Artificial intelligence is no longer just a buzzword—it’s a game-changer for organizations looking to unlock real business value. But making AI work in the real world requires more than just building models. It’s about crafting applications that integrate multiple models, enforce guardrails, and apply techniques to ensure quality, scalability, and impact.

At Avido, we view this journey through the lens of AI maturity, which unfolds in three phases: Control, Scale, and Differentiation.

Alongside this, we take inspiration from BCG’s 10-20-70 rule, a powerful framework for prioritizing the key factors that make AI deployments successful.

Let’s explore what this journey looks like—and how it drives sustained business value.

Stage 1: Control

The Control phase is the foundation of AI maturity. This is where companies start small, experimenting with AI applications that prioritize risk mitigation and predictable outcomes. At this stage, the focus isn’t on doing everything—it’s on building confidence in AI by delivering business value through limited, well-controlled applications.

Key priorities in this phase include:

• Implementing guardrails: Ensuring AI applications deliver reliable results while staying aligned with business goals and compliance requirements.
• Focusing on business impact: Instead of building models for the sake of experimentation, organizations prioritize applications that solve specific pain points or streamline processes.
• Rigorous testing: Variance testing (e.g., how users ask for the same thing in different ways) ensures the AI behaves consistently in real-world scenarios.

The goal of this phase is clear: build trust in AI as a tool for creating measurable business outcomes while keeping risks in check.

Stage 2: Scale

Once the foundational groundwork is laid, companies move to the Scale phase. This is where AI starts making a significant impact, driving cost efficiency and operational improvements across both internal and external applications.

Scaling AI successfully, however, requires more than deploying additional models. It’s about building applications that seamlessly integrate models, data, and workflows to create tangible business value.

Key components of the Scale phase include:

• Integrating multiple techniques: AI applications often combine predictive models, generative AI, and decision-making algorithms to tackle complex challenges.
• Streamlined infrastructure: Scaled AI requires robust data pipelines and automated monitoring systems to ensure reliability and accuracy across large-scale deployments.
• Empowering people and processes: According to BCG’s 10-20-70 rule, 70% of AI success depends on people and processes. Organizations must train teams, create trust in AI systems, and embed AI-driven decision-making into their operations.

The Scale phase isn’t just about efficiency—it’s about creating scalable systems that maximize the business value of AI.

Stage 3: Differentiation

The final phase, Differentiation, is where companies truly capitalize on AI as a competitive advantage. Instead of merely keeping pace with the market, they use AI to deliver transformative business value that sets them apart.

In this phase, AI is no longer just a tool for operational improvements—it’s a driver of innovation and customer experience.

Key priorities in Differentiation include:

• Redefining customer experiences: Hyper-personalization, real-time insights, and entirely new use cases create exceptional value for customers.
• Solving problems once deemed impossible: With advanced AI techniques and well-defined guardrails, companies can explore use cases that previously seemed out of reach.
• Sustained innovation: AI becomes an ongoing driver of creativity, agility, and growth in response to changing market dynamics.

In this phase, business value becomes exponential. Differentiated AI applications not only address existing needs but also create entirely new opportunities for growth.

As organizations progress through the three stages of AI maturity, the 10-20-70 rule, developed by BCG, serves as a practical guideline for allocating focus:

• 10% on models and algorithms: While essential, these make up only a small portion of the overall journey.
• 20% on technology and data: Robust infrastructure ensures AI applications can scale and function reliably.
• 70% on people and processes: This is where the true transformation happens—empowering teams, refining workflows, and fostering a culture that embraces AI.

However, our experience suggests that this balance is dynamic and evolves across the Control, Scale, and Differentiation stages of maturity. The effort required for algorithms, technology, and people follows an inverse U-shaped curve:

• In the Control phase, a greater focus is placed on algorithms and technology to establish foundational capabilities and infrastructure. At this stage, the need for people and process alignment is relatively lower as experimentation dominates.
• As organizations move to the Scale phase, people and processes take center stage. Empowering teams, embedding AI into decision-making workflows, and creating trust in the system are critical to unlocking broader operational efficiencies.
• Finally, in the Differentiation phase, the emphasis on people and processes tapers slightly. By now, the organization has matured its understanding of AI and governance, allowing for a more balanced effort across all three components.

This evolving distribution highlights that building impactful AI applications is not just about technology—it’s about timing the right priorities to the right stage. By tailoring efforts across models, technology, and people, organizations can ensure they extract the maximum business value from their AI investments at every step of the journey.

Why Business Value Is the True North

AI maturity isn’t about chasing trends—it’s about delivering business value at every stage of the journey. From the first experiments in Control to large-scale efficiency in Scale and finally to market-leading innovation in Differentiation, every phase is an opportunity to solve real problems, improve decision-making, and create sustainable growth.

At Avido, we specialize in helping organizations navigate this journey. Whether you’re looking to build your first AI application or take your existing capabilities to the next level, we’re here to help you unlock the real value of AI.

Ready to start your journey? Let’s talk.