“Are you okay?”, was the unexpected opening question from Gareth Herschel and Carlie Idoine, Vice President Analysts at Gartner, as they took the stage at this morning’s opening keynote at the Gartner Data & Analytics Summit.
And it wasn’t just rhetorical. Data and analytics leaders today are overwhelmed.
The relentless pace of AI advancements, soaring expectations, and mounting pressures have made many professionals feel trapped in a whirlwind of transformation. AI is an urgent, high-stakes reality, and organizations are demanding results.
In their keynote, Herschel and Idoine laid out a clear roadmap for navigating this time of AI-driven change. Success, they argued, isn’t about rushing to keep up with the latest AI trends. It’s about embarking on three strategic AI journeys:
- Business Outcomes: How trust, governance, and storytelling unlock AI’s full potential.
- Capabilities: How to build an adaptive, AI-ready data ecosystem.
- Behavioral Change: How to reshape culture, redefine roles, and drive adoption.
With AI’s role expanding across industries, these journeys offer a guide for organizations to move from fatigue and uncertainty to impactful execution.
The Journey to Business Outcomes: Measuring AI’s Impact
While cost reduction remains a core metric, Gartner suggests considering three types of value:
- Return on Employee (ROE): How AI enhances productivity and efficiency.
- Return on Investment (ROI): How AI improves decision-making and processes.
- Return on Future (ROF): How AI opens new markets or revenue streams.
Goldman Sachs, for example, reported that AI can now draft 95% of an IPO prospectus in minutes, replacing a process that previously took a six-person team two weeks. Similarly, Dutch software company, AFAS Software, recognized AI-driven productivity gains and responded by shifting to a four-day workweek, without reducing salaries or benefits.
The Journey to Capabilities: Building an AI-Ready Data Ecosystem
An AI-driven future requires a scalable, adaptable data ecosystem.
Many organizations are still in the early stages of AI readiness, and Gartner’s research suggests that investing in data foundations is the best first step.
Key Elements of a Strong Data Ecosystem
1. Modular & Open Architecture: 50 new AI vendors entering the market every day. To stay agile, organizations must evaluate their data architecture regularly and balance the trade-offs between single-vendor solutions and best-of-breed approaches.
2. Data Readiness Over Data Quality: Instead of aiming for perfect data quality, companies should focus on data readiness, ensuring data is fit for a specific AI use case.
“I’d rather have a worn out map at the right place than a pristine copy of the wrong map.”
Gareth Herschel
3. From Use Cases to Reuse Cases: Organizations should develop data products, reusable datasets with documented trust levels, contracts, and marketplaces to ensure efficiency.
4. Active Metadata for Adaptability: Traditional (passive) metadata simply describes data, but active metadata enables real-time monitoring, optimization, and automation. China Merchants Bank, for example, classifies data based on trust levels and continuously updates its system using active metadata.
The Rise of AI Agents
AI’s role in analytics is evolving from assisting human decision-makers to becoming autonomous decision-making agents. AI agents will not only analyze data but also execute actions, making AI-powered decisions that are perceptive, adaptive, and decisive.
The Journey to Behavioral Change: Managing the Human Factor
Even though technology evolves quickly, people adapt more slowly, and culture remains one of the biggest barriers to AI success.
Gartner’s research found that a lack of a data-driven culture is the #1 obstacle to data and AI initiatives.
Key Strategies for Behavioral Change
- Focus on Habits, Not Just Principles: Data leaders must move beyond principles like “be data-driven” and focus on small, repeatable habits that reinforce behavior.
- For example, if an organization values data-driven decision-making, it should hold regular meetings to review data product performance.
- Create New Roles for Change Management: AI requires new skills beyond traditional data engineering or analytics. Yet, only 5% of organizations have dedicated roles for change management. To successfully integrate AI, companies need roles focused on AI ethics, bias detection, problem-solving, and behavioral change.
- Embrace Cross-Functional Collaboration: AI is not just an IT initiative; it requires cooperation between data teams, security, risk management, software engineering, and business leaders. Organizations that embrace cross-functional teams will be better positioned for success.
The Future of Leadership in AI
The role of Chief Data & Analytics Officers (CDAOs) is also evolving. 54% of current CDAOs are the first in their organization, and as AI becomes a competitive battleground, other C-suite executives are vying for control.
To stay relevant, CDAOs must assert their leadership in AI strategy.
“If they don’t give you a seat at the table, bring a folding chair.”
Shirley Chisholm
The Wrap: From Exhaustion to Execution
Gartner’s keynote acknowledged the anxiety and fatigue that many data leaders feel as they navigate AI transformation. But despite the challenges, this is the most exciting time to be in data and analytics.
To succeed, organizations must embrace three AI journeys:
- Business Outcomes: Build trust, measure value, and use storytelling to communicate impact.
- Capabilities: Develop a diverse, adaptive data ecosystem that supports AI at scale.
- Behavioral Change: Drive cultural transformation with new roles, skills, and collaboration.
AI is a continuous transformation. The future isn’t something to anticipate. It’s something to execute, starting now.
