By: Michael Sutton, Co-Founder & Vice President of Client Service

AIMachine learning and AI are incredibly alluring to organizations aimed at digital transformation. The technology drives data initiatives that promise impressive return on investment, including cutting costs and increasing revenue. But, organizations who invest for aspirational reasons, alone, will likely fail to see the results they hope to achieve. Instead they end up with data they can’t measure, scale, or sustain.

A 2019 study on big data initiatives surveying Fortune 1000 and industry leading firms found that 92 percent of respondents—who were mostly C-level data executives, such as chief data, analytics, or information officers—are increasing their investment in big data and AI, driven largely by the desire for digital transformation, increased agility, or competitive advantage.

Fifty-five percent are spending more than $50M on these big initiatives, and nearly a quarter of them are investing far more. Despite the hefty investment, 77 percent agree that business adoption of the initiatives is a challenge for their organizations.

The challenge isn’t the technology, but rather the people and the processes involved in these big tech data initiatives.

Here are three questions to ask before moving forward with an AI investment, helping to ensure that you know why you’re making the investment in a tech-driven data initiative, what problems it will solve, and how your teams (your people and processes) will leverage the data and measure success.

1. What is the problem you need to solve?

Many organizations think that technology will solve their problems before they clearly understand what is causing the problem they hope to fix. For instance, consider that revenue is the problem. To generate more revenue, an organization needs to identify what’s contributing to its insufficient revenue flow. Is it related to marketing, supply, competition?

Go deep by first exploring the different layers of costs, by department, which may be contributing to the problem. Consider where you may be over- or under- spending. Next, break down the thought process to understand where there are areas or problems you can change through behavior. You want a quick win. So, identify a low-hanging fruit—a project that will have immediate impact, quickly generating revenue or decreasing costs within a single department.

Once you have identified a problem and understand where you can change it, you can empower your team with the right technology solution.

2. Do you have an effective data model and the people to support it?

Once you have identified the problem, you need a data model that will support and scale to the solution. Take, for example, machine learning (ML). Any organization can collect data, run it through a simulator that leverages ML, and extract value. What’s disappointing is that the data the tool produces doesn’t hold its value. It has a limited shelf life—often it is obsolete before it’s shared—and it’s often siloed within departments—rather than stored in a central repository— disabling it from being truly useful to the organization its mission is to support.

Departments can’t leverage data to drive critical decision making if they don’t know where the data lives or how they can access it.

Your data structure must support all individuals who can leverage the value of newfound data. They need to be empowered by having the right information delivered in a timely manner. And, they need to know what to do with it.

3. How can you maximize your return on investment (ROI)?

Next, decide how you will show ROI on the significant purchase you plan to make. How will you measure your first AI-driven project to show success and encourage rollouts throughout the organization?

If you started with a clearly defined objective, you can set specific revenue goals around your data initiative adoption and usage, and create a communications plan for sharing your successes, and ultimately, creating change throughout the entire organization.

Use the success of your initial project to attract and secure buy-in across the organization. Once other departments see how your initiative is impacting another department, they, too, will want to reap the benefits. You can then scale your data model to encompass other departments, generating additional value to the organization.

Best practices for maximizing ROI include the following:

  • Ensuring you have the proper documentation and training in place so teams know how to access and use data
  • Defining workflow processes that will help teams understand when information is relevant and how it can be used
  • Tracking progress with KPIs that directly relate to the original objective, so you can adjust when necessary
  • Leveraging quick wins to sell the solution throughout the organization, including to the C-suite
  • Using leadership to empower changes
  • Developing and sharing internal case studies to magnify wins within the organization

Here is an example of a B2B retailer getting the most out of its data initiative: The retailer was struggling with evaluating and converting prospects to customers. By leveraging data insights, it was able to identify patterns in data related to each prospect and understand what historically converted a prospect to a successful customer. The patterns allowed it to rank and score prospects to more clearly define the likelihood of conversion, helping its marketing team optimize conversion strategies and its sales team better anticipate revenue.

Conclusion: Pulling the Trigger on Powerful Tech-Driven Data Initiatives

Undoubtedly, investments in emerging technologies, like ML and AI, can digitally transform organizations, helping to solve their biggest challenges. To reap all the advantages data has to offer, organizations must understand what they need from technology and have the people and processes in place to guide and leverage the insights it will reveal.