As AI adoption becomes table stakes, organizations are searching for new opportunities for competitive advantage. Many are making significant investments of time and resources to reimagine their operations in an AI-centered future – and hoping that one or another of these investments will help catalyze long-term advantage or fundamentally alter their future position. 

Leading organizations recognize that the data at their fingertips – from internal data to data from partners and external sources – is a vital asset for AI strategy. They know that organizing and harnessing the power of that data isn’t just a box to check, but a lever for growth and innovation. But what differentiates a merely effective data strategy from a truly transformative one? 

Savvy enterprises may want to look at the “human factor”. Given the importance of data strategy in the AI revolution, data leadership can hold outsize power in shaping and influencing a company’s AI journey.

While technology skills are a foundational ingredient in AI implementation, the “soft” skills that define some of the best leaders are just as important for success, even in a technical field like data strategy.

Here are three qualities of transformative data leadership that are important in helping data leaders power the AI revolution:

1. Adopt a long-term vision

Given the pace of AI innovation, organizations should ground their adoption in long-term vision and a deeper understanding of how intelligence reshapes their business. The technology that seems like a difference-maker today may be table stakes within 12 or 18 months. Leaders with vision look beyond Horizon 1 (the business of today) to shape the data strategy that powers AI success in Horizon 2 (the business of tomorrow) and beyond.

In this era of rapid innovation, it’s no longer enough to see just around the corner or react to the most immediate developments. While leaders may not be able to plan every step of their business’s evolution in Horizon 2, Horizon 3, and even further into the future, leaders have a vision for what they want their company to be and set out to build it proactively. They align data strategy with long-term business goals, and look for signals that show how technology is evolving, where the market is likely to go, and how their business can shape that future on its own terms.

2. Prioritize collaboration and break down silos

Effective leaders take time to listen, learn, and translate the experiences of business units or pilot projects to enterprise-wide success. In data strategy, that means developing a data marketplace and treating data across the organization as a shared asset under every team’s purview. A data marketplace collects and organizes all data an organization has access to – including its own data, data from brokers or partners, and public sources – into a searchable, indexable, and discoverable whole, unlocking new opportunities for teams and leaders to better understand and leverage the organization’s full data footprint.

These leaders also automate collaboration itself, seamlessly connecting teams and sharing insights between them so everyone can benefit together. For example, consider a finance team within a large multinational organization that uses its data marketplace to develop a new process that identifies opportunities for cost savings 40% faster. A truly “intelligent” and collaborative organization might automatically, immediately share that process and related recommendations with other teams across geographies with similar use-cases.

The idea doesn’t live in a silo; it is rapidly rolled out through the organization. Cross-functional teaming and investments in AI fluency initiatives can also help encourage teams to deploy AI solutions together.

3. Champion experimentation and creativity

Organizations should experiment with AI and data throughout their organization with a “startup” mentality, even for established players. They should not expect innovative ideas to come solely out of their technology organization or from technology leadership. Instead, data leaders should empower even entry-level workers to experiment with AI technologies, seek insights in their work, and embrace an openness to learning from their entire staff.  

It is also important to learn from best practices in your industry, and to think outside the box if they aren’t driving the expected results. Use cases should not be guided by feasibility alone, but by the opportunity to redefine and reimagine how the business operates and meets its goals.

The future of business needs transformative data leaders and now is the time to become one

These are not the only qualities that define successful data leadership. But they capture a vision for how organizations should use, think about, and value data in the future of business, as organizations become smarter and the tools at their fingertips more powerful. 

Technical capabilities are often the easiest box for organizations to check on their journey to becoming “AI-powered.” Those that set themselves apart will likely be defined by their data leadership – and the vision, collaboration, and creativity that help them go beyond achieving AI adoption to redefine what’s possible.

Disclaimer: This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.

About the author:

Ashish Verma is a Principal  in Deloitte Consulting and the U.S. Chief Data and Analytics Officer and the Global Consulting AI & Data Offering Leader.

With 25 years of consulting expertise, Verma has significant experiences as a leading data and AI technology strategy professional. He has partnered with telecommunications, media, technology, financial services, automotive, transportation, hospitality, health care, life sciences, consumer and industrial product clients in the areas of domain specific vertical solutions, M&A, customer experience, business and finance transformation Solutions.

Verma is a frequent speaker at external vendor partners conferences and writes on the topic of disruptive technologies and their impacts on organizations.