No One’s Data is Ready for AI – Yet

In this blog, we explain that while you may think your data is ready for AI use, there are AI readiness risks you want to make sure you address first.

As organizations embrace the reality of a future powered by artificial intelligence (AI), there’s a common assumption that the data they have accumulated over the years is AI-ready. But that’s not the case. The reality is that no one has truly AI-ready data, at least not yet.

The outcome of this reality spans from suboptimal AI-generated information to an outright failure of AI to produce anything of value for your organization. But this does not have to remain your reality. To overcome it, you must work to understand the nuances and gaps in your data and then fill in what’s missing. Doing so is crucial to maximizing the value of your data and AI use, managing the risk of your AI models and tools, and informing your overall data acquisition strategy.

AI Readiness Risk No. 1: Your (Missing) Current Data

Traditionally, companies have collected and used data primarily to serve immediate operational needs and human-driven analyses. This approach, while practical, often leads to limited and gap-filled datasets as they lack the foresight of AI’s extensive analytical capabilities.

Data has traditionally reflected our past and present operations, not necessarily a comprehensive blueprint for the future, especially in an AI-driven world. In short, the data might be rich in specific operational aspects but is missing various other potential dimensions that AI could explore for deeper insights. Consider these three examples:

• An e-commerce store may prioritize analyzing user behavior on its site, like page views, clicks, hovers, and abandoned carts. Yet, it might overlook wider consumer trends, such as seasonal shopping habits, demand for sustainability and “green” products, changing competitor behaviors, or regulatory changes.
• A logistics company might not consider all the weather conditions their semi-trailers face, even though such factors can influence AI predictive models, like predicting when they need tire re-treading or other weather-related maintenance.
• In the restaurant industry, establishments may predict sales based on historical data like the time of year or day of the week. But a sudden rainstorm might increase delivery orders, or a sunny weekend might boost patio reservations. A people-mover event like a Taylor Swift concert may have regional impacts on restaurant demand. All of which AI could help anticipate and manage.

AI’s ability to provide valuable insights and predictive behavior is somewhat limited without additional data dimensionality. This can impact everything from over- or under-ordering to over- or under-staffing to loss of revenue or risk of employee safety. Even worse, your AI’s limited insights may suggest a strategic recommendation that does not manifest until it’s too late for your company.

AI Readiness Risk No. 2: Incomplete Data

Rushing into AI projects with incomplete data can be a recipe for disappointment. The power of AI lies in its ability to find patterns and insights humans might overlook. But if the necessary data is unavailable, even the most sophisticated AI cannot generate the insights organizations want most.

The risk isn’t only subpar results. It’s potentially drawing incorrect conclusions from the data you do have. These incorrect conclusions mean your AI models are at risk for bias. AI works by processing large amounts of data, and if that data contains holes, it can be difficult to identify patterns and trends. Consider the impacts of missing data on two example systems:

• Sales and Customer Relationship Management (CRM): If a CRM has incomplete account information or does not faithfully record things like returns, complaints or other customer service interactions, then an AI model may not find trends affecting customer satisfaction or may suggest customer segments that are suboptimal.
• Human Resources and Employee Retention: If an HRIS system and the organization do not always record reasons employees leave, work hours worked, sick leave, or vacation use, then AI may not spot leading indicators of at-risk individuals.

Evaluate Your Company Data for AI Readiness

If data is incomplete and dimensions are missing, then how can you get value from AI down the road while also maximizing the value you can get from your data today? Start by ensuring your data is ready before deploying your AI models.

1. Audit your current data: Understand the depth and breadth of your current datasets. What are you tracking? What are you missing? Understanding your current state of data allows you to know if it is complete and robust enough to use with an AI model, monitor and test for bias, and ultimately determine viable AI use cases.
2. Explore overlooked sources: Beyond your internal data, consider external data sources that might be relevant. Many data brokers across various industries offer data to augment and enrich your current data. This is usually the first approach to adding dimensionality to your data, and it can be used to fill in gaps for incomplete data.
3. Collaborate: Engage different departments in your organization. They might have the data you need or offer unique perspectives on valuable data sources you haven’t already considered.

Managing the Risk of Incomplete Data

No dataset is perfect. But recognizing and addressing the gaps is half the battle. The rest is acting on what you find. Here are some recommended actions for remedying incomplete data.

1. Data Augmentation: Use techniques to artificially enhance your data. For instance, synthetic data generation can help fill gaps in datasets.
2. Continuous Evaluation: Regularly assess and update your data sources. As your business evolves, so will your data needs.
3. Expert Consultation: Work with data experts to ensure you maximize the potential of your data.
4. Improve Data Governance: Look at your data governance strategy and identify opportunities to improve the robustness and reliability of the data you collect today. Add augmented and acquired data to your data governance strategy.

Get Greedy About Your Data

If you take away nothing else from this, consider this final thought: to become AI-ready, you must be data-greedy.

While the data you collect today might seem excessive or irrelevant, storing it comes at a mere fraction of a penny. Yet, this information could become valuable in an AI-powered future, turning those tiny fractional pennies into substantial dividends. With AI readiness, there is no such thing as too much data, but there is such a thing as too little.

Temper the enthusiasm for using AI with the reality of your current data. Embarking on the AI journey prematurely can lead to suboptimal outcomes, but with proper due diligence and planful thinking, you can find ways to get AI value from your data today. By looking at what is missing from your data today and improving upon it, you’re enabling a true AI-powered future for your organization.