Deeply embedding AI into a company’s operations is certainly not an overnight process.
Doing so, however, has compounding benefits. Organizations will realize more value from the same amount of effort as they improve their abilities and get more people both creating and using AI projects.
In order to reach this ideal state (and avoid overall project failure), though, organizations need to achieve goals at both ends of the spectrum:
- Quick, high-impact AI wins (because teams can’t afford to wait any longer, especially in the era of AI agents)
- Long-term AI transformation (because thinking about AI implementation on a use-case-by-use-case basis for the long term isn’t sustainable or economical)
Read along for the “how” on both fronts, along with ways Dataiku can help teams move from theory to practice when it comes to the concepts discussed.
How to Deliver on Quick, High-Impact AI Wins
In order to even think about long-term AI ambitions, organizations need a short-term strategy for proving the value of AI and how it can help make processes more efficient and decision making more precise.
This section highlights a few key ways to drive short-term success (and how Dataiku can act as a catalyst in that success):
Define an Initial Set of Use Cases
The initial set of uses should be a balance between ones that have the potential for high impact yet aren’t too difficult to tackle or take too long to deploy. An ideal AI project will have clear answers to each of the following questions:
- WHO will this project benefit?
- HOW will it specifically improve experience or outcomes, and HOW can this be measured?
- WHY is using AI for this purpose better than existing processes?
- WHAT is the upside if it succeeds, and WHAT are the consequences if it fails?
- WHERE will the data come from, and does it already exist?
- WHEN should an initial working prototype and, subsequently, a final solution in production be delivered?
Accelerate on Table Stakes Use Cases With Dataiku
Dataiku helps customers build and deploy their first flagship use cases more quickly, either via support from our in-house team of experienced data scientists or by leveraging our pre-packaged solutions.
These solutions are Dataiku add-ons that accelerate advanced and basic industry-specific use cases. They are an operational shortcut to achieve real-world use cases designed with the purpose of business value generation. Taking advantage of Dataiku’s core features, they are built to be fully customizable and entirely editable. They come with:
- A user-friendly interface that enables fine tuning to match with specific business requirements
- Ready-to-use dashboards that can be optimized
- Documentation and training materials
To take a real-life example, a small team of data scientists at an online fashion retail startup were able to build and deploy a market basket analysis solution in two hours, from start to finish. Without Dataiku, this would have been a weeks-long project. After performing the necessary quality control checks, the solution was deployed into production, informing the team of which products were typically purchased together, allowing them to improve their marketing and logistics.
Know Who to Tap as Ambassadors & Early Adopters
In order to get to the stage of long-term cultural transformation via an AI program, an organization needs ambassadors and early adopters to champion their triumphs — what use cases worked, how they were executed, and what the results were for the business.
The question is, though, who are these people?
- Business teams, who can evaluate and quantify the value that they’re seeing in ways that will resonate with other teams and other business units around the company
- Individual users, usually power users who can both evangelize and recruit other users plus solidify perception of AI initiatives as a positive force in the company, not just a top-down demand
- Team leads and managers, who are often helpful when it comes to driving upskilling programs not just within their own teams but across the entire organization, so getting a few on board as early adopters is key
- IT managers, essential for effective, smooth roll-out of any technology as well as — from a more philosophical perspective — critical for instilling a culture of access to data balanced with the proper governance and control
How to Attain Long-Term AI Transformation
There will inevitably come a point in time where the economic value of short-term AI initiatives decreases. This section will demonstrate how organizations can shift to a more holistic, scalable vision of value creation.
Scaling to achieve this future state requires a fundamental shift in company culture, adopting processes and capabilities that will reduce the cost of each incremental AI use case.
Establish AI Governance Practices for Strategic Alignment and Steering
AI governance delivers end-to-end model management at scale, with a focus on risk-adjusted value delivery and efficiency in AI scaling, all in alignment with regulations. It sits at the intersection of value-based (responsible AI) and operational (MLOps) concepts.
A key component of a modern AI governance strategy is finding a balance between governance and enablement that will allow this future state of AI to flourish. Put simply, governance should not — and cannot — be a blocker to innovation.
Teams, therefore, need to make distinctions between proof-of-concepts (POCs), self-service data initiatives, and industrialized data products, as well as the governance needs surrounding each. Space needs to be given for exploration and experimentation, but teams also need to make clear decisions about when self-service projects or POCs should have the funding, testing, and assurance to become an industrialized, operationalized solution.
Reduce Risk and Remove Friction With MLOps
Closely related to AI governance is MLOps, which focuses on end-to-end model management, from data collection to operationalization and oversight. Organizations need systems to monitor pipelines, models, infrastructure, and services to make sure they are doing what they are supposed to.
A robust machine learning (ML) model management program would aim to answer questions such as:
- Who is responsible for the performance and maintenance of production ML models?
- How are ML models updated and/or refreshed to account for model drift?
- What performance metrics are measured when developing and selecting models, and what level of performance and risk is acceptable to the business?
- How are models monitored over time to detect model deterioration or unexpected, anomalous data and predictions?
- How are models audited, and are they explainable to those outside of the team developing them?
Having MLOps processes in place is critical to not only avoiding AI project failure, but achieving long-term success with AI. Learn more about unified AI Ops (including DataOps, LLMOps, and AgentOps) here.
Formalize Education & Upskilling Programs for User Adoption
This long-term transformation is such a massive shift that it requires a lot of time, energy, and resources via personalized, multi-step training, ingrained in the company strategy and culture and inclusive of hard and soft skills.
Upskilling people across an organization is a huge challenge. Diverse skill sets and needs mean one-size-fits-all training will very likely fail, but more specialized training requires more time, effort, and resources.
Here are some proven tactics we’ve seen work amongst Dataiku customers:
- Start with an assessment of skills to learn where gaps lie (i.e., evaluate the tech skills of business people and vice versa).
- Create a training plan based on long-term upskilling goals and existing skills.
- Create tracks based on existing skills or responsibilities. Dataiku customer GE Aviation offers in-depth 100-, 200-, and 300-level courses to upskill and onboard end users to their self-serve data efforts in addition to a full-day executive training that is more focused to suit their needs.
Putting It All Together
Crafting a reliable, short-term strategy that proves the value of AI to skeptics as well as a long-term strategy that initiates a full, organizational transformation are both key elements for AI success at scale.
At Dataiku, our biggest customers rely on us to drive analytics speed and agility, but with an overarching organizational control and governance. Having a platform that can facilitate and address both is invaluable because:
- Upskilling becomes easier with technology that facilitates the development of AI skills in a centralized, controlled, yet creative environment. Dataiku is the best of both worlds: fit for expert users who want maximum flexibility, but also suited for beginners who need guardrails and guidance.
- Developing robust AI governance or Ops processes is within reach by leveraging a tool that has built-in capabilities to facilitate and enforce their implementation. Dataiku allows AI creators and consumers to understand model outputs and increase trust as well as have high-level views of governance and Ops processes.
- Short-term time-to-value can be sped up with out-of-the-box solutions for your industry’s most pressing use cases, which Dataiku offers and continues to release a robust catalog.