How to Build a Mature Machine Learning Organization

The Georgian Impact team adds value to our portfolio companies by accelerating their adoption of major trends such as applied AI. We regularly assess how we can be most effective and have the biggest impact through our work, whether it’s advisory work, applied research engagements or the development of software products.

We’ve spent a lot of time thinking about the paths that software companies take towards maturity in artificial intelligence and machine learning (AI/ML). As a result, we created an AI/ML maturity framework for companies to plot their own maturity and identify activities to progress to the next stage.

Why Did We Create The Framework?

We use this maturity framework at Georgian to identify how we can help our companies adopt AI. Working with them, we help to set the strategy and identify the highest value product opportunities in AI. We then support our companies as they build and scale their ML team. For more mature organizations, we partner on applied research projects to build software solutions. Where these solutions are applicable to more than one organization, we make the underlying algorithms available as open-source software libraries to our portfolio.

The framework covers product, technical, and organizational readiness, recognizing the importance of cross-functional expertise in bringing AI-driven products to market.

The framework covers product, technical and organizational readiness, recognizing the importance of cross-functional expertise in bringing AI-driven products to market.

How Does the Maturity Framework Work?

Our AI/ML Framework has four levels: Getting started, exploring, building and advanced. At each level, organizations will have to tackle a unique set of challenges, be it setting a strategy, making your first hires or putting more mature processes into place.

Getting Started

FocusCompany CharacteristicsHow to Reach the Next Stage
ProductAI/ML not seen as a strategic priority.
No understanding of opportunities.
Run a workshop to set vision with the leadership team.
Identify opportunities that are high value/low cost.
DataNo understanding of available data.Conduct a data audit.
TeamNo ML team in place.Hire or assign existing Product Manager.


FocusCompany CharacteristicsHow to Reach the Next Stage
ProductAI/ML is a strategic priority.
Some understanding of opportunities available through AI/ML.
Define product strategy and prioritize AI/ML opportunities.
Build first ML-based minimum viable product (MVP).
DataSome understanding of available data.
TeamNo ML R&D team in place.Hire ML Scientist and Engineer.
Establish cross-functional initiative with Product, UX, Data Science,
Engineering and Marketing teams.


FocusCompany CharacteristicsHow to Reach the Next Stage
ProductGood understanding of product opportunities and priorities.
At least one AI/ML model in production.
Roadmap in place.
Go-to-market plan in place.
Comprehensive sales enablement and marketing program.
DataGood understanding of available data.Ensure ML projects have feedback loops and models continuously improve.
TeamSmall ML R&D team in place.
Some engineering support for AI/ML projects.
Scale ML R&D team.
MLOpsDevelop processes and invest in appropriate frameworks for ML development and deployment.


FocusCompany Characteristics
ProductContinuous execution on AI product strategy and roadmap.
Excellent understanding of how to market, brand and differentiate on AI-based capabilities in the market.
DataExcellent understanding of data.
AI/ML models continuously improve via customer feedback loops.
TeamMature product and R&D organization that continuously deploys ML into production.
MLOpsMature processes and technology.

Factors to Consider at Each Level

Getting Started

First, develop a strategy and application focus for AI/ML. At the Getting Started stage, consider the following questions with your leadership team:

  • Start by documenting the business processes your solution enables
  • Who are your key customers and audiences?
  • How can you address their daily pain points or improve their workflow?
  • What key insights are they looking for?
  • Where do they currently experience friction?
  • How can automation improve processes and job roles? Do they regularly make decisions based on data?

Talk to product management, customer success, support, sales and your clients themselves, gather information, and answer the questions above.

Use the information gathered to brainstorm opportunities for automation in your business, always keeping in mind that the end goal is to make your customer’s life better. Think about where you could drive the most valuable insights with your unique data for your customers. You can prioritize these opportunities using a value/cost framework. This allows you to build a priority list before you hire anyone and know why you’re hiring.



Make your first hires count. Hire or assign a product manager who can help to validate market problems, create product requirements and manage the roadmap. The next roles to hire are an ML Scientist to run experiments and an ML engineer who can support the experimentation, establish a data pipeline, and integrate predictions and models into the core application. Hire the most senior scientist you can attract to set your team up for long-term success. Ideally, the scientist you hire should also have engineering skills. A senior hire can:

  • Help build and attract a first-class data science team
  • Accelerate the R&D by selecting the most important avenues to explore
  • Drive the creation of IP and develop ML models that are close to production-ready
  • Set up tooling and processing efficiently and with scale in mind.

Leverage domain expertise from elsewhere in the organization to guide the ML scientist. The scientist and engineer can then learn in parallel about their respective parts of the problem. The scientist will focus on understanding the nature of the problem; the engineer can create the architecture and think about questions such as data security and privacy.

Make sure your early hires are good communicators so that they can quickly understand business problems and report back on their findings with clarity to the leadership team.


At the Building stage, you understand the opportunities and have a small team in place but have yet to scale any projects into production. At this stage, you will want to start putting the right ML tools and processes in place. Prioritize and focus on one use case that is feasible to take to market within a relatively short time frame. Refer to our Principles of Applied AI for guidance on how to apply ML. Use tools like the AI Project Canvas document your plan.

Validate that you are solving market problems just as much as you would with any other product. Early adopters or an advisory board can help.

At this stage, build your go-to-market plan, maintaining focus on how you are solving your buyer’s problems. Not everyone will need to know that machine learning is driving the product. Understand which of your buyer personas will be impressed by the underlying technology and build a sales enablement and marketing program to deliver the right messages along the buyer journey.

Once you have brought your first product to market, revisit your priorities and ensure that the roadmap still makes sense.

As you start to iterate and bring more models into production, you’ll need to scale and hire more specialized roles on the team. For example, you can bring on:

  • A data engineer to build a catalog of all available data, architect and build data pipelines
  • An ML architect to oversee the overall technology vision and implementation
  • A full-time product manager to establish a full product strategy and long-term roadmap.

Our Principles of Applied AI provide a pragmatic framework to assist the adoption of machine learning and other building blocks of AI in your software solution.

The white paper:

  • Gives you a framework for getting started and avoiding common mistakes
  • Provides a maturity model so that you can measure your progress

Get the Principles of Applied AI >


At the Advanced stage, you’re a mature AIML organization with an established data science team, closely aligned with Product and Engineering teams. Your processes and tooling allow you to manage many ML models in production. Your team and company are recognized as thought leaders in your space and you are able to use this to clearly differentiate from your competitors.

Companies at the bleeding edge may be developing their own AI/ML frameworks to bridge gaps in open source and commercial tools and are likely deploying the latest AI/ML research into production to build a strong, differentiated product.

Why Use the Maturity Framework?

Our maturity framework allows companies that are on the path towards AI/ML maturity to perform an honest assessment of where they are today and understand what they need to do to reach the next stage.

We’d love to hear from organizations about their thoughts on the framework and where they think they currently sit. What’s the main block on your path to maturity? How did you get started? What resources were most useful to you? How did you map out the most valuable opportunities available to you? How did you set about hiring a team? How did you balance building an AI/ML organization versus other roadmap priorities? Who is the champion in your organization? What benefits have you seen since starting on your journey?

Get in touch here.


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