3 Keys to Scaling AI

Dataiku Product, Scaling AI, Featured Marie Merveilleux du Vignaux

AI can change the way you do business, but the truth is that most companies are still at the very beginning of their AI journey. The value of AI will not come from a single project but from embedding AI into all processes across your business. That is when you will see growth in revenue, customer satisfaction, or other KPIs.

Many companies already have data scientists and maybe even a few successful AI projects in production, but still face the challenges all organizations run into when attempting to scale AI. In this blog post, we will go over three of the five key challenges presented by Dataiku Chief Technology Officer Clément Stenac during the 2021 Dataiku Product Days.

→ Watch the Full Video for a Complete Overview of the 5 Main Challenges

1. Access: In order to scale AI, more people across the enterprise must drive value from it. Not everybody can be a data creator or designer, but many individuals in the company are potential consumers of insights. So how will you get AI into the hands of everyone? How do you make AI consumable in your business?

Access is about enabling more people across the enterprise to create and engage with data. Dataiku continues to invest in the user experience for business analysts so they can collaborate when building datasets, reports, dashboards, and even full-fledged apps directly in Dataiku.

2. Trust: In order to scale AI, everyone needs to trust that machine learning efforts are in line with expectations and business outcomes. You do not only need to build AI, you also need to build trust in AI.

Dataiku believes trust in AI comes from a variety of sources and indicators. This includes understanding how a predictive model behaves. Dataiku has invested in explainable AI capabilities to help data teams and business stakeholders both at model design and model usage stages to understand model behavior, why a model makes a certain prediction, and the bias in models. Dataiku also has a built-in audit trail which logs all actions performed by users, allowing for advanced monitoring and simplifying compliance constraints.
team working with Dataiku platform on computer3. Collaboration: Delivering AI at scale is not an initiative for just one team or department. A central data department cannot own the entirety of AI within a company. While AI initiatives may start with a business domain expert and a data team, production projects quickly involve IT and other business units. So how are you enabling collaboration between people and teams?

Dataiku has always been a leader in collaborative AI for data teams and we continue to invest in ways for more people to engage in development of data-driven projects and for teams to collaborate on AI as they scale. One of Dataiku’s collaborative features is its single place for discussion and data projects — a win for governance — that synchronizes with other collaboration platforms such as Slack, Atlassian Confluence, or Microsoft Teams. But it doesn’t stop there — check out all the features in Dataiku that take collaboration to the next level.

In order to scale the adoption of AI, companies must put systems in place to systematically attack these challenges. The rewards they will experience can be extremely high. Streamlining AI adoption will deliver a competitive advantage and value to the business in terms of better and faster decisions, automated processes, and improved customer experience that generate more revenue at lower costs.

You May Also Like

What Is DataOps?

Read More

What Does It Take to Democratize AI? Insights From the Field

Read More

Maintaining The Human Factor in Machine Learning

Read More

Navigating Targeted Ad Bias With Responsible AI

Read More