Generative AI (GenAI) adoption is surging. Sixty five percent of respondents to the McKinsey Global Survey on the State of AI in Early 2024 indicate their businesses are using generative AI in at least one functional area. Yet, more than half of individual GenAI adopters use unapproved tools at work, according to a Salesforce survey. Clearly, businesses want and need to implement the technology to meet their business goals, but in the absence of a clear path forward, employees are finding ways to adopt it anyway, perhaps putting sensitive data at risk. Organizations need to move fast, put a strategy in place, and implement pilot projects with impact.
But what’s the best way to get started?
We get this question often at Kopius. Maybe you have a problem you need to solve in mind or a general use case, or maybe that’s not yet clear. You might understand the possibilities but haven’t narrowed down an opportunity or area of impact. Regardless of which camp you’re in, when we peel back the onion, we find that most companies need to step back and address fundamental issues with their data foundation before they can begin to tackle GenAI.
At Kopius, we have a detailed framework for walking you through the things you need to take into consideration to identify a GenAI pilot project and build a data foundation to support. But asking—and answering questions like the ones below—is at the root of it.
- What problem are you trying to solve?
In a survey of Chief Data Officers (CDOs) by Harvard Business Review, 80% of respondents believed GenAI would eventually transform their organization’s business environment, and 62% said their organizations intended to increase spending on it. But no company can afford to make investments that don’t deliver on outcomes. While there is value in just getting started, it’s both worthwhile and necessary to define an initial use case. Not only do you want your program to have impact, but the GenAI ecosystem is so broad that without some sort of use case, you will be unable to define what type of outputs need to be generated.
Some companies will have a clear use case, while others will have a more general sense of where they’re headed. Still others are working with an “AI us” request from senior leadership to explore the landscape. Wherever you are in this process, our framework is designed to help you identify a meaningful pilot project. - What are your data sources? What do you need to capture?
Next, you’ll need to take stock of your data sources, so you have a solid understanding of the full set of data you’re working with. What inputs do you have coming in and what inputs do you need to get to your end goal? Often, there is a project behind a project here. If you don’t have the data you need to solve the business challenge, then you’ll have to develop and implement a plan to get it. For instance, say you want to measure the impact of weather conditions on fleet performance, and you’re planning on using IoT data from your vehicles. You’ll also need to determine what weather data you need and put a solution in place to get it. - What is the state of your data? Is it relevant, quality, and properly housed and structured?
With GenAI, your ability to get quality outputs that deliver on business outcomes depends on the quality of your inputs. That means data must be current, accurate, and appropriately stored and structured for your use case. For instance, if you’re developing a GenAI enabled chatbot that employees can query to get information about policies, procedures, and benefits, you’ll need to make sure that information is current and accurate.
At this point, you’ll also need to consider where the data is being stored and what format it’s in. For instance, JSON documents sitting in non-relational database or tables sitting in a SQL database are not necessarily a model for GenAI success. You may have to put your raw data in a data lake, or if you already have a data lake, you may need to warehouse and structure your data so that it’s in the right format to efficiently deliver the output you want. - What governance and security measures do you need to take?
Data governance is about putting the policies and procedures in place for collecting, handling, structuring, maintaining, and auditing your data so that it is accurate and reliable. All these things impact data quality, and without quality data, any outputs your GenAI solution delivers are meaningless. Another important aspect of data governance is ensuring you are compliant with HIPPA or any other regulatory mandates that are relevant to your organization.
Data security, in this context, is a subset of data governance. It is about protecting your data from external threats and internal mishandling, including what user groups and/or individuals within your organization can access what. Do you have PPI in your system? Salary data? If so, who can modify it and who can read it? Your answers to these questions may inform what data platform is best for you and how your solution needs to be structured. - What is your endgame? What types of outputs are you looking for?
The problem you’re trying to solve is closely tied to the types of outputs you are looking for. It’s likely that exploration of the former will inform conversation of the latter. Are you building a chatbot that customers can interact with? Are you looking for predictive insights about maintaining a fleet or preventing accidents? Are you looking for dashboards and reporting? All this is relevant. This also gets into questions about your user profile—who will be using the solution, when and where will they be using it, what matters most to them, and what should the experience be like?
A Rapidly Evolving Data Platform Landscape Drives Complexity
Getting started with GenAI is further complicated by how complex the third-party GenAI, cloud, and data platform landscapes are and how quickly they are evolving. There are so many data warehouse and data lake solutions on the market—and GenAI foundational models—and they are advancing so rapidly that it would be difficult for any enterprise to sort through the options to determine what is best. Companies that already have data platforms must solve their business challenges using the tools they have, and it’s not always straightforward. Wherever you land on the data maturity spectrum, Kopius’ framework is designed to help you find an effective path forward, one that will deliver critical business outcomes.
Do You Have the Right Data Foundation in Place for GenAI?
In the previously mentioned survey by Harvard Business Review, only 37% of respondents agreed that their organizations have the right data foundation for GenAI—and only 11% agreed strongly. But narrowing in on a business problem and the outcomes you want and defining a use case can be useful in guiding what steps you’ll need to take to put a solid data foundation in place.
One last thought—there are so many GenAI solutions and data platforms on the market. Don’t worry too much about what’s under the hood. There are plenty of ways to get there. By focusing on the business problem and outcomes you want, the answers will become clear.
JumpStart Your GenAI Initiative by Putting a Solid Data Foundation in Place
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
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