Let’s face it — Generative AI will continue to be the star of the show in 2024. Our commissioned study with Forrester surveyed 220 AI decision makers at large companies in North America in November 2023, and a whopping 83% of them are already exploring or experimenting with the technology.
But AI success hinges on a comprehensive organizational understanding of effective data and analytics processes beyond Generative AI alone. With that in mind, here are four fundamental elements that will be critical for Generative AI success in 2024.
#1: Empowering Domain Experts
In their recent study State of AI, the McKinsey Global Institute identified 63 Generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries.
However, it will be impossible to unlock this value without shared accessibility. Data-savvy domain experts possess specialized knowledge and frontline involvement in business use cases, so they hold the key to generating maximum value.
Democratizing data and AI means a few things in this context. First, organizations must do more to enable these data-savvy domain experts with thoughtful support so they can contribute to the delivery of data products.
That means providing them with reliable data as well as self-service data cleansing capabilities, all within a user-friendly environment. It also means giving domain experts the ability to create, enhance, and disseminate their expertise into data products with ease, whether that means insights derived from classic predictive or innovative Generative AI methods.
But democratizing AI also means safely putting Generative AI applications in their hands, and into the hands of all business users — even if they’re not the ones building those use cases themselves. For example, LG Chem noticed that their employees were spending a lot of time searching for safety regulations and guidelines so, with the help of Generative AI and Dataiku, they provided an AI service that helps them find that information quickly and accurately.
#2: Enabling Data Experts to Move Faster
Data scientists often grapple with the dual complexity of developing machine learning (ML) models and managing sophisticated point tools and systems at the same time. The rise of Generative AI only adds to this challenge, making it even more burdensome to develop, deploy, and monitor models from across the business.
Data scientists deserve a robust environment that accelerates their ability to build both ML and Generative AI applications, eliminating the need for constant tool switching and poorly supported integrations. In light of the pace of innovation in Generative AI, more than ever they need simplified oversight, orchestration, and implementation, all while adhering to the security and compliance frameworks established by IT. In the face of escalating model intricacies and the expanding landscape of advanced AI use cases, this capability has become increasingly indispensable.
Dataiku explicitly empowers data scientists in the age of Generative AI with enhanced large language model (LLM) orchestration via the LLM Mesh as well as optimized fine-tuning and structured prompt optimization through Prompt Studios — all in a familiar workspace.
Of course, it’s not just about orchestration of Generative AI use cases. Data scientists and programming enthusiasts can also move faster in their day-to-day via Generative AI-powered acceleration features within Dataiku. For example:
- While crafting Python scripts, they can use AI Code Assistants to swiftly explain functions or generate code.
- AI Explain produces explanations for code recipes and workflows for technical audiences, domain experts, and/or executives with a single click, eliminating extensive documentation and promoting standardization. Standardization also extends to code environments, ensuring data scientists work with identical package dependencies as their collaborators. This maintains code health and robustness while upholding consistent project standards.
#3: Orchestrating AI Growth
With the fast-growing field of Generative AI, investing solely in one LLM is not a viable option. A single LLM system doesn't address all needs, and it can prove risky in the long run due to the effects of vendor lock-in, where your strategy is no longer guided by your choices but by those of your sole provider.
Enter: The LLM Mesh. In 2023, we launched this differentiated solution, enabling users to orchestrate and benchmark LLMs with a coordinated approach. In 2024, the LLM Mesh will continue to be a strong block of Dataiku’s orchestration strategy by helping any organization to scale Generative AI no matter the underlying environment.
.png?width=700&height=631&name=LLM%20Mesh%20Visual%20(1).png)
Dataiku’s LLM Mesh is a well-designed bridge that facilitates seamless communication, resource sharing, and collaboration for Large Language Models (LLMs). Thus, Dataiku now provides the same kind of robust data service to LLMs as it does to other-species AI. [Organizations] who are considering adopting Gen AI would be wise to investigate Dataiku’s capabilities.
— Robin Bloor, “Is Dataiku the key to unlocking Gen AI’s potential?”
Dataiku’s LLM Mesh not only facilitates a harmonized approach but also serves as an essential tool for cost management and performance optimization. It meticulously monitors the cost per query to various LLMs, aggregating these expenses by application and service, thereby enabling teams to accurately forecast expenditures and make judicious decisions regarding service utilization.
The LLM Mesh also enhances operational efficiency by caching responses to frequent queries, eliminating the need for repeated response generation, which translates to both cost savings and performance enhancement. What’s more, it maintains a comprehensive and fully auditable log, detailing the usage of each LLM and service for specific purposes. This not only facilitates cost tracking and potential internal re-billing but also ensures complete traceability of both requests and responses to these occasionally unpredictable models, underscoring its pivotal role in efficient and transparent AI project management.
#4: Instill Trust & Transparency While Scaling
A recent survey by BCG X revealed that 52% of senior leaders are actively discouraging the use of Generative AI. When questioned, the reasons for this lack of trust revolve around the origin of the data, transparency, or the privacy of data used by Generative AI, all of which can be subject to skepticism.
To (safely) sweep away this sentiment and avoid falling behind in AI adoption, it is more crucial than ever in 2024 to build systems that are transparent and governed. Yet trust is not straightforward given the multitude of projects, specific models, and different LLMs in play.
The LLM Mesh is part of the answer, but instilling trust — whether in data, analytics, ML, or Generative AI — requires visibility, transparency, and auditability across the entire organization, among all stakeholders, and in all systems.
Ahead of impending regulatory changes in 2024 (including the EU AI Act), Dataiku Govern is a single place to track the progress of multiple data initiatives, Generative AI-related or otherwise, and ensure the right workflows and processes are in place to deliver responsible and transparent AI.
If your teams develop models in third-party environments like Google Vertex, Azure ML, or Sagemaker, the need for a unified operationalization environment is even more pressing. In 2023, Dataiku pioneered the ability to start in a third-party system and operationalize within the same environment. These features will be expanded in 2024 through a unified monitoring approach, allowing you to track all deployed models from any platform.
If you need yet another reason to check out Dataiku as a solution for building trust and AI, IDC recognized us as a Leader in their MarketScape for AI Governance in November 2023, confirming our commitment not only to accelerating AI, but ensuring confidence in its operation and results.