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Market Trends and Growth Factors

Looking forward, our 2030 market size projections begin to exceed the AI GovTech and AI TRiSM markets by nearly 90 times and 50 times, respectively. This divergence is also captured by our estimated CAGR of 108% for the AIAT market, as compared to estimates ranging from roughly 40-60% for the AI GovTech market or 15-20% for the AI TRiSM market 1. In our estimation, these reports are undervaluing the growth rate of AIAT markets, which should experience a growth rate faster than the AI market—often ranging from 30% to 50%, depending on what AI businesses are in-scope 2.

"The pace and advancements in technology innovation, particularly around Generative AI, is at a rate which we have never experienced before. This is exposing new risks and gaps in enterprise risk and governance processes to ensure the safe and trusted outcomes from AI investments, alongside the ability to comply with an evolving AI regulatory landscape."

David Marriage
Partner, PwC UK

At a minimum, the forthcoming wave of AI regulation enforcement will soon provide a substantial boost to the scale of the AIAT market and also establish a market floor. Moreover, there are additional reasons that AI companies and AI-adopting enterprises will invest in safety and security beyond the thresholds set by legislation: AIAT will both support with the minimisation of losses due to AI risks and may also confer competitive benefits by way of improved model performance, increased end-user trust and engagement, or sustained access to strategic geographies or sectors with evolving AI standards.

The sum total of both required and voluntary AIAT spending will be the modern-day enterprise governance, risk management, and compliance (GRC) budget in the age of AI. As a general-purpose technology, AI is expected to be used in virtually every sector of the economy. This means sectors that traditionally had smaller GRC budgets will now find it imperative to increase related spending; likewise, it is plausible that GRC allocations across many industries will begin approaching proportions comparable to that of banking, financial services, and insurance (BFSI)—a sector where maintaining trust and integrity is of critical importance.

To illustrate further our rationale for a forecasted year-over-year doubling of the AIAT market, there are five key market trends to review:

KEY MARKET TRENDS
  1. The excitement around AI is translating to real investments: With 42% of large firms already using AI technologies in their operations and 38% now making use of Generative AI 3, there is a growing need to ensure the reliability of AI systems. Moreover, by 2025 it is expected that large enterprises will allocate over 40% of their IT budgets to AI-related initiatives, and worldwide investment in AI solutions could reach over $500 billion by 2027 4. And thirdly, the growth rate in the cost of training notable ML models since 2009 has, on average, tripled (~3.1x) every year since! 5.
  2. In tandem with rising AI adoption, are rising safety and ethical concerns: Growing concerns from consumers and businesses about reliability, bias, fairness, and accountability in AI decision-making are prompting organisations to consider what contemporary risk management means in the age of AI. In one survey, AI “inaccuracy” was considered the most relevant threat among respondents, followed by cybersecurity, IP infringements, explainability, and data privacy 6. Another survey found data privacy (57%) and trust and transparency (43%) concerns among the biggest inhibitors for IT professionals not implementing Generative AI 7. Without commensurate investments in AIAT solutions, business leaders recognize that AI-adopting organisations will be exposed to heightened risks of reputational damage, the loss of customer trust, financial loss, regulatory penalties, or litigation 8.
  3. Suitable regulations have only just begun to take shape: There is a dynamic and rapidly-unfolding regulatory landscape around the full lifecycle of AI, including upstream inputs and downstream applications, as demonstrated by recent legislation from the EU AI Act, GDPR in Europe, White House Executive Order, US Department of Commerce, UK AI Regulations, Hiroshima Process, or the CAC Provisions on Deep Synthesis. Globally this process is far from finished, and it will also take years to implement and enforce compliance requirements at-scale. Likewise, companies—meaning AIAT customers—would be wise to act preemptively, future-proofing their business operations ahead of forthcoming legislative requirements.
  4. Organisations have much to improve regarding risk management: It is currently estimated that only 6% of organisations have a dedicated team for risk assessment and mitigation as it relates to Generative AI, and similarly, only 5% consider their organisation to have a mature AI governance program 9. Likewise, other surveys demonstrate that the vast majority of AI-adopting organisations are not prepared for reducing unintended bias (74%), mitigating performance variations (68%), making sure their AI is explainable (61%), developing ethical AI policies (60%), or safeguarding against adversarial threats (59%). In light of the above-mentioned market trends, enterprises are expected to invest in closing this risk management gap as quickly as possible.
  5. AI will soon intersect with other technological breakthroughs: With AI capabilities showing no sign of slowing down, AI is expected to accelerate new scientific discoveries, solving unaddressed problems and further contributing to economic growth 10. Positive signals have emerged in nanotechnology 11, chemistry and materials science 12, biotechnology 13, sixth-generation wireless technologies 14, space exploration 15, as well as neurotechnology 16, just to name a few. Considering ongoing improvements in the generality, multimodality, autonomy, fidelity, and adaptability of AI—among its other attributes—it becomes evermore difficult to predict what innovations await beyond the horizon. And as each of these discoveries are scaled through industrial applications, the addressable market for AI Assurance Tech solutions will expand even further.

Footnotes​

  1. Persistence Market Research. "AI Governance Market to Reach a Revenue of Around US$4.7 Billion by the End of 2033 at a CAGR of 40.5% - Persistence Market Research." GlobeNewswire. May 13, 2023. Accessed April 6, 2024. https://www.globenewswire.com/en/news-release/2023/05/13/2668277/0/en/AI-Governance-Market-to-Reach-a-Revenue-of-Around-US-4-7-billion-by-the-End-of-2033-at-a-CAGR-of-40-5-Persistence-Market-Research.html; SNS Insider. "AI Trust, Risk, and Security Management [AI TRISM] Market Size to Cross USD 6.02 Billion by 2030 Due to Rising Demand for Ethical AI - Research by SNS Insider." *GlobeNewswire. *January 19, 2024. Accessed April 12, 2024. https://www.globenewswire.com/en/news-release/2024/01/19/2812347/0/en/AI-Trust-Risk-and-Security-Management-AI-TRISM-Market-Size-to-Cross-USD-6-02-Billion-by-2030-due-to-Rising-Demand-for-Ethical-AI-Research-by-SNS-Insider.html ↩

  2. Grand View Research. "Artificial Intelligence (AI) Market." Accessed April 18, 2024. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market; Expert Market Research. "Artificial Intelligence Market." Accessed April 2, 2024. https://www.expertmarketresearch.com/reports/artificial-intelligence-market; MarketsandMarkets. "Artificial Intelligence in the Healthcare Market." Accessed April 7, 2024. https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html.; Fortune Business Insights. "Generative AI Market." Accessed April 17, 2024. https://www.fortunebusinessinsights.com/generative-ai-market-107837. ↩

  3. IBM. “AI Adoption Index 2023”, IBM, 2023, Accessed April 10, 2024. https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters ↩

  4. IDC. March 7, 2023. Accessed March 4, 2024. https://www.idc.com/getdoc.jsp?containerId=prUS50454123 ↩

  5. Epoch AI. "Investment Trends." Accessed April 7, 2024. https://epochai.org/trends#investment-trends-section. ↩

  6. McKinsey & Company. "The State of AI in 2023: Generative AI's Breakout Year." McKinsey & Company: QuantumBlack. Accessed April 17, 2024. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year. ↩

  7. IBM. "Data Suggests Growth in Enterprise Adoption of AI is Due to Widespread Deployment by Early Adopters." IBM Newsroom. January 10, 2024. Accessed April 18, 2024. https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters ↩

  8. Boston Consulting Group (BCG). "Emerging AI Risks Call for a New Era of Responsible AI." Press Release. June 20, 2023. Accessed April 17, 2024. https://www.bcg.com/press/20june2023-emerging-ai-risks-need-for-responsible-ai ↩

  9. KPMG. "Generative AI 2023." Accessed April 27, 2024. https://info.kpmg.us/news-perspectives/technology-innovation/kpmg-generative-ai-2023.html ↩

  10. Organisation for Economic Co-operation and Development (OECD). "Accelerating Science Could Be the Most Valuable Use of AI." OECD.AI. Accessed April 1, 2024. https://oecd.ai/en/wonk/accelerating-science ↩

  11. IBM Research. "AI for Metamaterials." IBM Research Blog. February 21, 2023. Accessed April 1, 2024. https://research.ibm.com/blog/ai-for-metamaterials ↩

  12. Microsoft. "How AI and HPC Are Speeding Up Scientific Discovery." Microsoft News Center, Accessed April 2, 2024. https://news.microsoft.com/source/features/sustainability/how-ai-and-hpc-are-speeding-up-scientific-discovery/ ↩

  13. Gibney, Elizabeth. "AI Designs Quantum Physics Experiments Beyond What Any Human Has Conceived." Nature 590 (February 9, 2021): 507-510. Accessed April 2, 2024. https://www.nature.com/articles/d43747-021-00045-7; and ScienceDaily. "AI learns to use tools in complex ways after watching humans." ScienceDaily. August 30, 2023. Accessed April 3, 2024. https://www.sciencedaily.com/releases/2023/08/230830151740.htm ↩

  14. Kim, Taesup, James Diffenderfer, and Ishaan Gulrajani. "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE Solvers." arXiv, April 3, 2022. Accessed April 3, 2024. https://ar5iv.labs.arxiv.org/html/2204.00914 ↩

  15. Howell, Elizabeth. "NASA's Viper Moon rover will rely on artificial intelligence to chart unexplored lunar territory." Space.com. February 27, 2023. Accessed April 3, 2024. https://www.space.com/nasa-viper-moon-exploration-artificial-intelligence ↩

  16. Neuroscience News. "New Research Shows How AI Uses Imagination and Memory." Neuroscience News. November 6, 2023. Accessed April 4, 2024. https://neurosciencenews.com/ai-imagination-memory-25498/ ↩