Turning Data Mountains Into Insights
Erin Tavgac is the cofounder and CEO of Redbird, an AI-powered analytics platform for enterprises.
According to a recent McKinsey report, half of employees are seeking training in AI, highlighting a growing demand for AI literacy across industries. Research and insights professionals—skilled in crafting questions and working with a wide range of data sources—have an advantage in paving the way when it comes to enabling these employees to use AI in the workplace.
This process typically requires multiple departments to pool their data and several skilled teams to collaborate. Now, AI can help research and insights professionals drive cross-functional collaboration and data integration efforts for their teams and the broader organization.
Drawing on my experience working with research professionals across industries, let’s explore how insights teams can lead AI-driven organizational transformation.
Going Big With Data: Putting Researchers In The Limelight
AI agents can now ingest and synthesize data from diverse sources—ranging from insights surveys and executive interviews to social media sentiment, third-party reports and internal planning documents.
But ensuring the right pieces come together still requires both a perspective on the business problems at hand as well as a deep understanding of data archetypes, research methodologies and statistics. Research and insights professionals, who are wired to dig deep into the data and connect the dots, can guide this effort.
By working collaboratively with various organizational functions, researchers can take a strategic seat serving their enterprise and crafting holistic, data-driven stories. Their role can continue as navigators, interpreters and strategists as information cascades to various stakeholders who apply the learnings to their respective lines of work.
This means research teams are well-positioned to lead AI transformation as the hub of a hub-and-spoke model. In this model, a key player—the research professional—reaches out to other departments to bring them into the AI fold. When other departments also pick up the game and start “playing ball,” it becomes a collaborative effort. The hub-and-spoke model is notable for its ability to foster collaboration and to align vision and strategy.
Bridging Insights And Analytics
For researchers to effectively use AI, the solutions should be nimble enough to bring in analytical functions and pressure test emerging trends with statistical rigor, identifying key business drivers and revealing their connections to corporate goals.
These connections show leaders and other stakeholders the strategic importance of research in their decision-making process. Some ideal use cases and KPIs where research professionals can apply AI include:
1. Optimizing marketing efforts to generate more revenue.
2. Deeply understanding key market trends to inform product development and maintain or grow market share.
3. Increasing productivity while reducing employee time spent conducting menial tasks and improving operating margins.
4. Increasing employee satisfaction and engagement and reducing employee churn while building a culture of innovation.
Research professionals often serve stakeholders who do not have the same technical knowledge but need quick answers to critical questions. Not everyone is a data scientist or a trained researcher who knows how to craft the right questions and do the analysis needed to answer those questions. Everyone in the organization should be able to extract insights from data using everyday language.
AI chat, for example, can help solve this by understanding questions in natural language and generating data-driven answers to those questions. With an interface similar to a typical search engine, stakeholders across the organization can access insights on top of the ingested datasets, simply by having a conversation with AI. This feature can be extremely helpful in extending insights use cases and getting stakeholders to embrace research as part of their business.
How To Operationalize AI-Powered Research
Implementing an AI-powered solution requires researchers to align with organizational dynamics and key players. That’s easier said than done—but here are three steps to find sponsors, broaden your network of collaborators, earn leadership support and drive actual transformation:
1. Scope realistically and start small. Define clear boundaries and set achievable expectations for AI capabilities. Begin with a small test initiative using a few data sources, where the research team develops something useful for a specific business function.
2. Iterate quickly. Collect feedback from research and related teams. Demonstrate AI’s power and adaptability by iterating rapidly.
3. Enable self-service AI. Once past the proof of concept stage, encourage initial adopters from other teams to pull insights themselves and apply them to their work. AI-based, self-service analytics puts the research and insights professional in a strategic advisor seat.
The approach also democratizes analytics for nontechnical stakeholders, cross-pollinates different departments with insights and extends the impact of research. AI for insights and analytics is revolutionizing the field. Researchers now have the power to disseminate their knowledge effectively within their organizations and beyond.
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