These are formidable challenges coming. But there are proven steps leaders can take to close the digital marketing gap and manage strategies, organization, and data to make progress toward the growth mission and create a data-driven decision-making organization. 

Every company is somewhere along an open-ended journey to achieve data and analytics superiority. While there is no final destination — there will always be more to do — there is a roadmap for efficiently and strategically progressing on the journey. By using the right tools, companies can create a unique profile of their data and analytics capabilities; and they can then address areas of weaknesses and enhance their strengths, driving an ongoing data and analytics transformation that can deliver real sustained competitive advantage.

One misconception that exists in the minds of many managers is that AI is a decision maker that provides end-all answers and the functionality will cut costs or reduce the need for costly labor. The reality, however, is that AI remains more of a decision-support mechanism.

However, organizations often struggle with knowing where investments in AI will really pay off. A first AI project can be daunting, but knowing which factors to focus on will bring the project down to earth — and clarify whether it’s worth the investment at all. Companies need to:

- Ask whether they really need AI,

- Pick a task to start with, not a project

- Identify what data and complimentary systems it will require,

- Adjust expectations around accuracy accordingly,

Not rush to deploy it enterprise-wide,

- Ask whether they have the necessary skills to maintain an AI,

- Decide whether the returns will outweigh the costs.

Sometimes there is a lack of vision in managing large technology investments. Too many executives today feel they are behind on digital investments, including cloud computing, AI, and other technologies that competitors and tech vendors flaunt, all while using a significant portion of their discretionary investment to keep existing technology up to date. Business cases typically focus almost entirely on efficiency improvements — e.g., headcount savings from performing tasks faster or with less human intervention, or reductions of the technology cost itself. Be more ambitious. How will the investment change success in customer acquisition or retention? How will it improve your insights and help you better deliver your value proposition? What will it do to your carbon footprint? If your business case doesn’t address outcomes, the project itself is likely not transformational enough.

As you broaden the articulation of benefits, you will also need to hold your teams accountable for delivering that value. No longer should success be measured by whether the system “goes live,” but by whether it drives a change in your outcomes with customers. Defining clear fact-based measures isn’t easy, but without them, you’re basically just crossing your fingers and hoping that transformation will come on the back end of massive technology bets.

Research has shown that multi-sided platforms have the highest valuations of the dominant alternative business models — more than four times the annual revenue multiples attached to some legacy business models. 

How Network Orchestration Business Models are enabled by AI technologies. Platform companies like Facebook, Amazon, Google, and Tencent have created value at stunning rates. They grow rapidly and own few assets — and they’ve all made strong use of AI. What can legacy companies learn from these platforms? And is it possible for legacy companies to use this business model, too?

The growth of artificial intelligence has enabled a variety of new strategies and business models, from programmatic ad targeting to the sharing economy to the metaverse. The companies that have been most successful in employing these models — digital natives, almost to a one — have been “multi-sided platforms,” in which a company at the hub of an ecosystem.

 This is largely because they grow rapidly and have to own relatively few assets themselves. Platform business models typically generate large volumes of data from all participants in the ecosystem, and AI is required to make sense of it all. 

How do leaders make sure that digitalization makes a purposeful and sustainable impact on the business — and doesn’t just follow the next tech hype?

If digital transformation is supposed to be meaningful and lasting, companies must think about changes in products and processes more than changes in technology.

One approach to counter this imbalance is to think of digitalization as business model innovation rather than technology-related change.

The target is considering Customer data management a competitive advantage. It’s one that any company in any industry needs to take on to stay relevant. We can think of no other capability that is so universally needed. 

To stay ahead of competitors, companies need to implement a system of privileged insights: unique and relevant information about customers that competitors don’t have access to.

There are different ways companies can use to gain their own privileged insights — including creating a more robust and engaging customer service experience, integrating customers into product and service development, and observing and interacting with customers while they use products.

There are some powerful best practices:

- The first is to build trust. Customers that see their lives or businesses intrinsically linked and improved because of what a company offers are much more likely to engage and more willing to exchange unique information and insight into their core needs and challenges.

- The second: Privileged insights should be embedded into existing customer touch points (e.g., customer service, warranty support, product delivery, etc.).

- The third: Every business unit should be empowered to make decisions based on these unique insights.

New tools are enabling organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks.

Organizations considering implementing these tools should take five steps:

- Provide ongoing education,

- Provide visibility into similar use cases throughout the organization,

- Create an expert mentor program,

- Have all projects verified by AI experts,

- Provide resources for inspiration outside your organization.

AI is in its infancy. Organizations are continuously trying to determine how and whether to use AI, particularly against a backdrop of doubting its trustworthiness. Whether you trust AI novices with your AI strategy or not, following these steps will ensure a disciplined approach to AI, will maximize the benefits that AI can bring, and will minimize potential risks. Put simply, following these five steps should be a part of basic AI hygiene. Whether to democratize or not democratize AI is up to you.

Companies have every opportunity to use data, analytics, and AI to transform their businesses. Now is the moment to rethink how these investments are being made. It is time for data leaders to deliver transformative business outcomes. This is the moment to move forward and learn from the lessons of the recent past. 

Companies need to rethink how they’re investing in data, analytics, and AI — and whether these investments are creating real business value. Based on a recent survey of senior data and analytics leaders of Fortune 1000 companies, we offer four recommendations:

- Focus on culture change and its business impact;

Start small;

- Build strong business partners and sponsors at every stage;

- Pay attention to data ethics.

There is a clear need for CDOs to focus on adding visible value to their organizations.

The CDO role is poorly understood, and incumbents of the job have often met with diffuse expectations and short tenures. There is a clear need for CDOs to focus on adding visible value to their organizations. We suggest 8 strategies for CDOs to create — and show — value for their companies:

- Assume responsibility for analytics and AI;

- Focus on data products;

- Measure and document results;

- Build relationships with peers and business leaders who get it;

- Focus on data governance;

- Work on creating a data-driven culture even though it’s difficult to show value quickly;

- Build analytics and data infrastructure;

- Focus on a few key projects of value to stakeholders.

In one recent survey of large companies, 83% reported having a CDO. This isn’t surprising: Data and approaches to understanding it (analytics and AI) are incredibly important in contemporary organizations. 

But traditional data management approaches are unlikely to provide visible value in themselves. Many nontechnical executives don’t really understand the CDO’s work and struggle to recognize when it’s being done well. CDOs are often asked to focus on preventing data problems (defense-oriented initiatives) and such data management projects as improving data architectures, data governance, and data quality. But data will never be perfect, meaning executives will always be somewhat frustrated with their organization’s data situation. While improvements in data management may be difficult to recognize or measure, major problems such as hacks, breaches, lost or inaccessible data, or poor quality are much easier to recognize than improvements.

So how can CDOs demonstrate that they’re creating value? The primary ways that data adds value to companies is through enabling them to understand and predict business performance and customer behavior, and embedding it into products and services — all offense-oriented initiatives. CDOs, then, must be able to help companies achieve value through better data usage and consumption.

How CDOs Can Create Value:

- Assume responsibility for analytics and AI.

- Measure and document results. 

- Build relationships with peers and business leaders who get it. 

- Highly sophisticated companies can focus on data governance.

- Create a data-driven culture.

There is little doubt that organizations need chief data officers and that the job is here to stay as long as its incumbents add value. Some are clearly doing so. The job may have short average tenures, but 30% of the CDOs in recent surveys had already occupied their jobs for more than six years. If CDOs adopt these and related approaches to producing tangible value with data, analytics, and AI, they will be instrumental in transforming their organizations into more digital and data-driven competitors. As Bill Groves, a veteran CDO who held the role at Walmart, Honeywell, and Dun & Bradstreet put it, “This [the CDO function] is not a service organization; it’s a transformation organization.”