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In the years since the term “digital business” emerged, so too has our understanding of digital maturity. As it turns out, you don’t get to the right-hand side of the maturity curve by merely collecting and using operational data to make business decisions. Companies now have access to entirely new categories of more meaningful data: unstructured data, Internet of Things (IoT) data, images, social data and more.

Still, high data volumes do not equal digital maturity. Businesses need to know which data matters, and they need to be able to access and use it to operate with precision.

To identify the data that’s most relevant to meeting their business goals and make these data types actionable, organizations are turning to advances in artificial intelligence (AI) and deep learning, and they’re using new data architectures to allow that data to come together for the first time.

To learn more about what it takes to attain digital maturity and achieve business success, Cognizant and ESI ThoughtLab conducted a worldwide survey; the idea was to identify businesses’ digital maturity in stages ranging from “beginner” to “leader.” (For more on the study, read our full report, “The End of the Beginning.”)

AI: Walking before you run

In our study, we discovered a distinct correlation between digital maturity and use of AI (see Figure 1). Only 4% of “beginners” (respondents lower on the maturity curve) considered themselves advanced in AI vs. 48% of “leaders,” or more digitally mature organizations. Less digitally mature businesses are more likely to focus on what we consider to be the prerequisites or drivers of AI, such as IoT and cloud adoption.

Figure 1

Involvement in these types of initiatives is a positive sign, as it signals a shift to collecting data that matters. However, full digital maturity means integrating data, analyzing content, understanding which data matters most, and using AI to predict and prescribe actions that will increase shareholder value.

These capabilities, however, are beyond human scale. Businesses need to make decisions continuously, often based on incomplete or inaccessible information, and always based on limited bandwidth. Further, business decision-making itself often exceeds the number of parameters that humans are capable of mulling over.

This is where AI comes in. Today, most companies apply AI and data to automate menial tasks. Where they’ll see outsize results, however, is in applying AI to specialized decision-making roles, such as video analytics and radiobiology, where these tools can perform at a scale that’s 10, 100, even 1,000 times greater than human capabilities.

The latest advancements in AI, like evolutionary AI, allow AI to scale with significantly fewer data scientists, and they enable business users to optimize algorithms across hundreds of parameters. AI mechanisms also exist that can take hundreds or even thousands of parameters and find the 10 that are most worth focusing on — the data that will move the needle on any particular business goal.

Why is revenue off by 5%? Why are customers not completing transactions? This requires an understanding of the data with the highest causal relationship to an outcome — “the data that matters most” — and how that information impacts our goals.

More specialized than originally thought

First, however, businesses may need to reset some expectations. AI is far more specialized than companies expect, more like a neurosurgeon than a general practitioner. That means practitioners need to know where to apply it and what it’s good at (and not good at) to maximize results.

For example, it’s fruitless to apply AI to data in its as-is state. What is causal within the data? What are the problems? What is influencing engagement and a decision to buy? The goal is to extrapolate causal data to understand what impacts human behaviors that, in turn, affect business outcomes.

The first part of that involves figuring out and then focusing on those behaviors (reflected in data) that drive true business value. As it turns out, evolutionary AI is very good at not only establishing causality but also incorporating it into a continuous loop that is followed by prediction (determining the outcome of decisions, even in contexts that have never been seen before) and prescription (identifying actions that will achieve the best outcomes).

Big challenge, big returns

While the impact of AI is great, it’s also among the most difficult of digital disciplines to master. Not only does it require a modern data foundation that brings together all the data that matters; it also requires new skills to extract meaning from that data. This explains the higher adoption rates among digital leaders as compared with beginners.

But respondents to our study that have taken on the complexities of AI are reaping benefits from their investments. For the 47% of respondents who said they’re making significant investments in AI, 59% believe the ROI is significant.

Increasing AI maturity

Businesses today need digital tools that can surpass human abilities to keep up with the complexity, velocity and scale of change. To move further along the digital maturity curve, companies should:

  • Start thinking beyond data warehouses and cloud migration and start to focus on modernizing data: integrating and virtualizing the data that matters and using new tools to assess data quality and causality.
  • Embrace AI (including evolutionary AI) to get predictive and prescriptive analytics driving the business. It’s critical to enable data and AI to scale beyond the limits of what’s been possible with reporting and data warehouse technologies.
  • Establish new roles and governance for data, including the formation of a chief data officer as part of the leadership team.
  • Assess all the organization’s data and create a data modernization roadmap.
  • Establish an AI center of excellence and an AI strategy, identifying needed skills, prioritizing use cases, initiating AI projects, and setting AI standards and governance.
  • Scale AI as part of digital initiatives and programs.

With AI, the best time to have planted a tree was 10 years ago. The second best time is now. Armed with new skills and technologies (and new top-down leadership styles), encourage your teams to start small with test cases that identify data weaknesses. Only then can your organization’s new AI skills pay dividends.

To learn more, read our “Investing in AI” research report, visit the AI section of our website or contact us.