October 2025 Summaries

Ohad Parush, leading R&D at Gong, explains how AI has transformed their engineering processes by becoming a peer programmer rather than replacing engineers, enhancing productivity and learning speed. At Gong, AI tools assist in coding by generating drafts, suggesting refactors, and automating repetitive tasks, which allows engineers to focus on judgment and critical review. While AI significantly boosts efficiency in structured tasks, deep engineering still requires human oversight. AI has been particularly effective in testing, where it outperforms humans in speed and accuracy, and in reducing onboarding time, enabling engineers to quickly adapt to new frameworks. Despite AI's integration, Gong prioritizes security, employing secure sandboxes and rigorous human code reviews. The company's hiring practices still emphasize analytical skills over prompt-engineering, as AI is seen as a tool to enhance, not replace, engineers. Gong's experience highlights that AI can enhance engineering capabilities by making great engineers more efficient and new engineers more adept, without altering the fundamental need for human insight and creativity.

HeyGen, a leader in the AI video space with over 85,000 customers, is leveraging AI to revolutionize its development processes, as explained by Head of Product Engineering Nikunj Yadav. AI has blurred the lines between backend and frontend roles, enabling engineers to write frontend code and reduce handoffs, thus speeding up the development cycle. The most significant AI benefits are in code generation and testing, with AI facilitating the creation of unit tests and improving code coverage. Despite its current limitations, AI-assisted tools like Graphite show promise in refining code quality by catching bugs and offering basic feedback. The hiring process at HeyGen now emphasizes AI fluency, assessing candidates' problem-solving skills and their ability to work with AI-generated code. AI also enhances productivity by allowing non-engineering teams to self-serve customer inquiries, minimizing interruptions for engineers. Overall, AI has expanded the capabilities of HeyGen's engineering team, fostering a culture of experimentation and innovation.

Alex Turkovic, Sr. Director of CX at Belfry and host of the Digital CX Podcast, discusses the transformative impact of AI on customer success teams, highlighting how AI tools like Vitally and Gong enable proactive identification of customer churn through sentiment analysis and engagement signals. This shift allows teams to address issues early, moving from a reactive to a predictive approach. While AI automates data gathering and triage, it cannot replace the empathetic aspects of customer relationship management, thus freeing customer success managers (CSMs) to focus on the human elements of their roles. Turkovic notes the emergence of the automation specialist role in customer success, which involves creating workflows and maintaining data hygiene using tools like n8n and Zapier, fostering a digital CX layer that connects sales, success, and support data for a unified customer view. Future CX leaders will need to blend empathy with automation skills, as AI handles routine tasks, enabling CSMs to concentrate on building meaningful relationships.

Merge is revolutionizing how companies integrate AI into their operations by mandating its use across all departments, from engineering to recruiting, to foster skill development and efficiency. Under the leadership of co-founder and CTO Gil Feig, Merge utilizes AI tools like Windsurf for code generation and review, emphasizing the importance of effective prompting to enhance productivity. While AI has shown significant impact in developing new products with minimal technical debt, its application to older codebases remains challenging. Beyond engineering, AI has been notably beneficial in recruiting, aiding in candidate identification and outreach. However, AI's influence in sales for enterprise deals remains limited due to the need for nuanced relationships. Merge prioritizes hiring for curiosity and adaptability over specific AI expertise, encouraging a culture of experimentation and continuous learning through small-scale AI projects. This cultural shift towards AI is reinforced by promoting AI usage as a default practice and celebrating innovative applications that boost confidence and foster a collaborative environment.

In 2025, customer support has significantly evolved with the integration of advanced AI technologies, transforming how teams handle inquiries and issues. AI tools now go beyond basic keyword-matching chatbots to systems that comprehend context, detect customer emotions, and autonomously route complex problems to the appropriate teams. This advancement includes conversational agents with memory, real-time knowledge retrieval for agents, sentiment and speech analysis, predictive ticket routing, and automated bug capture. These tools enhance the efficiency and effectiveness of customer support by assisting human agents with response suggestions, analyzing customer emotions, and routing tickets based on a myriad of variables. The implementation of AI in customer support is supported by platforms like Intercom, Zendesk, Forethought, and Jam, each offering unique AI-driven features tailored to different organizational needs. Despite the benefits, challenges such as data privacy, AI hallucination, and change management for agents remain critical considerations for successful adoption. The shift towards AI-assisted workflows aims to streamline support processes, reduce resolution times, and improve customer satisfaction while maintaining compliance with data protection regulations.

Wix is incorporating AI into its engineering processes by treating it as a productivity tool akin to a junior developer, useful for tasks like repetitive edits and documentation rather than making architectural decisions. Chief Architect Yoav Abrahami emphasizes the importance of defining guardrails and exposing internal systems to guide AI copilots effectively. With over 100 engineers focused on improving developer velocity, AI is layered into their optimization efforts, and its integration is frequently tested through proof-of-concept trials, guided by both quantitative metrics and developer feedback. The primary challenge lies in user experience design rather than the technical aspects of AI, as demonstrated by the year-long design process for their AI site-builder, Astro. Abrahami highlights the ongoing hype cycle in AI code generation, advocating for experimentation and iteration to discover real use cases. He foresees AI chat interfaces as the next major distribution channel, emphasizing the importance of structuring products and documentation for AI readability. Overall, Abrahami argues that while AI won't solve every problem, not leveraging it would be a significant oversight for engineering leaders.

Ricky Perez, Director of Support at Appcues, emphasizes the transformation of customer support through AI and the need for support teams to become more technically skilled to reduce the burden on engineering teams. He advocates for autonomous support teams that can debug and fix small issues, making handoffs to engineering more efficient and allowing engineers to focus on complex problems. AI plays a crucial role in summarizing ticket threads, triaging issues, and providing auto-replies for common requests, but should not replace human empathy in escalations. Perez highlights the proactive potential of AI, citing Appcues' Captain, an insights agent aimed at preventing issues before they arise. He also stresses hiring for empathy and curiosity, as AI tools lower technical barriers, enabling support staff to learn new skills. His approach aligns with trends among other leaders in support and engineering, as organizations increasingly use AI to enhance efficiency and customer satisfaction.

Patrick Leung, CTO of Faro, is leveraging AI to accelerate the traditionally slow and complex process of designing clinical trials in the pharmaceutical industry, aiming to save companies like Merck over $100 million per trial. AI enables Faro's team to prototype and iterate at unprecedented speeds, transforming laborious tasks typically conducted in Microsoft Word into efficient, AI-enhanced workflows. Despite the productivity gains, a new bottleneck has emerged in code review, prompting Faro to explore AI-assisted tools to maintain high standards of code quality. Faro's AI adoption extends beyond software development, with UX, QA, and DevOps teams automating various functions, allowing for broader operational efficiencies. Patrick emphasizes the importance of hiring engineers who are curious and adaptable, capable of integrating AI into their workflows, and believes that AI proficiency will be a crucial differentiator in the industry.

Vanta utilizes AI to enhance security and compliance automation, viewing it as a co-pilot rather than a replacement in their development processes. AI is effectively used in testing and communication, enhancing speed and clarity, while its role in hiring focuses on candidates' openness to learning and experimenting with new tools. The company adapts its interview processes to reflect real-world scenarios where AI tools are accessible, emphasizing code review over code generation. AI has allowed engineering leaders to engage more closely with the codebase by automating routine tasks, thus creating tighter feedback loops between management and execution. The impact of AI varies with seniority, accelerating senior engineers' productivity while sometimes slowing down junior engineers who may struggle to define quality outcomes. Vanta's integration of AI highlights the importance of focusing on areas with clear ROI, fostering curiosity in hiring, redesigning interviews to incorporate AI tools, and prioritizing a strong review culture to ensure code quality.

Lindsey Simon, VP of Engineering at Vercel, discusses the evolving role of AI in engineering, emphasizing the shift towards "vibe-codeable" systems where ideas drive the coding process, rather than simply accelerating code generation. He highlights that AI's impact on engineering velocity depends on the flexibility of existing systems, suggesting that modernizing codebases will unlock greater efficiency. As AI enhances code generation, the focus shifts to design clarity and articulating quality prompts, marking a shift from execution to ideation as the bottleneck. AI-driven test loops are transforming test-driven development into a powerful feedback mechanism, making it crucial for engineers to pass tests rather than obsess over implementation details. Vercel's hiring criteria remain unchanged, but candidates' ability to use AI for creative problem-solving and optimization is now a priority, signaling a move towards personal software where engineers build tools tailored to their individual needs.