a process the organization can reuse as its AI capabilities grow. The Three Little Pigs of AI Franck shares a fitting analogy: the story of the three little pigs. One builds quickly with straw. Another takes a little more time and uses wood. The third invests the time to build something solid. Businesses face a similar choice with AI. The pressure to move quickly can encourage rushed implementation, but solutions built without a strong foundation may struggle when the business grows or requirements change. Before investing, leaders should understand their data infrastructure, define their vision, identify the right use cases, assign responsibilities, and establish safeguards. Build the foundation first. The ability to scale comes from getting the fundamentals right. About Franck Leveneur & Data-Sleek Franck Leveneur is the founder of Data-Sleek, which helps midsize businesses turn fragmented data into a reliable foundation for AI and better business decisions. From data strategy and warehousing to governance and infrastructure, DataSleek helps organizations address the data challenges that can prevent successful AI adoption—all without unnecessary enterprise overhead. Get Your Free AI Readiness Assessment Before investing in AI, find out whether your data and infrastructure are ready to support it. Data-Sleek offers a free AI readiness assessment to help businesses evaluate their data governance and infrastructure and identify potential gaps. Start here: https://data-sleek.com/AI-readiness-assessment Data-Sleek — Better data, smarter decisions. Coming Up on CyberSoul.io This is Part 2 of our three-part series with Frank Leveneur. Episode 3: Garbage In, Garbage Out — Why Your AI Is Only as Good as Your Data Next, Keelin and Franck dive into the data foundation behind AI, exploring why data quality matters and how unreliable information can undermine even the most sophisticated technology. Subscribe to The CyberSoul.io and join the conversation about cybersecurity, AI, and the human side of technology. Because AI isn't going away. Let's make tech human again.
