At the recent HG Insights Revenue Growth Summit, the first day’s keynote served as a case study on how Oracle uses AI, machine learning (ML), and external data to transform their go-to-market (GTM) strategy. Throughout the session, Filip Vanacht, Vice President of Business Insights, Analytics, Sales Intelligence, and Global Revenue Operations at Oracle, speaks about moving from siloed processes to a unified, customer-centric strategy by using AI and ML and aligning with sales to create more balanced territories.
Why this session stood out:
- Get a behind-the-scenes look at how one of the world’s largest tech companies uses AI and ML to reshape GTM.
- Practical examples of using data to improve segmentation, quotas, and territory design are provided.
- See evidence of real business impact, including 2x conversion rates and more balanced, dynamic sales territories.
From siloed to customer centric
As Filip mentions in the session, Oracle has undergone a major transformation over the past few years. Once compiled of siloed teams and disconnected processes, the enterprise has evolved into using a more unified GTM strategy. This shift has been powered by blending internal data with external sources like HG Insights, giving Oracle the ability to focus on the customer journey rather than just internal metrics.
At the center of this change is a stronger commitment to customer success. By listening to clients’ needs and aligning solutions to their GTM strategies, the enterprise created clarity for its internal teams and customers. This helps build trust in long-term technology adoption — something that required years of investment from Oracle and its partnership clients.
Building a culture of customer focus
In the keynote, Filip highlights that this transformation was as much cultural as it was technological. Oracle moved toward a mindset of shared growth, recognizing that when customers succeed, the enterprise does as well. This perspective shifts the company from pushing products to identifying and nurturing the accounts and opportunities where its solutions could have the greatest impact.
Key takeaways:
- Oracle shifted from fragmented divisions to a unified GTM.
- Customer success became the foundation for both data strategy and culture.
- Growth now comes from focusing on the right accounts, not broad outreach.
AI and ML in practice
In the session, Filip makes it a point to underline that at Oracle, AI and ML are tightly governed tools that only get deployed when they’ve proven their value. His team does not implement an ML model until it is at least 90% accurate and precise on three years of historical data. This rigorous threshold makes sure that models are truly actionable for sales and GTM teams.
Filip also explains that there is a clear distinction between the two technologies. ML models are trained on Oracle’s proprietary internal data, a long but necessary process to achieve reliable predictions. AI agents, on the other hand, can act without training, providing a more dynamic way to automate actions and insights at scale. This combination allows Oracle to balance precision with speed, ensuring innovation doesn’t come at the cost of accuracy.
Real-world applications
These technologies have tangible outcomes; ML models now drive segmentation, quota setting, and territory design, making each process more precise and dynamic. What used to be static, spreadsheet-heavy exercises done annually at best can now be revisited quarterly. This agility means Oracle can respond faster to market changes and give sellers more balanced and productive territories.
Key takeaways:
- Oracle sets high accuracy standards for ML before rolling it out to avoid wasting time and risking trust.
- The company balances AI’s agility with ML’s precision to strengthen its go-to-market strategy.
- By making quota setting and territory design dynamic, Oracle has created more balanced, productive sales teams.
Smarter territories and sales alignment
Oracle recognized that its old approach to territory design was limiting growth. Their previous formula relied on only internal data, which tended to overvalue existing customers and undervalue new opportunities. This meant sales teams were focused on “farming” existing accounts rather than balancing efforts between nurturing current clients and pursuing new business.
By incorporating external data and applying ML models, Oracle was able to formulate its territory design. The result, as Filip mentions in the session, was more balanced territories for its 50,000 sellers, giving them clearer guidance on where to focus their efforts. This transformation took two years to validate because their team had to ensure the ML model was 90% accurate before full deployment. It now actively guides sales in Q1 of Oracle’s fiscal year.
Aligning marketing and sales
Siloed teams are a tale as old as time, and the territory reformulation alone wasn’t enough to flip the script. Filip highlights that the integration of marketing and sales was just as important in maximizing impact. By creating a continuous feedback loop and running A/B tests, the teams discovered that providing enriched intelligence to Sales Development Representatives (SDRs) doubled conversion rates compared with reps who didn’t have that information. This tight integration ensures that marketing campaigns and sales efforts are working together toward shared goals, rather than operating in isolation.
Key takeaways:
- Oracle used ML and external data to create more balanced territories, correcting biases toward existing accounts.
- Validation over two years ensured the model was accurate and actionable for tens of thousands of sellers.
- Integrating marketing and sales through intelligence and feedback loops led to measurable improvements in conversion rates.
Oracle’s journey shows how combining data, AI and ML, and strategic alignment can transform a massive go-to-market operation. By breaking down silos, focusing on customer success, and applying ML to territory design and quotas, the company created a more dynamic, precise, and customer-centric approach.
The results are tangible: territories are balanced across 50,000 sellers, marketing and sales work in close partnership, and enriched intelligence drives higher conversion rates. Most importantly, Oracle has shifted its culture so teams focus on insights and impact rather than just processing data, setting a model for any organization aiming to scale effectively in our data-driven world.
Watch the full session and explore other highlights from the HG Insights Revenue Growth Summit to see how leading companies are leveraging data, AI, and GTM strategy to drive growth.