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Coming to Terms with Your AI Competitive Readiness

In our last piece, The Anti-Consulting Guide to AI-Centric Value Creation,” we established a framework for software and software-enabled services businesses to minimize revenue erosion risk, accelerate sustainable growth, and pave new paths to category leadership in an AI-first market environment.

In that article, we outlined eight steps your management team and board can use to take stock of where your business stands in its AI maturity and to make rapid progress establishing an AI-centric value creation strategy.


In this piece, we dig into the essential starting point of that process, coming to terms with where your solution offerings currently stand in their ability to compete in the age of AI. We’ll introduce Thinktiv’s AI Maturity Model, a foundational tool for evaluating the AI maturity of existing products and forward-looking product roadmaps, both within your own business and across your ecosystem of competitors. This critical first step sets the stage for both mitigation of revenue erosion risk and the development of AI-first product roadmap strategies that can deliver sustainable growth and category leadership.

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Before your business can make any progress in building an AI-centric value creation strategy, you must honestly weigh your progress to date on the path to an AI-first future. In this case, we’re focused not only on the AI maturity of your current products, services, and forward-looking innovation roadmap, but those of your competitors as well. 

To create a common language that everyone on your team and board can understand and leverage in this process, Thinktiv has developed a proprietary AI Maturity Model for existing products and roadmaps. This framework acts as a universal, repeatable tool for assessing the AI maturity of your products, services, and roadmap, while simultaneously weighing competitive threats through the lens of AI. 

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The basic conceptual framework of the AI Maturity Model is outlined in the visual. At the bottom of the pyramid, basic AI utilities offer only moderate product and service value and will soon become table stakes in competitive environments. Moving up the pyramid, AI-enabled solutions become increasingly powerful, differentiated, and valuable to end users and customers. 

It’s important to acknowledge that this framework represents a continuum of competitive readiness, and that every software-centric business will be at a unique stage of maturity at the outset. The key is to first recognize how much (or little) danger AI currently poses to your competitive position. Then you can move quickly to close the gap and accelerate toward differentiation. The AI Maturity Model serves as a compass in this process, guiding businesses as they formulate strategies for AI-first revenue growth and category leadership.

Five Critical Attributes of AI Product Maturity

Thinktiv has identified five critical attributes that collectively define AI product maturity, each of which become more relevant and powerful as one moves from the bottom of the AI Maturity Model pyramid toward the top. Understanding these attributes—and how they complement one another as solutions become more AI-mature—is key to connecting the dots between today’s reality and the value that can be delivered in your products and services over time. Once understood, these variables serve as a useful roadmap for organizations to assess their current progress and begin to architect more valuable horizon roadmap strategies. The five attributes of AI product maturity are as follows:


The level of independence the AI has in performing tasks or making decisions


The complexity and advanced nature of the AI’s cognitive and decision-making capabilities


The AI’s ability to understand and use contextual information from various data sources


The AI’s ability to tailor experiences or decisions based on individual user data or preferences


The scope of tasks or decisions the AI is capable of handling


How Does AI Maturity Manifest in Real Solutions?

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Now that we’ve established the key attributes that collectively determine the AI maturity of products, services, and forward-looking roadmaps, we can bring them to life in a conceptual framework of real-world solutions. 

In the diagram, we’ve added categories of AI solutions at each level of the maturity pyramid, which gives us a more practical mechanism for classifying existing solutions and roadmap capabilities. We then provide examples of each solution type, as well as descriptions of how the key maturity attributes are manifested at each level.  


Basic Utilities

Examples: Spam filters and basic chatbots, which perform predefined tasks like filtering unwanted emails or providing scripted responses to common queries

  • Autonomy: Very Low, as these tools operate under strictly defined rules without deviation
  • Sophistication: Very Low, utilizing straightforward algorithms for specific tasks
  • Contextualization: Very Low, limited to direct input without understanding broader context
  • Personalization: Very Low, offering uniform responses regardless of user specifics
  • Breadth: Very Low, designed for narrow, specific functionalities

Process Automation

Examples: Predictive maintenance systems that schedule servicing based on usage data and invoice scanning tools that digitize and categorize expense data

  • Autonomy: Low, automating tasks based on predefined workflows and rules
  • Sophistication: Low, capable of handling sequences of tasks with some degree of decision-making
  • Contextualization: Low, using specific data points to guide processes
  • Personalization: Low, beginning to adjust processes based on user data
  • Breadth: Moderate, covering a broader range of tasks within its domain

Preference Engines

Examples: The Netflix recommendation engine and Spotify playlists, which tailor content based on user interaction history and preferences

  • Autonomy: Low, with system actions guided by user data
  • Sophistication: Moderate, due to more complex algorithms for personalized recommendations
  • Contextualization: Moderate, integrating user behavior and preferences over time
  • Personalization: High, actively tailoring outputs to individual users
  • Breadth: Moderate, focusing on content or product recommendations within specific domains

Knowledge Assistant

Examples: ChatGPT, offering conversational assistance across various topics, and GitHub Copilot, providing coding suggestions based on previous code interactions

  • Autonomy: Moderate, performing tasks with some level of independence from direct user input
  • Sophistication: High, understanding and generating complex responses
  • Contextualization: High, utilizing a broad array of data sources for informed interactions
  • Personalization: High, adapting interactions based on user history and preferences
  • Breadth: High, capable of assisting across a diverse range of tasks and topics

Reasoning Agent

Examples: Advanced medical diagnostics tools that interpret patient data to suggest diagnoses and autonomous financial trading systems that make trades based on market data analysis

  • Autonomy: High, executing complex tasks and making decisions with minimal human intervention
  • Sophistication: Very High, employing advanced decision-making and problem-solving capabilities
  • Contextualization: High, integrating complex, multi-source information to inform actions
  • Personalization: High, considering user-specific goals and preferences in decision-making
  • Breadth: Very High, addressing complex, multifaceted problems across domains

Digitized Self

Examples: Personal AI assistants that manage schedules, communications, and preferences, acting as an extension of the user

  • Autonomy: Very High, operating independently across a wide range of activities and decisions
  • Sophistication: Very High, demonstrating expert-level capabilities and understanding
  • Contextualization: Very High, applying insights from extensive and varied data sources
  • Personalization: Very High, embodying the user’s preferences, ethics, and decision-making styles
  • Breadth: Very High, capable of representing and acting on behalf of the user in virtually any scenario

Apply the AI Maturity Model to Your Products, Roadmap, and Competitive Ecosystem

We now have a framework that is actionable and which can serve as a common language for your management team and board to begin making progress toward AI-centric value creation. 

After reviewing each category of solution in the AI Maturity Model, consider where your current products and services fit on the pyramid (if at all). List elements of your solution offerings that fit along the AI maturity continuum and map them to each category. Then do the same for key capabilities and new solutions currently planned within your roadmap. 

Next, take the 2-3 direct competitors you’re most familiar with, and do the same exercise. How do you feel about your competitive readiness? Are there any new AI-native players making noise in your competitive space? How would they map their maturity?

As you weigh your company’s current standing in an AI-first market environment, you’ve officially begun the journey toward an AI-centric value creation strategy. Remember that we’re early in this game, and that you come to the arena with key assets and differentiators that existing competitors and new insurgents do not. It’s a matter of exploiting those to your advantage.

A universal framework like Thinktiv’s AI Maturity Model sets the stage for deeper exploration and quantification of your commercial risk and, more importantly, becomes the launching point for leveraging your strategic assets and differentiators to architect AI-centric strategies that accelerate sustainable growth and category leadership positioning.

Finally, let’s revisit why this is urgent.

Remember the basic principle at work here as AI innovation accelerates: In order to carve out a category leadership position (or to even remain competitive over time), software and software-enabled services businesses must align their product and business strategies to an AI-first market reality. 

Every company should be orienting their product roadmaps to include AI capabilities. Companies that don’t will lose market share and experience revenue erosion as new AI-native emergents enter the market. That means subpar (at best) capitalization outcomes for teams and shareholders. Companies that do this well will compound their current advantages, extend their category leadership, reinforce sustainable growth trajectories, and ultimately capture superior capitalization outcomes at premium valuations.

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The evidence is already building that “AI vs. Non-AI” is now the critical value determinant for software-centered businesses. The fantastic Tomasz Tunguz of Theory Ventures illustrates this in recent research, one great chart from which is shown here. 


As you contemplate your position in the AI Maturity Model and weigh your strategic priorities for 2024, we’ll let Mr. Tunguz close this one out:

“This yawning difference should compound over time as the adoption of AI is still relatively early - we’re only 18 months into it. AI software companies are projected to grow 63% faster in 2024 than non-AI software companies because of customer demand.”

If you’re ready to mobilize and build a winning AI-Centric Value Creation strategy, consider a 1- or 2-day Thinktiv AI Strategy workshop. We’ll help you map your business, products, and roadmap to the AI Maturity Model, identify your most valuable data assets and differentiators, and give your management team the tools to make rapid leaps forward in your AI competitive readiness.

Click here to arrange a time to discuss how Thinktiv AI Strategy workshops can impact your business or portfolio.


"AI-Centric Value Creation series: Weighing your AI-driven commercial risk exposure"

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