From Digital Transformation to AI Lift-Off: Doing More With Less in the Insights Industry

AI only pays off when the basics are in place. By cleaning up processes, standardizing data, and connecting knowledge, teams can finally let AI amplify insights - turning “insight debt” into compounding value and making smarter decisions faster.

The 'more with less' reality.

Tighter budgets and faster timelines are now the norm, while expectations continue to surge. In research and insights, innovators must ship more products with fewer resources, and fight for attention across channels that are noisier than ever.  

For those operating in this landscape, “more with less” means fewer manual handoffs, simpler tech stacks, clearer standards and output that goes further and allows for faster decision making. But, right now, many teams are in limbo: headcount is frozen, and the promised productivity boost from AI hasn’t fully kicked in yet. The result? Processes – and people - are feeling the stretch. 

Digital transformation is more than a buzzword – it's the foundation for AI readiness.  

Before AI can deliver real value, the groundwork must be solid. That starts with cleaning up the basics: removing redundancy, increasing reliability and standardizing how we work. What’s needed is a practical, staged approach that helps teams evolve from scattered processes to structured systems that AI can truly amplify. Each step builds toward a future where insights are faster, decisions are smarter, and the interface is as natural as a conversation.  

Crawl: Eliminate waste, increase reliability. 

This first step is foundational; clean up the clutter. Identify repetitive tasks – like building screeners, presentations and questionnaires - standardize them and aim to automate. Standardize audience definitions so “day zero” setup is ready to plug and play. Centralize reporting formats and secure data storage with consistent permissions and audit trails. The immediate pay-off is obvious: reduced cycle time and less repetition. The deeper value is structured, trustworthy inputs that are essential for AI to deliver accurate, actionable insights instead of amplifying the noise. 

Walk: Standardize outputs that are consistent and scalable. 

Now that inputs have been tidied up, outputs must be standardized. CPG teams often operate across categories, regions and channels – so reusable reporting shells, shared data schemas and consistent tagging vocabularies are critical. Lock in KPIs and thresholds to avoid having to reinvent the wheel on every project. Add versioning and lineage so anyone can trace how a number was produced. The result? Outputs that are machine-readable, human-friendly and ready to scale. No wasted data, no duplicated effort. 

Run: Turn research projects into learning systems. 

Treat research as a system, not a series of disconnected decisions. Build a system in which standardized outputs feed into a shared, searchable knowledge base. Use meta-analysis to surface patterns across projects. Establish benchmarks by category, target and market to make results meaningful in context. Govern how metrics evolve so insight remains comparable over time. This is where decision speed accelerates, and institutional knowledge becomes an evergreen asset – not data tables buried in decks. 

Fly: Use AI to multiply impact. 

With clean inputs, consistent outputs and a connected knowledge base, AI agents can finally be deployed. They can ingest new results, run relevant analyses, draft narratives and flag emerging patterns. Use retrieval-augmented generation, grounded in your own research, to scenario-test recommendations – always keeping humans in the loop. The payoff? More strategic time with the same headcount, sharper decisions and faster iteration. The focus shifts from digging around in data, to asking better questions. And as tech leaders note, the best interface is no longer a dashboard, it’s a conversation. 

From “insight debt” to “insight compounding”. 

Most insights teams aren’t suffering from a lack of data; they’re suffering from decision drag. Without shared standards or a single source of truth, each new project adds to the pile without adding momentum. This is insight debt: repeated work, missed connections, slow decisions. 

The shift to insight compounding starts with a system: standardized inputs → consistent outputs → a shared knowledge database that gets smarter with every study. AI turns that system into leverage – synthesizing findings faster, comparing results across markets and categories, and generating narratives that drive action, not just charts. No data waste, no duplicated effort and decisions that get sharper over time. 

The Outcomes 

• Sharper bets, sooner: Automation and standardization surface insights earlier, reducing wasted effort and dead ends. 
• Creative confidence: Faster learning loops empower teams to test bold ideas and adapt quickly. 
• Less friction, more flow: Streamlined hand-offs across functions cut down re-work and accelerate decision making. 
• Compounding insight value: Every study strengthens a shared knowledge base, making each decision smarter. 
• Scalable intelligence: AI agents amplify impact, enabling better questions and faster, data-backed answers. 

Practicing what we preach at Product Hub.  

We’ve taken a phased approach to transformation - starting with centralizing and structuring qualitative data using AI Explorer for Qual (to theme, summarize, and tag at scale). We then applied the same discipline to quantitative data with AI Explorer for Quant, enabling automated significance testing, standardized KPIs, and deeper diagnostics. Finally, we streamlined reporting with presentation-ready outputs, combining standardized formats and AI-powered commentary. The result is a growing, searchable corpus -including proprietary benchmarks like MMR’s Product Potential Rating - and an AI layer that helps teams move from “what happened?” to “what should we do next?” 

The Takeaway 

Don’t chase AI as a destination. Treat it as a tool for a targeted transformation you can measure. Crawl by removing toil. Walk by standardizing outputs. Run by connecting tests into programs so learning compounds. Then you’re ready to fly – this is where AI finally pays off as leverage for your team’s judgment and creativity. 

Start NOW! The race has already begun, and while many are still experimenting, the winners are crystallizing fast. 

Author: Alex Dobromir is Product Manager at Product Hub by MMR. 

Alex is trained in Social Psychology, Data Analysis, and Behavioural Economics at an MSc level and holds professional certificates in Product Management & Agile Practices. With previous experience in both FMCG and tech startups, Alex is making sure that Product Hub delivers the best experience to its users and stays at the forefront of ResTech.