Only 37% of Fortune 1000 companies have been able to improve data quality

However, organizations that invest in these technologies for aspirational reasons alone often fail to achieve their desired outcomes. Instead, they end up with data they can neither measure nor scale effectively, hindering long-term success.

Challenges of AI Adoption

A 2024 study surveying Fortune 1000 companies and industry leaders reveals that 90% of respondents—mostly Chief Data Officers and AI/Analytics leaders—are increasing their investments in AI as a top organizational priority. This is largely fueled by the pursuit of digital transformation, agility, and competitive advantage.

More than half of these companies are allocating over $50 million to these initiatives, with nearly a quarter of them investing even more. Despite these large investments, 77% of respondents report that business adoption remains a significant challenge, with data quality being a persistent issue.

Only 37% have been able to improve data quality, indicating that the real hurdle is not the technology itself, but the people and processes involved.

Before diving into an AI investment, it’s essential to answer the following three key questions. Doing so will help ensure that the investment aligns with your organization’s needs, solves the right problems, and empowers your teams to leverage data effectively and measure success.

1. What Problem Are You Trying to Solve?

Many organizations fall into the trap of believing that technology alone will solve their problems without first identifying the root causes. For example, consider the goal of increasing revenue. The first step is not just investing in AI but analyzing the specific factors hindering revenue generation. Is the issue marketing, supply chain inefficiencies, or competition?

To understand the problem, start by examining the various layers of costs across departments. Is there overspending or underspending in any areas? Look for quick wins—small adjustments that can drive immediate impact, such as improving efficiencies in one department.

By identifying a clear problem and understanding where you can make targeted changes, you can then choose the right technology solution to address it.

2. Do You Have an Effective Data Model and the Right People to Support It?

Once the problem is identified, it’s essential to ensure your data model can scale and support the solution. For example, many organizations use machine learning tools to analyze data, but the insights generated often have limited value. This can be due to siloed data, which makes it difficult for departments to access and act on valuable information.

For your data to be truly useful, it must be stored in a centralized location and easily accessible to everyone who can use it to make informed decisions.

It’s also crucial that teams trust the data they’re using.

A lack of confidence in data quality can hinder the effectiveness of AI and machine learning initiatives. According to the study, 63% of organizations still struggle with data quality, underlining the importance of building a data model that ensures reliability and accessibility for all stakeholders.

3. How Will You Maximize Your Return on Investment (ROI)?

To justify the substantial investment in AI and data-driven initiatives, you need a clear plan for measuring ROI. How will you track the success of your first AI-driven project and ensure that it delivers tangible results that encourage further adoption across the organization?

By starting with a well-defined objective, you can set specific, measurable goals related to revenue and data initiative adoption. A communications plan will be essential to share successes and demonstrate the value to the entire organization, helping to build momentum for broader implementation.

Maximizing ROI

To maximize ROI, follow these best practices:

• Ensure documentation and training are in place so teams know how to access and use data.
• Define clear workflow processes to guide teams in determining when and how to use relevant data.
• Track progress with key performance indicators (KPIs) that align with your original objectives, and adjust your approach as needed.
• Leverage early wins to build support for the initiative throughout the organization, including among executives.
• Empower leadership to drive organizational change.
• Create and share internal case studies that highlight successful outcomes and encourage further adoption.

Example: B2B Retailer Maximizing Data Insights

A great example of an organization reaping the benefits of its data initiative is a B2B retailer struggling with converting prospects into customers.

By leveraging data insights, the retailer was able to identify patterns linked to successful conversions, enabling it to score and rank prospects based on their likelihood of conversion.

This insight empowered both marketing and sales teams to optimize their strategies, driving higher conversion rates and anticipating revenue more accurately.

Successfully Leveraging AI for Digital Transformation

Investing in cutting-edge technologies like ML and AI can catalyze digital transformation and solve some of your organization’s most pressing challenges. However, to fully harness the power of these technologies, it’s essential to first understand the problems you are trying to solve, ensure you have the right data model and team in place, and create a clear plan to measure and maximize ROI. By aligning technology with people and processes, organizations can unlock the full potential of AI-driven data initiatives, ultimately driving long-term success and growth.