How to Use AI to Boost Customer Service and Sales
Pedro Daniel analyzes AI integration across sales processes, CRM unification, and the practical limits of automation.
Pedro Daniel, founder of Escola de IA, explains how small and mid-sized businesses can integrate artificial intelligence into their operations. The discussion covers CRM data centralization, the human role in complex sales, and common automation pitfalls.
Pedro Daniel, creator of Escola de IA, joined Bruno Gimenes on Marketing - The Podcast to discuss the practical application of artificial intelligence in marketing, customer support, and sales operations. Throughout the episode, Daniel explained how technological democratization allows businesses of all sizes to build efficient solutions, provided they maintain strict standards around data security, incremental testing, and human oversight.
Tech Journey and First Steps in AI
Daniel's connection to technology began in childhood, encouraged by his mother's work in computer programming during the 1980s. Early exposure to coding and visual editing preceded his professional shift to the digital market in 2020, when he built a services operation alongside his sister. The launch of ChatGPT in late 2022 solidified his exclusive focus on intelligent tools, tracking international market trends to anticipate solutions for the Brazilian ecosystem.
"In the end, in November 2022, ChatGPT came out. I was one of the first people in the world to use ChatGPT. And then I said: 'Man, this thing is going to revolutionize the world,'" Daniel recalls about the moment he decided to pivot his career toward applied AI. The practical details of this journey and the behind-the-scenes of his first implementations can be seen in the video above.
AI Democratization and Quality-Control Risks
The evolution of generative tools and autonomous agents has reduced the cost and technical barriers to developing internal systems and CRMs. However, Daniel warns that ease of development exposes serious operational vulnerabilities, particularly regarding information security, data privacy compliance, and diminished technical quality when tasks are automated without validation from qualified professionals.
For Daniel, technology serves as operational support rather than a replacement for analytical thinking. "Artificial intelligence is a tool, a means, not the end. I ended up seeing a lot of this in large corporations—people using artificial intelligence poorly," Daniel emphasizes, noting that rushing automation often leads to lower-quality output.
Sales Automation Versus Consultative Selling
While artificial intelligence plays a crucial role in initial lead qualification as an SDR and in closing low-ticket products, consultative and high-value negotiations still require human sales reps. Building trust, conducting strategic needs diagnostics, and fostering interpersonal relationships remain indispensable in complex commercial processes.
Daniel highlights the limitations of direct automation in high-ticket transactions: "When you sell a low-ticket item, a small product under $50, artificial intelligence will perform very well. But when we talk about higher-ticket items, over $1,000, $10,000, $20,000, $50,000, ultra-high-ticket products of $100k or $200k, man, it's no use putting AI in there—it won't sell."
Omnichannel Strategy, Data Unification, and the Sales Funnel
The long-term viability of customer acquisition strategies depends on centralizing information within a unified CRM and structuring a full product suite. With the rising cost per qualified lead across paid media platforms, companies that fail to build multiple touchpoints throughout the buyer's journey face margin compression and lost efficiency.
"Today you can unify everything—you can have a proprietary CRM inside your company that you develop yourself, and on top of that, you can unify all your data there. That way, you have multiple touchpoints with the customer," Daniel explains, emphasizing that continuous follow-up sustains the purchasing cycle.
Three Common Mistakes in Business Implementation
When reviewing problematic implementations across companies of various sizes, Daniel highlights three recurring errors: deploying AI in areas with no measurable business impact, keeping data scattered across disconnected spreadsheets, and trying to eliminate human customer service in post-sales, which damages support quality and customer lifetime value (LTV).
"What differentiates you over the long run and at scale is how much metric tracking and data you possess. Because as I always say, I don't work with guesswork," Daniel points out regarding the necessity of backing processes with solid metrics before scaling automated solutions.
Practical First Steps for SMBs
For small and medium-sized businesses looking to adopt AI without disrupting their workflow, the path begins with fostering an internal culture receptive to technology and conducting controlled tests by department. Rather than attempting an immediate overhaul of entire processes, organizations should prioritize gradual integrations and master communication skills before jumping between specific tools.
"If you're thinking about AI tools and believe that the tool itself will solve your problem—whether it's Cloud Code or Codex—man, you have to understand the fundamentals of AI first. The first thing you need to learn is how to communicate," Daniel concludes.