The TACO AI Maturity Model: A Strategic Framework for Assessing Operational AI Maturity

You probably know that artificial intelligence (AI) can improve your SaaS business operations.

‍

‍However, with the number of AI tools on the market ballooning, it’s extremely difficult to separate hype from substance. Achieving operational excellence means choosing the right tools for the use case. 

‍

‍How do you evaluate and embrace AI tools for a given use case? As experienced SaaS operators, we recommend using the TACO AI maturity model promoted by KPMG and other operations experts.

‍

What is the TACO Model?

‍

The TACO model is an AI maturity framework that evaluates what specific AI tools can actually do for your operations. When you're choosing between AI technologies, you need to know whether a tool can handle your specific requirements.

‍

This AI maturity model categorizes AI tools into four operational capability levels, from basic task execution to autonomous workflow management. By identifying which level your AI use case requires, you can select tools that can match and actually improve your current capabilities.

‍

TACO stands for four progressive levels of AI operational capability:

‍

• Tasker - Performs individual tasks with human direction
• Automator - Executes multi-step processes automatically
• Collaborator - Anticipates needs and acts proactively
• Orchestrator - Coordinates complex workflows autonomously

‍

‍

As you move to the right along the AI maturity curve, the potential impact of the application increases. Let’s break down each stage of the TACO model and see what it looks like.

‍

1. Tasker: Foundational AI Utility

‍

At this initial stage, AI operates as a specialized tool designed for distinct tasks. These systems require explicit instructions from humans and usually lack contextual awareness.  

‍

Example Use Case: Document Summaries

‍

Using a GPT to summarize documents and emails is an example of AI performing a specific, well-defined task on command: extracting key points from lengthy texts.

‍

This process does not involve proactive engagement or autonomous decision-making. This task-oriented approach helps streamline information processing, freeing up human resources for more strategic and complex tasks.

‍

2. Automator: Streamlining Repetitive Workflows

‍

Progressing to this phase, AI begins automating multi-step processes. These systems operate on predefined rules but can’t adapt to novel scenarios. 

‍

Quick tip: Before implementing tools, prepare your SaaS data for AI analysis.

‍

Example Use Case: Routine Report Generation

‍

Generating reports on a regular basis is an example of AI executing tasks on a predefined schedule without prompting from human intervention. AI tools in this phase automatically update PowerPoint presentations, dashboards, and visualizations with fresh data on a daily, weekly, or monthly basis.

‍

This eliminates a time-consuming manual process for human operators.

‍

3. Collaborator: Proactive Partnership

‍

In this stage, AI can analyze and understand context. This means it can anticipate needs and execute proactively without human prompting. 

‍

Example Use Case: Predictive Chatbots

‍

Predictive chatbots can analyze user behavior and proactively offer suggestions and solutions. These applications make a bigger impact by reducing process times, improving business outcomes, and facilitating operational excellence.

‍

4. Orchestrator: Autonomous Ecosystem Management

‍

At peak maturity, the AI tool can autonomously coordinate tasks across platforms. This usually involves embedding a layer of AI in all or a majority of decision processes.

‍

Example Use Case: Managing AI/Human Teams

‍

Mature AI tools can autonomously coordinate complex workflows across multiple AI agents and human team members. This means it assigns tasks, facilitates communication, and optimizes performance in real-time.

‍

This advanced level of AI application encourages seamless collaboration between humans and AI systems, leveraging the strengths of both to achieve desired business outcomes.

‍

Applying the TACO Framework

‍

Now that you understand the four distinct phases of AI maturity, you can evaluate any AI tool for alignment with its intended use. Follow the steps in this process to think through an AI tool's capabilities and how they align with each use case.

‍

1. Will the AI perform individual tasks with human prompting?
   • Yes: Tasker
   • No: go to step 2
2. Will the AI execute repetitive multi-step processes with human prompting?
   • Yes: Automator
   • No: go to step 3
3. Will the AI anticipate outcomes and execute proactively?
   • Yes: Collaborator
   • No: go to step 4
4. Will the AI coordinate multiple tasks across platforms?
   • Yes: Orchestrator
   • No: review the process

‍

Example: Anticipating Customer Churn

‍

Here’s what this could look like (this is a hypothetical scenario). ‍

‍

Allie runs customer success at HelthYTech, a growing SaaS business with $50M in annual revenue. She wants to use AI to anticipate customer churn and proactively address problems.

‍

Allie is considering a tool that features predictive analytics. She applies the TACO model, seeing that her intended use case fits in the Collaborator stage. The tool’s predictive capabilities also place it in the Collaborator stage, meaning it aligns with Allie’s intended use case. She implements the tool, which helps her team reduce churn by 23% over six months.

‍

Alternatives to the TACO AI Maturity Model

‍

If the TACO model is not well-suited for your AI capabilities, here are other models you can consider for your SaaS company.

‍

1. MIT CISR Enterprise AI Maturity Model

‍

The MIT CISR model evaluates enterprise AI capabilities that correlate with financial performance. Based on research from 721 companies, organizations in higher stages consistently outperform industry financial averages.

‍

Phases:

‍

• Experiment & Prepare - AI education and policy development (28% of companies)
• Build Pilots & Capabilities - Systematic pilot programs and data consolidation (34%)
• Develop AI Ways of Working - Industrialized AI with scalable architecture (31%)
• AI Future-Ready - AI embedded in all decision-making (7%)

‍

While TACO evaluates individual AI tools for immediate operational deployment, MIT CISR measures enterprise-wide AI transformation and its correlation with financial performance. You can use MIT CISR for strategic planning and board discussions about AI investment ROI, but use TACO when your team needs to choose between specific AI tools for a particular operational challenge.

‍

2. Gartner AI Maturity Model

‍

Gartner's framework provides a comprehensive multi-dimensional assessment across seven high-performance organizational capabilities. Unlike single-path models, it evaluates AI strategy, value, organization, people & culture, governance, engineering, and data as separate dimensions that can mature independently.

‍

Phases:

‍

• Planning - Strategic foundation setting and initial AI roadmap development
• Experimenting - Pilot projects and proof-of-concept initiatives across dimensions
• Stabilization - Process standardization, governance establishment, and risk management
• Scaling - Enterprise-wide deployment and cross-functional integration
• Leading - Industry leadership, innovation, and advanced AI-driven business models

‍

TACO focuses on the functional capabilities of individual tools, while Gartner assesses whether your organization has the strategy, culture, and infrastructure to support AI initiatives. Choose Gartner when you need to diagnose organizational gaps before implementing AI, but use TACO when you already know you need AI and want to evaluate which specific tools can deliver the operational capabilities you require.

‍

3. McKinsey Responsible AI Maturity Model

‍

McKinsey's RAI framework assesses how organizations develop and implement responsible AI governance practices. Their research focuses on building trustworthy AI systems through proper oversight, risk management, and ethical deployment frameworks.

‍

Phases:

‍

• Initial - Organizations beginning to establish basic responsible AI foundations
• Developing - Implementation of core governance structures and risk identification
• Advancing - Systematic integration of responsible AI practices across operations
• Comprehensive - Full implementation with proactive monitoring and governance
• Leading - Advanced responsible AI practices with continuous improvement and oversight

‍

McKinsey's RAI model focuses exclusively on governance, ethics, and risk management rather than operational capabilities. While TACO helps you select AI tools based on what they can do functionally, McKinsey RAI helps you implement those tools responsibly and safely. Most SaaS companies typically need both TACO for tool selection and capability matching and McKinsey RAI for governance and responsible deployment frameworks.

‍

The Bottom Line: Use TACO to Evaluate Operational AI Applications

‍

Now that you understand how TACO works and compares to other AI maturity models, you have the tools to evaluate AI capabilities strategically. Whether using TACO for operational tool assessment or the other alternative tools, these frameworks provide structured approaches to navigate the AI landscape.

‍

However, selecting the right framework is just one step. You also need to turn AI insights into actionable business decisions. This is where Chief's Predictive Operations Platform bridges the gap. Chief leverages multiple LLMs like GPT-4o, Claude, and Llama to transform your SaaS data into predictive insights that drive growth, enhance customer experiences, and deliver ongoing support for data-driven decision making.

‍

Chief turns your operational data into actionable insights using the same advanced AI models you've been evaluating. Instead of choosing between individual AI tools, get them all working together in one platform designed specifically for SaaS growth. Schedule a demo to see how Chief transforms AI capabilities into measurable business outcomes.