The last couple years have seen accelerating adoption of AI and Machine learning in business. Here we have compiled a list of key statistics as 2022 unfolds - covering everything from adoption and implementation to maturity and concerns.
A.I. Usage in Organizations
More than half of organizations use A.I. for at least one business function (Source)
Only 17% of companies use A.I. for customer service analytics, and only 14% use it for customer segmentation (Source)
Product and service development and optimization is where the majority of companies are currently using A.I. (Source)
Only 9% of companies are currently using A.I. for Supply-Chain Management (Source)
IDC (International Data Corporation) predicts that 60% of enterprises will deploy AI enabled tools and functions to manage network performance issues (Source)
61% of business executives with an innovation strategy say AI helps them identify otherwise missed opportunities in data (Source)
75% of commercial enterprise applications will use AI by 2021, according to IDC predictions (Source)
65% of companies say the Covid-19 pandemic revealed gaps or shortcomings in their analytic and AI/ML models (Source)
$57.6 billion will be spent globally on cognitive and AI systems in 2021. Spending is expected to hit $98 billion by 2023 (Source)
72% of leaders surveyed in the technology, media and telecommunications industry think their product offerings will be impacted significantly by AI in the next five years (Source)
In a 2020 study, IDC found that 2 out of 3 organizations expect their spending on analytics and AI to increase or remain steady (Source)
70% of managers and directors say they are being called upon to make their businesses more data drive (Source)
Impact on Revenue and Costs
80% of firms using A.I. for Marketing and Sales saw an increase in revenue in 2018 (Source)
79% of firms using A.I. for Marketing and Sales saw an increase in revenue in 2019 (Source)
10% of firms saw an increase in revenue of greater than 10%, while 26% saw between a 6 and 10% increase (Source)
40% of businesses using A.I. saw a cost decrease for the function in which it was used (Source)
Respondents at high AI performing companies were 2.3 times more likely to see their C-level executives as being very effective (Source)
Adoption Rates
Only 26% of AI adopters are seen as Seasoned according to Deloitte Insights (Source)
90% of seasoned adopters see AI as very or critically important to their business (Source)
53% of adopters have spent more than $20 million over the past year on AI related technology and employees (Source)
71% of adopters expect to increase their investment in the next year (Source)
81% of seasoned adopters report their payback period is less than 2 years (Source)
The majority of AI adopters see their industries transforming in the next 1 to 3 years (Source)
93% of adopters use cloud-based AI capabilities (Source)
Talent and Implementation
Only 47% of AI adopters say they have a high level of skill related to selecting AI technology and suppliers (Source)
Only 45% say they have a high level of skill in incorporating AI into their existing IT environment (Source)
70% of business decision-makers say workers can focus on more meaningful work with the help of AI (Source)
88% of companies that get value from an AI solution see the value as coming from an integration with overall digital strategy (Source)
Market leaders (top quartile) have AI talent that is 2-3 times larger than other organizations (Source)
AI tools and services are being expanded by cloud service providers (Source)
“Bain expects that by the end of the 2020s, automation of business processes may eliminate 20% to 25% of current jobs” (Source)
80% of tech and business leaders believe AI creates jobs and improves worker efficiency (Source)
54% of business executives who have implemented AI solutions say productivity has already increased (Source)
“Enterprises have started realizing the importance of democratizing AI to address this persistent AI talent gap,” Alisha Mittal, Everest Group (Source)
Concerns about A.I.
57% of adopters see using personal data of their customers without consent as a major or extreme concern (Source)
53% of adopters worry about liability for decisions and actions made by AI systems (Source)
54% of adopters see potential bad decisions made by AI as a major or extreme concern (Source)
57% worry about how new regulations will impact their AI initiatives (Source)
47% of executives say it’s challenging to integrate AI initiatives with existing processes and systems (Source)
Organizations see AI as both an opportunity and a risk (Source)
Chinese firms see AI as riskier, but also see a greater revenue impact than other parts of the world (Source)
Legal Issues
The NIST recently published a white paper on the Four Principles of Explainable Artificial (Source)
Intelligence that “Are heavily influenced by considering the AI system’s interaction with the human recipient of the information” (Source)
Principle 1 “Explanation: Systems deliver accompanying evidence or reason(s) for all outputs.” (Source)
Principle 2 “Meaningful: Systems provide explanations that are understandable to individual users.” (Source)
Principle 3 “Explanation Accuracy: The explanation correctly reflects the system’s process for generating the output.” (Source)
Principle 4 “Knowledge Limits: The system only operates under conditions for which it was designed or when the system reaches a sufficient confidence in its output.” (Source)
The NIST also clarified explanations of user benefit, societal acceptance, regulatory compliance, system development, and owner benefit (Source)
Usage Contexts
According to Fortune Business Insights the worldwide speech and voice recognition market is expected to grow to $28.3 billion by 2026 (Source)
Researchers recently created a new machine learning hearing model that performs 2000 times faster than other machine-based hearing solutions (Source)
”Companies expect significant growth in advanced automation over the next two years” (Source)
Conversational AI and Machine learning are seen as the most complex automation tasks (Source)
Approximately half of automation processes are not meeting savings goals according to a global automation survey (Source)
Companies say they cut an average of 20% of costs using automation (Source)
“The median payback period on automation is about 13 to 18 months” (Source)
Automation has reduced customer status update labor time by an average of 27% (Source)
“Executives cite staff flexibility, direct cost savings, and faster processes as the main benefits of automation” (Source)
75% of companies plan to increase automation initiatives because of Covid-19 (Source)
AI will become better at processing unstructured data (also known as Robotic Process Automation or RPA) “Using AI to complete the complex task of understanding unstructured data and then provide a defined output such as a customer’s intention will enable RPA to complete the action.” Wayne Butterfield, Director of ISG Automation (Source)
The increase of remote employees will lead to an increase in the adoption of natural language processing and automated speech recognition, especially in customer contact centers according to Butterfield (Source)
AI Maturity
A research cooperation between MIT and the Boston Consulting Group recently identified four subgroups of AI maturity (Source)
Pioneers (20%) – organizations that have understood and adopted AI
Experimenters (18%) – Organizations that have AI pilot initiatives or programs, but little depth of understanding about AI
Investigators (30%) – Organizations that have some knowledge of AI, but are not currently deploying beyond a pilot stage
Passives (32%) – Organizations which have not adopted any AI initiatives and have little understanding of the technology
72% of AI adopters who had seen revenue growth expect that growth to continue (Source)
AI Pioneers are taking what are perceived as greater risks, but are also seeing higher rates of return across their projects (Source)
35% of AI pioneers have invested in at least 20 AI projects (Source)
Future Concerns
“Business executives need to consider how they can reinvent and reimagine many of those processes in the context of what AI enables. This is where AI’s true potential will emerge: not in doing the same thing better, faster, and cheaper but by doing new things altogether” (Source)
“Taking on a lower-risk cost reduction initiative is less likely to produce transformational strategic results and is empirically less likely to create expectation of increased value over time. Doing so, however, can allow a company to develop new ways of working across the business in order to start building organizational capabilities to get value from AI.”(Source)
87% of tech firms are not satisfied with their current use of AI (Source)
90% of tech executives see AI and machine learning as a priority to be incorporated with their products (Source)
85% of executives are uncertain of the value of AI (Source)
85% of executives don’t feel they have the needed talent to take advantage of AI (Source)
81% of executives feel that current AI solutions in the marketplace are not relevant to their needs (Source)
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