Podcast Summary: SaaStr 836

The Step-By-Step Playbook for Building AI-Powered GTM Teams with Personio's CRO

Date: January 7, 2026
Host: SaaStr
Guest: Philippe Lacour (CRO, Personio)
Special Guest/Moderator: Emilio (SaaStr)

Episode Overview

This episode features Philippe Lacour, Chief Revenue Officer at Personio, sharing his detailed playbook for transforming traditional go-to-market (GTM) teams into AI-powered engines. He draws lessons from Personio’s rapid adoption of AI, offering tactical advice for SaaS leaders and founders seeking practical ways to integrate AI into their sales, marketing, and customer success functions. The conversation includes best practices, pitfalls, and tactical anecdotes from Personio’s ongoing journey.

Main Discussion Points

1. Background: Personio’s AI Journey

• AI Search Week Launch:
  • In May, Personio organized an "AI Search Week," inviting speakers from OpenAI, AWS, and Mistral, giving all employees hands-on access to large language models (LLMs).
  • Over 90% of the GTM team now uses LLMs weekly.
  • "The entire company was buzzing. [...] However, we felt that was maybe not enough to reach true transformation." (Philippe, 02:30–03:22)
• Initiating Real Change:
  • The company launches an “AI Powered Go To Market” Slack channel (mid-June).
  • Five key lessons and four in-depth use cases are presented.

2. Five Lessons Learned in AI GTM Transformation

• a) Combine Bottom-Up and Top-Down Approaches (03:30)

  • Bottom-Up: Equip everyone with tools, training.
  • Top-Down: Leadership must prioritize AI, allocate resources, and grant permission for process redesign.
  • "It’s very important that in the journey from experimentation to scale... you also have like a top down decision making." (03:42)

• b) Cross-Functional Collaboration (04:25)

  • Essential to bring together Data & Systems, RevOps (including new “Go-To-Market Engineers”), Marketing, Sales, and Customer Success.
  • The initial working group included ~15 people for broad functional coverage and culture change.

• c) Ruthless Prioritization (06:58)

  • Starting too many projects can cause chaos; frameworks (jobs-to-be-done, customer journey mapping) are used for systematic prioritization.
  • "We had more and more opportunities... then the ideas started flowing... but we hadn’t finished the first one. So... we need a better framework to prioritize." (06:58)

• d) Culture of AI (Curiosity & Buy-In) (09:12)

  • True transformation relies on curiosity and openness; change is driven as much by “acceptance” as planning.
  • Engraining AI practices into habits: role modeling, internal showcases, and celebrating champions ("President's Club" now includes seats for best AI contributions).
  • "The acceptance part and the creating of buy-in and getting people comfortable... is a very important part." (09:12)

• e) Stack & Context (13:15)

  • Don’t chase every tool—start with your current stack (Salesforce, Snowflake, Gong, Qualified) and clean your data sources first.
  • Add company-specific context and knowledge to LLMs for best results (“battle cards,” ICP definitions, internal docs).
  • "Great AI comes from your stack. But then, equally important, your context." (13:15)

Four Key Use Cases (Practical Applications)

1. Win/Loss Analysis with AI (19:31)

• Problem: Salesforce “reason” fields are vague and outdated (“30% other”).
• Solution: Feed 5,000+ Gong calls and emails into a GPT to analyze actual win/loss drivers, dynamically update “battle cards,” and deliver competitive insights.
• Benefits: Deeper product feedback, data-driven strategy, dynamic materials for sales and product teams.
• "The goal was to really better understand our loss reasons, our win reasons and really also get competitive, competitive insights." (19:31)

2. Expansion SDR Assistant (21:40, see also intro at 00:11)

• Problem: SDRs spent ~2 hours/day gathering data for cross-sell.
• Solution: AI assistant connected to Salesforce/Snowflake fetches all account-relevant information in seconds and recommends next actions (account status: green/yellow/red).
• ROI: Research time reduced from 2 hours to 15 minutes per day; pipeline per FTE doubled.
• "For example... research time that an ESDR spends on this work went from two hours a day to 15 minutes." (00:11, 22:10)

3. Intent Scoring and Outbound Targeting (26:15)

• Pipeline Building: Combine LLM-powered personalization with custom-built dynamic account and intent scores (website visits, G2 reviews, user moves).
• Automation: Integrate all scores and signals in Salesforce to create actionable, prioritized prospect lists (with “flames” as intent signal).
• Iterative Tuning: Regularly fine-tune models based on outcomes to improve signal quality.
• "We show these little flames. So three flames, that's where you start." (28:00)

4. AI SDR / Chatbot for Inbound (29:40)

• AI SDR, “Nia”: Qualified chatbot handles real-time website chats, auto-books demos, answers product questions 24/7.
• Insights: Leadership, including Philippe, reads chat logs daily—direct window into prospect needs and process gaps.
• Continuous Tuning: A dedicated manager (Amelie) reviews outputs daily to avoid pitfalls—e.g., legal/product mistakes, negative competitor comments.
• "I got totally hooked on it. I'm reading these now every day... my poor team over the weekend." (30:49)
• "It is surprising to see what the AI is already able to do." (31:30)

Q&A and Practitioner Insights

On Leadership and Change Management (38:09–41:06)

• Pressure for AI adoption came from Personio’s founder CEO, motivated by VC/board attention:
  • "Our founder CEO kicked off this big search week and that was a sign for the company. Okay, we're going to be AI first SaaS." (40:39)
• Philippe self-educates via LLMs and SaaS AI content, reinforcing the need to "seize the moment and lead from the front." (41:23)

On AI Maintenance and Human Adoption (42:10–45:08)

• Agent management is “addictive” but “pretty complex”; intricate user flows are hard to pre-empt with rules. (42:57)
• Continuous tuning is required, especially to avoid errors or missed opportunities.
• 90%+ of team uses LLM assistants weekly.

On Team Impact, Hiring, and Reallocation (45:52–47:56)

• No revolts: AI embraced; jobs will evolve ("lean into AI" is best career advice).
• "The default should be like, can I solve this with AI? [...] Can we double the business with the same headcount?" (46:37)

On Use Cases and the Future (48:36–50:53)

• Next focus: automating CRM admin (“handoffs"), deeper cross-sell automation, "next best action" for account managers.
• Planning to apply AI to renewals, but evaluating data governance.
• Not worried about resource allocation if AI closes deals—more focused on efficiency and growth.

On Budget and Pitfalls (51:05–51:45)

• Significant AI investment ("each SDR agent about $100,000").
• Biggest mistake to avoid: "Endlessly testing tools. [...] It's about doing AI instead of learning AI." (51:45)

Notable Quotes & Moments

• On Scaling AI:
  • "When you want to drive more transformation, you really need to prioritize." (Philippe, 06:58)
• On Adoption:
  • "We try to make AI a habit by leading it, by showing it, and by celebrating it." (Philippe, 09:12)
• On ROI:
  • "The ROI isn't instant, but I do think it shows up in many places—deal velocity, pipeline velocity, customer retention, pipeline quality, win rate..." (36:40)
• On Advice to Peers:
  • "If you try to read all the papers and not do anything, I don’t think you'll move fast." (Philippe, 51:45)
• On the Future:
  • "I wake up every day and think, we need to go faster, we need to go faster. But that is the opportunity for everybody." (Philippe, 37:45)

Key Timestamps

• 02:30 – Philippe introduces Personio, AI Week, and transformation thesis
• 03:30 – 5 Lessons on AI-powered GTM
• 13:15 – Technical stack and cleaning data
• 19:31 – Use Case #1: Win/Loss AI analytics
• 21:40 – Use Case #2: Expansion SDR Assistant
• 26:15 – Use Case #3: AI-powered outbound/intent
• 29:40 – Use Case #4: AI SDR/Chatbot ("Nia")
• 38:10 – Q&A and leadership journey
• 41:23 – Adoption and self-education
• 46:37 – Potential headcount impact and “big question”
• 51:45 – Common mistakes to avoid

Takeaways for SaaS Leaders

• Start with What You Have: Clean data, add contextual knowledge to your stack.
• Dual Approach: Enable bottom-up experimentation, but drive with clear top-down support, structure, and priorities.
• Cross-Functional Engagement: Data, RevOps, and Business must work in sync.
• Continuous Delivery and Tuning: AI must be actively managed; dedicated roles (like the “Nia” lead) are valuable.
• Measure ROI Broadly: Automation, happiness, and growth—look far beyond “cost savings.”
• Act, Don’t Overthink: Avoid analysis paralysis; select a few tools/areas and go deep with both effort and training.

How to Connect

• Reach Philippe Lacour on LinkedIn.
• For further questions about Personio’s AI experiments, "ping me on LinkedIn or Clay." (52:10)