During a recent webinar, I discussed AI and its dependence on data foundations, emphasizing how successful AI projects—those that move beyond pilot phases, mitigate risks, and deliver measurable value—hinge on trustworthy and reliable data.
We pinpointed four crucial areas for data infrastructure:
• User Focus – Aligning the AI products, data, and goals with the needs/goals of end users
• Comprehensive Models – Creating the taxonomies, ontologies, schemas, etc that describe your organization’s domain consistently across your systems, business units and interfaces
• Organizational Alignment – Ensuring that strategy, data, technology, KPIs, and resourcing are all working together
• Stability – Ensuring that the user understanding, models, and organizational alignment are maintained going forward.
Stability’s emergence as a key area was unexpected. User understanding, robust model creation, and organizational data alignment are widely recognized principles, forming core tenets of Factor’s methodology. Stability has never been an explicit area of focus, although I believe it has been one of our core concepts nonetheless. The scale and reach of AI projects just helped bring it to the forefront.
What makes stability different from the other three areas of focus is that it’s more than a particular task or set of tasks. There are particular tasks that can be associated with understanding users, building models, and driving organizational alignment, but stability is as much a mindset and process as it is any specific action.
When we talk about stability in AI, we’re really talking about building a solid platform. Think of it as the base for all our AI tools and solutions. It’s about providing consistent data, validation, and usage with all the core parts that make AI work, from development to deployment and ongoing operations.
Why is this so important? Because AI projects are complex and a big investment. For them to truly succeed–in other words, to be trustworthy and actually useful, rather than just impressive–we need a stable foundation. AI systems are always learning and changing with data, algorithms, and models. Their dynamic nature means we need a steady base that won’t suddenly shift.
Consistency: The Key to Trust and Efficiency
At the core of stability is consistent behavior. This is crucial for both the data AI uses and the information that defines how it operates. Consistency isn’t a nice-to-have; it’s essential for everyone involved in the AI journey:
• AI Model Creators: Data scientists and machine learning engineers need steady and predictable data streams to train and fine-tune models. Messy data can lead to skewed results, biased predictions, and AI you can’t rely on. A stable data environment lets them work confidently, knowing their models are learning from predictable and representative data over the long run.
• Tool Developers: Software engineers and platform architects who build AI development and monitoring tools need a stable base. They connect different components, libraries, and frameworks, so frequent, unannounced changes to foundational elements means a lot of re-engineering, delays, and potential problems.
• Rule and Process Folks: For organizations and individuals setting the rules and processes around AI, consistency in the underlying infrastructure is absolutely vital. This includes ethical guidelines, governance, and compliance. When the foundational elements are stable, it allows for strong, enforceable rules that can be applied consistently, building trust and accountability.
• End Users: Ultimately these tools need to be used by people. Tools built on a stable base will provide consistent and trustworthy results. Users will soon learn what a tool is most valuable for. If the capability, trustworthiness, or focus of the tools changes frequently then the tools themselves will lose their value and end users will abandon them.
A stable AI platform frees these key players from constantly worrying about disruptive changes. Instead of spending precious time adjusting to shifting landscapes, they can focus their expertise on innovating, optimizing, and making AI solutions even better. This dependable foundation leads to:
• Predictability: Developers can guess how their models and tools will behave, which means fewer surprises and more confidence in their deployments. A solution that worked yesterday will likely work tomorrow.
• Scalability: A stable foundation makes it easy to grow and extend AI operations. New models and applications can be built on the existing work.
• Maintainability: Consistent infrastructure simplifies upkeep and troubleshooting, making it faster to find and fix issues.
• Trust and Adoption: Ultimately, a stable and consistent AI environment builds trust among users, stakeholders, and the wider community, encouraging more people to adopt and innovate responsibly.
What does all this look like in a practical sense? It looks like having processes in place that ensure data is collected in a coherent way, and that it’s encoded, decoded and stored in ways that are predictable, transparent, and useful to the artificial intelligence infrastructure that’s being built out.
So many organizations are trying to get past the proof of concept phase with AI. They’re trying to understand not only how to get AI to work, but how to get it to work in the long run and create consistent ROI. These are the organizations for which this notion of stability is essential.
It’s what’s going to allow them to reap long-term rewards from their investment of hundreds of thousands or millions of dollars into AI development–resources that could easily be used for something else.
Stability is the Missing Piece
I think this is why so many organizations are having a hard time getting past the proof of values phase.
Sure, you can take your AI tool, point it at a dataset, and get some decent results.
Maybe they’re not great, but given the small amount of effort required, those results can look very promising.
But I think a lot of organizations are running into trouble when they try to move something that’s 80% of the way there to something that’s 95%—something that can actually be put in front of customers or employees without fear of providing wrong information or wasting people’s time.
So for organizations looking to build out their AI practice, I’d encourage you to really think about this idea of stability.
Ask yourself: What are we building our AI tools on top of?
Artificial intelligence provides a lot of capability, but like anything else in this world, there is no free lunch.
Because fundamentally, without good data, there’s no good AI.
