AI training data is a key to success, and budgets will increase
LXT
March 8, 2022
Executive Summary
by Joaquim Cardoso MSc.
Chief Strategy Officer (CSO) — “The Digital Health Institute”
March 15, 2022
About the research (see appendix for more details)
- 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.
Here are some of the key takeaways:
AI investment is strong across all company types surveyed:
- The survey found that over a third of high revenue companies are spending between $51–100 million on AI and seven in ten organizations are spending at least $1 million or more of their budget on AI.
More than 40% of organizations have reached maturity:
- more than 40% of organizations have reached high levels of maturity, from Operational (AI in production, creating value) to Transformational (AI is part of business DNA) status.
AI training data is a key to success, and budgets will increase:
- When asked about the benefits experienced as a result of high-quality training data for AI, companies that are earlier in their AI journey see efficiency and agility gains, while more mature organizations report accelerated time to market and improved competitive advantage
AI mature organizations:
Dedicate higher budgets 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:
- 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.
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.
ORIGINAL PUBLICATION
Last month, we published the findings of our recent executive survey in a report entitled The Path to AI Maturity .
Our goals for this commissioned research study were to understand how companies currently view their level of AI maturity, the characteristics of companies that have reached higher levels of maturity as well as how these attributes change as a company matures in its AI journey, and to share these insights with the broader AI community.
Here are some of the key takeaways:
- AI investment is strong across all company types surveyed
- More than 40% of organizations have reached maturity
- AI training data is a key to success, and budgets will increase
1.AI investment is strong across all company types surveyed
The survey found that over a third of high revenue companies are spending between $51–100 million on AI and seven in ten organizations are spending at least $1 million or more of their budget on AI.
Enterprises are using AI to innovate, scale up and drive competitive advantage as well as gain internal efficiencies.
2.More than 40% of organizations have reached maturity
As part of the survey, executives placed their companies on the Gartner AI Maturity Model.
According to the results, more than 40% of organizations have reached high levels of maturity, from Operational (AI in production, creating value) to Transformational (AI is part of business DNA) status.
Given the high percentage of AI projects that fail to move to the production phase, it is encouraging to see that 40% of companies have started to see ROI from their AI initiatives and are even moving beyond this phase where AI is becoming part of the DNA of their business.
To get to maturity, a quarter of maturing organizations are spending $51 million or more on AI, compared to just 8% of experimenters who represent the 60% of companies that are in the earlier stages of maturity. This underscores the investments needed to get to AI maturity.
3.AI training data is a key to success, and budgets will increase
Mature organizations say that quality training data is the most important contributor to the success of their AI strategies.
Mature organizations say that quality training data is the most important contributor to the success of their AI strategies.
When asked about the benefits experienced as a result of high-quality training data for AI, companies that are earlier in their AI journey see efficiency and agility gains, while more mature organizations report accelerated time to market and improved competitive advantage.
Four in 10 organizations surveyed allocate a high proportion (70%+) of AI budget to training data.
On average, over half of the total AI budget for the companies surveyed is dedicated to training data.
Organizations that are investing in AI recognize the importance of high-quality training data and how prioritizing this investment allows them to get to market faster with far less rework.
As a result, two-thirds of all organizations expect their need for training data to increase over the next five years.
Conclusion
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
- Use AI to scale up and create competitive advantage
- Rely on supervised and semi-supervised machine learning
- Consider quality training data to be a key to the success of their AI strategies
1.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.
2.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.
3.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.
4.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.
APPENDIX
About the research
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
AI Trends by Industry
Financial services companies report highest levels of AI maturity
The financial services industry was an early adopter of AI to maintain competitive advantage in a market that has been disrupted by new entrants. With the growth in customer demand for digital experiences, financial services institutions have embraced AI as a powerful tool to transform their businesses, from internal operations to customer engagement. The availability of structured transactional datasets provides this industry with a strong foundation for AI initiatives; 43% of respondents stated that their organizations have reached the highest levels of maturity.
Following the financial sector is the technology industry;
22% of organizations in this vertical report having reached the Systemic and Transformational levels of AI maturity.
Text is the leading data type used across all industries
Future data types include audio, user behavior and video When asked about the types of data currently used across industries, text data was first on the list, followed by numerical data and then product/SKU data. The financial services industry relies heavily on numerical, text and transactional data as one would expect. In manufacturing/ automotive, sensor data is the most used data type. Plans for future data types vary by industry, with audio, user behavior and video data cited the most often.
Learn more in the full report
Download the full findings here .
Originally published at https://www.lxt.ai on March 8, 2022.
