What does it take to truly integrate AI into a company’s core strategy? In a 2024 Everyday AI San Francisco session, “Sticking the Landing: Making the Leap to AI, and Surviving to Tell the Tale,” industry leaders from Palo Alto Networks and Prologis explained how they are infusing AI into their business operations, prioritizing AI use cases, and achieving organizational alignment — with support from Dataiku.
Palo Alto Networks — AI in Cybersecurity and Customer Support
Palo Alto Networks is a leading cybersecurity company, committed to making each day safer than the last by offering a comprehensive enterprise cybersecurity platform. This includes network security, cloud security, endpoint protection, and a set of cloud-delivered security services.
Key AI Use Cases at Palo Alto Networks
Lionel Some, a principal data scientist at Palo Alto Networks, highlighted several AI use cases that his team is working on:
- Customer Retention Models: These models monitor customer health to enhance retention efforts, predicting potential churn and enabling proactive interventions.
- Escalation Management Platform: Developed during a company hackathon, this platform predicts the likelihood of support tickets escalating, thereby helping the support team prioritize tasks and reduce resolution times, ultimately improving customer satisfaction.
- Content Generation for Marketing: A Retrieval Augmented Generation (RAG)-based application designed for the marketing team that automates the generation of content such as blog posts, press releases, and articles, streamlining the content creation process.
Prioritizing AI Use Cases
Prioritizing AI use cases at Palo Alto Networks is a structured process that involves multiple teams. First, business leaders submit requests through a business intake form. These requests are then evaluated by an AI model team, which includes data science, information security, and legal experts. Each team assesses the feasibility, risks, and value of the project from their unique perspectives, ensuring a holistic evaluation.
Today, the Palo Alto support team uses an app built with Dataiku by internal teams, the Escalation Management Platform mentioned earlier. Born out of a hackathon hosted by the Palo Alto IT organization, this platform helps predict the likelihood that a support ticket will escalate. This initiative was fast-tracked due to its feasibility and the business impact it could deliver. This project exemplifies how Dataiku was instrumental in rapidly moving from data preparation to model building and integration, allowing the team to focus on the application’s presentation and functionality.
Ensuring Transparency and Explainability
Lionel's team, composed of nine data scientists, is focused on assisting customer support, sales, and marketing teams through various AI-driven projects. In all of these projects, there is a significant focus on ensuring transparency and explainability of AI models. Making stakeholders understand how AI models work, what data they use, and the constraints they operate under is necessary to bring projects to production and ensure everyone is on the same page.
For example, when presenting their RAG-based content generation tool to the architecture team, Lionel’s team received a request to increase visibility in what was happening behind the scenes. The team thus added features to the UI that allow users to see the source chunks retrieved from a vector database, fostering trust and enabling validation of the tool’s functionality as well as rapid feedback.
For me, transparency is about explaining what goes into the model, how the model works, and also what constraints we have to work with. Explainability, on the other hand, is about providing a description of what the model outputs or how the model output came to be.
— Lionel Some, Principal Data Scientist at Palo Alto Networks
Explainability was all the more important for Palo Alto as they were replacing existing rule based systems, which are usually easily interpretable, by more complex ML models that are not as interpretable. For these new models, Lionel’s team provided explanations using models like SHAP to clarify how the model outputs were derived.
This transparency and explainability are critical in maintaining stakeholder trust and ensuring the successful adoption of AI tools through feedback loops and continuous improvements.
Prologis – AI in Logistics and Operational Excellence
Prologis is the world’s largest logistics real estate company. It currently manages over 1.2 billion square feet of logistics real estate, playing a unique role in the supply chain and serving businesses of all sizes.
Prologis’ AI Journey
Luke Slotwinski, VP of data & analytics at Prologis, and Jennifer Garcia, a lead AI/ML engineer on Luke’s team, provided insights into Prologis’ AI journey, which began with a traditional data warehouse setup and evolved into a sophisticated cloud-based environment that uses Snowflake for data warehousing and Dataiku for AI/ML development.
From a priority perspective, Prologis aligns their AI use cases to the company's business strategy. These include improving operational efficiencies, enhancing decision-making, and driving business growth. The main considerations for every AI use case are strategic alignment, technical fit, and the potential impact on business decisions.
For example, as the company continued to diversify, the data and analytics team followed by moving their data stack to the cloud with Snowflake. They wanted their data and AI efforts to always accurately reflect the company’s position and identity.
The company’s AI journey started with descriptive analytics including management and operational analytics. The goal of this first use case was to clearly communicate what was happening in the company. To do this, the team looked at proper data modeling and delivered value from there. As Prologis continued to evolve, the data and analytics team shifted towards more advanced use cases, including geospatial analysis and predictive modeling.
Overcoming Challenges in AI Deployment With Dataiku
Before Dataiku, data scientists at Prologis would build models on their local machines, often with data extracts, and then hand them off to the data and analytics team for deployment. This process was time-consuming and had some challenges related to system dependencies and packaging.
To streamline this process, Prologis adopted centralized development platforms like JupyterHub servers, which allowed for seamless integration of data science code into production environments. However, the need for coding skills was still a barrier. This led Prologis to explore auto AI/ML platforms like Dataiku, which democratized AI by enabling analysts to build and deploy models without extensive coding knowledge.
Analysts can come in, they get seamless integrations with all of our data source systems. They can do EDA, build models, build web apps or dashboards. It's very powerful and it's enabled a lot of our users.
— Jennifer Garcia, Lead AI/ML in Data & Analytics at Prologis
Stakeholder Alignment for Significant Results
The technology, frankly, is the easy piece of most problems that we solve.
— Luke Slotwinski, VP of Data & Analytics at Prologis
According to Luke, the more challenging parts of any project are making sure that the project aligns to the company's strategic goals and having a dedicated business leader invested in the success of each AI project. This leader’s role is crucial in ensuring that the AI tools developed are integrated into daily decision-making processes, which is essential for driving adoption and realizing value from AI investments.
Prologis ensures this by partnering with their in-house operational excellence team. This team focuses on process engineering, process adherence, and standard work. They ensure that deployments fit into a business process, have a decision attached to them, and have a clear business process step that measures the efficacy of the project.
Navigating Emerging Technologies
As AI technologies evolve, companies must adapt their strategies to incorporate new capabilities. Prologis only makes a few select choices when it comes to technologies in which they would be deep-rooted — like Dataiku or Snowflake.
When it comes to emerging technologies, like Generative AI, they focus on tools that enable easy transitions. This includes modular architecture and interoperability, allowing them to plug and play new components as needed without becoming entrenched in any single technology. This approach ensures flexibility and agility as the AI landscape continues to evolve.
When considering new technologies, Prologis does their research, narrows down the list, and then conducts quick proofs of concept to evaluate their feasibility and potential impact. If a technology proves valuable, it is integrated into their AI strategy; if not, they move on without significant time investment, maintaining their focus on initiatives that will move the needle for the company.
Conclusion: Leveraging AI for Strategic Advantage
From Palo Alto Networks’ focus on transparency and explainability to Prologis’ emphasis on strategic alignment, this session highlights how organizations are leveraging platforms like Dataiku to democratize AI, enabling broader participation in AI development and deployment and, ultimately, driving greater business value.