How to Build a High-Impact AI Roadmap 

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In an era defined by volatility, complexity, and rapid technological change, AI offers business leaders both a powerful opportunity and a daunting challenge. Executives are under growing pressure to “do something” with AI — yet few have a clear roadmap for doing it right. 

That’s where Lux Research’s latest Inspire Report, “Creating an Applied AI Strategy and Roadmap for R&D and Innovation Leaders,” comes in. This report introduces the Lux AI Application Selection Framework — a pragmatic, decision-oriented guide to prioritizing and implementing AI across the enterprise. 

Why AI needs more than hype 

The launch of generative AI tools like ChatGPT has fueled breathless optimism about AI’s potential to revolutionize business. But seasoned leaders have been here before — from the promises of Industry 4.0 to the blockchain boom — hype alone does not deliver ROI. 

Lux Research argues that AI success hinges not on ambition, but on alignment — aligning applications with business priorities, corporate culture, digital infrastructure, and available resources. 

AI isn’t plug-and-play: It’s an organizational transformation. And that transformation needs to be measured, iterative, and grounded in real business value. 

Introducing the Lux AI Application Selection Framework 

Lux’s framework is built around two fundamental questions every organization must answer: 

  1. Which AI applications create the most value for my business? 
  1. Which should we prioritize based on risk, readiness, and speed to results? 

To answer these, the report introduces three core tools: 

  1. The AI Application Map helps clarify how AI functions across diverse applications. 
  1. The AI Application Value Model evaluates applications by either automation value or knowledge value.  
  1. The AI Application Prioritization Model reviews time to value and likelihood of success to help build the application roadmap.  

Case study: Developing your AI roadmap 

To illustrate the model, imagine the fictionalized company “BiCorp” — a midsize specialty chemicals company that deployed AI to improve internal sales knowledge management. 

By combining off-the-shelf large language models with a custom-built knowledge graph, BiCorp improved cross-selling capabilities and accelerated time to sale. 

The success of the initiative was grounded in: 

  • Strong executive alignment on business goals 
  • Use of nonsensitive, structured data (technical datasheets) 
  • Clear KPIs and phased rollout 
  • Early engagement with both IT and business unit stakeholders 

The case shows how applying the Lux framework reduces risk, increases buy-in, and accelerates measurable impact

4 strategic imperatives for AI leaders 

While AI evangelists may believe that transformation is just around the corner, successful business leaders will chart a pragmatic path through the complex human and corporate ecosystems. Business leaders should strive to: 

  1. Deliver results in under 12 months 

Avoid grand transformations. Start with use-cases that produce quick, measurable wins. Build credibility early. 

  1. Anticipate and align with cultural resistance 

People define their value through their roles. If AI threatens identity or autonomy, expect resistance. Choose tools that solve pain points and elevate human talent. 

  1. Treat AI as a business process change 

AI isn’t an app you install — it’s a process transformation. Bring in business unit leaders early and align projects with their KPIs and incentives. 

  1. Watch for the Risks of Success 

When AI works, it changes behavior. Performance reviews based on AI-derived insights may overlook crucial offline work. Be ready to manage unintended consequences. 

Steps for AI implementation success 

The corporate graveyard is full of buzzwords and failed digital initiatives. AI doesn’t have to be the next one — provided leaders chart a clear, defensible, and value-driven path

Lux’s framework is a blueprint for doing just that — cutting through noise, aligning with strategy, and unlocking AI’s full potential through rigor and realism. 

Unlock the full framework, application maps, prioritization models, and case studies to guide your AI roadmap with confidence. 

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