Agentic AI for Analytics & Insights: Make or Buy?

Enterprise analytics leaders face a pivotal decision: whether to build agentic analytics capabilities in-house or buy solutions that promise instant AI-powered insights.

This choice can determine how quickly and effectively an organization benefits from enterprise AI, predictive analytics, and decision intelligence.

Why In-House Agentic Analytics Builds Often Stall

The appeal of building internally is clear. With skilled data teams and complete control, organizations believe they can tailor solutions to unique needs and develop their own conversational analytics, predictive models, and semantic models.

However, unless these projects receive full-time commitment and resources, most struggle to move beyond early pilots.

Without structure and prioritization, in-house agentic AI and self-service analytics initiatives risk becoming endless experiments, rarely reaching the level of an AI data scientist or delivering true AI-powered insights at scale. This approach tends to work only at analytics-native or very large firms with established data science teams.

The Limits of Vendor Tools

Buying from vendors promises speed and simplicity. Many generative BI tools now offer text-to-SQL, chart generation, and basic conversational AI as part of the existing data stack. These capabilities can accelerate time to value, making AI-powered analytics and LLM analytics available to more users.

But most vendor solutions provide only junior-level support, focusing on descriptive reporting rather than agentic analytics or RGM analytics.

Strategic guidance, domain expertise, and the nuanced capabilities of an AI data analyst still rely on internal experts. Over time, organizations often find themselves locked into limited tools without seeing measurable improvements in KPIs or adoption.

What Works: A Hybrid, Outcome-Driven Approach

Success with enterprise AI is less about the technology itself and more about how people, processes, and supporting tools are combined. Leading organizations take a hybrid approach:

• Define clear use cases and success criteria. 

  They focus on high-impact areas like predictive analytics, revenue growth management, and self-service analytics, setting specific targets for adoption and measurable results.

  
  

• Leverage internal experts and specialist vendors. 

  By combining domain expertise with AI-powered platforms, companies bridge the gap between generic analytics and tailored, actionable insights.

  
  

• Set up pilots with clear timelines and expectations. 

  They treat the AI as a new team member, using a structured rating scale to measure progress from intern (limited value) to expert (trusted advisor), as shown below.

  
  

Tip: If an AI-powered analytics solution does not reach “senior” level within a typical 6–8 week pilot, it is time to reassess the approach. The goal is not just quick wins, but sustainable decision intelligence that supports revenue growth management, self-service analytics, and advanced agentic analytics.

Conclusion: Decision Intelligence Requires Both People and AI

Adopting agentic AI for data analysis is not about choosing between human or machine. It is about integrating AI-powered insights with business knowledge, process rigor, and continuous improvement.

The right blend of predictive analytics, conversational AI, and tailored semantic models empowers teams to leapfrog to instant, scalable, and transparent decision intelligence—delivering measurable impact for the enterprise.