The path to AI maturity

An executive survey


In late 2021, LXT commissioned a survey of 200 senior executives (two-thirds C-Suite) with artificial intelligence (AI) experience at mid-to-large US organizations. The goal of the research was to understand the current status of AI maturity in organizations overall, and the unique characteristics of organizations based on their AI maturity phase. The results of this study reveal the investment levels and business drivers behind AI initiatives at all phases – and the associated benefits of AI – which can help shape the business case for increased investment in the technology to fuel business growth.

This study is based on Gartner's AI Maturity Model which characterizes an organization's journey with AI into 5 levels:

Gartner’s AI maturity model levels

  • Level 1: Awareness – An early interest in and conversations around AI strategy
  • Level 2: Active – Initial experimentation and pilot projects with AI
  • Level 3: Operational – AI used in at least one workflow
  • Level 4: Systemic – AI is present in the majority of workflows/operations, inspiring new, digital business models
  • Level 5: Transformational – AI is inherent in the DNA of the business
Our findings provide a view into the current AI maturity level of 200 US-based organizations. Awareness and Active level organizations share several attributes in the earlier stages of adoption, so they are identified in this report as “Experimenters.” Because the Operational, Systemic, and Transformational level ones are farther in their AI journey, they are called “Maturing.”

The findings in this study provide guidance for organizations whose goal it is to reach the Transformational level and reveal the characteristics of the companies who have succeeded in reaching this phase.

This report also examines AI trends by industry, including an analysis of AI maturity for five verticals. The research investigates how the drivers behind AI strategies differ for these industries.


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Key findings

AI investment

  • More than half of organizations surveyed (56%) are spending between $1 million and $50 million on AI annually, and 15% are spending $51 million or more.
  • Over a third of high revenue companies are spending between $51 million and $100 million on AI annually.
  • AI strategies are primarily driven by innovation and growth needs, where AI enables businesses to scale and innovate faster, and to secure competitive advantage.
  • Efficiency and productivity gains are seen as the most dominant problems that AI can solve across industries. Improved analytics and business expansion are also high priorities.

AI and maturity levels

  • 40% of organizations rate themselves within the three highest levels of AI maturity according to Gartner’s AI Maturity Model: Operational, Systemic, and Transformational.
  • Companies in the Systemic and Transformational levels of the AI maturity model are using AI to scale and drive competitive advantage and product innovation. They are also budgeting higher amounts overall for AI programs, and are using both supervised and semi-supervised machine learning methods.
  • AI investment and AI maturity correlate, with a quarter of AI maturing organizations spending $51 million or more on AI, compared to just 8% of experimenters (those organizations in the awareness and experimental stages of AI adoption).
  • Regarding the drivers behind AI strategies, businesses at the Transformational end of the scale have already experienced the benefits of better risk management and delivering on customer needs. Now, they are looking to scale up and accelerate product innovation. AI Experimenters are focused on driving innovation and managing large volumes of data.
  • AI Maturing organizations rely more on semi- and fully-supervised machine learning and AI nascent organizations report a greater use of unsupervised machine learning.
  • Maturing organizations view quality training data as an essential contributor to AI success.
  • When asked about the benefits experienced as a result of high-quality training data for AI, Experimenters see efficiency and agility gains, while Maturing organizations report accelerated time to market and improved competitive advantage.
  • Two-thirds of all respondents expect their need for training data to increase over the next five years, and AI Maturing organizations indicate the highest need to increase their training data budgets over this timeframe.

AI trends by industry

  • The financial services industry is leading the way, with 43% of organizations having reached Systemic or Transformational levels of AI maturity.
  • The tech industry follows the financial services industry, with 22% of organizations having reached the highest levels of AI maturity.
  • Respondents state that efficiency and productivity gains are the most common goals of AI strategies for their industries as a whole, except for financial services companies, which say that improved analytics is the main goal.
  • The tech, retail, manufacturing/automotive and professional services industries are deploying AI to innovate and advance product development, while financial services companies are driven by competitive advantage and risk management.
  • Text data is the leading type used across all industries; future data types include audio, user behavior and video.

01 AI investment

AI investment is strong across mid-to-large US organizations
The potential of AI has yet to reach its peak. AI continues to boom as both a trend and emerging technology, with many organizations investing heavily in AI innovation. The strategic importance of AI is growing rapidly too, as businesses start to see the benefits of their investments.

Seven in ten organizations are spending $1M or more of their budget on AI
Our research found that over half of organizations (56%) are spending between $1 million and $50 million on AI annually, and 15% are spending $51 million or more.

Survey Q10: In which range approximately is your organization’s total budget for AI?
a graph of the total budget for artificial intelligence

AI budgets correlate with an organization’s revenue
When it comes to companies grossing $100 million to $1 billion a year, AI investments are proportionally steep. The annual budgeted spend on AI for these businesses falls between $1 million and $50 million. And for that upper level of businesses earning $1 billion in revenue, 37% of them are spending $51 million to $100 million on AI.

LXT State of Training Data in AI.
Survey Q10: In which range approximately is your organization’s total budget for AI?
Survey Q2: Approximately how much is your company’s annual revenue worldwide?

ai across revenue levels

Overall, there’s a good correlation (38%) between an organization’s revenue and its level of AI investment
43% of organizations with over a $1 billion of annual revenue are spending $51 million or more on AI annually. In contrast 95% of organizations with less than half a billion revenue are spending less than $50 million annually on AI.

Innovation and product development are key drivers for AI strategy
Organizations surveyed said there were five primary drivers for investing in AI:

  • Innovation and product development
  • Ability to scale more quickly
  • Competitive advantage
  • Risk management
  • Handling large volumes of data
Interestingly, cost reductions rank lower on the priority list when it comes to increased AI implementation. Just 34% of businesses cite cost savings as a driver, which indicates that innovation, competitiveness, and business growth are more critical priorities.
Survey Q3: What are the key business drivers of your organization’s AI strategy?
a graph about key business drivers of AI strategies

Businesses agree that AI brings efficiency and productivity gains
When asked about the potential impact of AI on their specific industries, 65% of survey respondents state that efficiency and productivity gains are the most dominant problems that AI can solve. This is followed by improved analytics (50%) and business model expansion (48%). Customer retention ranked lowest at 33%. Respondents were able to select as many options as were applicable to their organization from a list of choices.

Survey Q6: In your opinion, what problems can AI solve in your industry, outside of your organization?


AI is fast becoming one of the most important technologies behind any business. Companies that have reached AI maturity are using the technology to transform into AI-first organizations where the technology is embedded into the fabric of the business. This research study uncovered the following characteristics of these firms:
AI mature organizations:

Dedicate higher budgets overall for their AI programs
Companies in the Systemic and Transformational levels on the maturity model are budgeting higher amounts overall for their AI programs. This is empowering, as it illustrates a direct relationship between AI success and allocation of investment. It presents a clear path for businesses looking to get ahead in the AI race

Use AI to scale up and create competitive advantage
The results of the study show that when organizations reach the highest levels of AI maturity, their business drivers shift. While in the earlier phases of AI maturity companies are predominantly focused on deploying AI to drive innovation and product development, a shift occurs at the Systemic and Transformational stages. Organizations that have reached these levels of maturity are now using AI to scale more quickly; they have established a strong foundation with AI and are now well positioned to expand.

Rely on supervised and semi-supervised machine learning
Machine learning models also vary across AI maturity levels. Systemic and Transformational companies are focused on supervised and semi-supervised machine learning approaches while those at the Awareness stage lean more towards unsupervised machine learning.

Consider quality training data to be a key to the success of their AI strategies
Research findings show that as companies move through the phases of AI maturity and reach a tipping point where AI is successfully in production, the value of quality training data increases. Systemic and Transformational organizations say quality training data is the most important contributor to the success of AI strategies, ahead of quality controls and good algorithms. As a result, companies at the highest levels of maturity indicate the strongest need to increase their training data budgets over the next five years.

Path to AI maturity


Behind the research

LXT commissioned a survey of 200 senior decision-makers working for US organizations. Two-thirds of our respondents were of C-Suite level and all those who took part had verified AI experience; only 25% of those who applied met the criteria required for participation, which included their level of AI knowledge and experience.

Contributors were engaged using online surveys, answering on behalf of a range of business sizes, revenues and industries. Each participant represents a US organization with at least $100 million in annual revenue and over 500 employees.

The research was conducted from November 29 to December 10, 2021, by Reputation Leaders, an independent research organization. Interviews were conducted in the US by online survey using the panel services of Borderless Access.

Reputation Leaders is a global thought leadership consultancy that causes people to think about your brand positively and differently.

The number of employees in your organization around the world

About LXT

LXT is an emerging leader in AI training data to power intelligent technology for global organizations. In partnership with an international network of contributors, LXT collects and annotates data across multiple modalities with the speed, scale and agility required by the enterprise. Our global expertise spans more than 145 countries and over 1000 language locales. Founded in 2010, LXT is headquartered in Toronto, Canada with presence in the United States, Australia, Egypt and Turkey. The company serves customers in North America, Europe, Asia Pacific and the Middle East.

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