Gartner forecasts that over 80 per cent of enterprises will have used generative artificial intelligence (GenAI) by 2026, up from less than five per cent in 2023.
This includes enterprises that will use GenAI APIs or models and or deployed GenAI-enabled applications in production environments, according to the 2023 Gartner Hype Cycle for Generative AI.
“Generative AI has become a top priority for the C-suite and has sparked tremendous innovation in new tools beyond foundation models,” Gartner's distinguished VP analyst Arun Chandrasekaran said.
“Demand is increasing for generative AI in many industries, such as healthcare, life sciences, legal, financial services and the public sector.”
Gartner predicts three GenAI technologies – GenAI-enabled applications, foundation models and AI trust, risk and security management – will have a significant impact on enterprises within the next ten years.
GenAI-enabled applications
GenAI-enabled applications that enhance user experience and assist users with tasks are expected to permeate a wide spectrum of skill sets within the workforce.
“The most common pattern for GenAI-embedded capabilities today is text-to-X, which democratises access for workers, to what used to be specialised tasks, via prompt engineering using natural language,” Chandrasekaran said.
“However, these applications still present obstacles such as hallucinations and inaccuracy that may limit widespread impact and adoption.”
Foundation models
Gartner placed foundation models on the Peak of Inflated Expectations on the Hype Cycle.
The company predicts that by 2027, foundation models will underpin 60 per cent of natural language processing use cases, up from fewer than five per cent in 2021.
“Foundation models are an important step forward for AI due to their massive pretraining and wide use-case applicability,” Chandrasekaran said.
“Foundation models will advance digital transformation within the enterprise by improving workforce productivity, automating and enhancing customer experience and enabling cost-effective creation of new products and services.”
“Technology leaders should start with models with high accuracy in performance leaderboards, ones that have superior ecosystem support and have adequate enterprise guardrails around security and privacy."
AI trust, risk and security management (AI TRiSM)
Gartner forecasts that AI TRiSM will reach mainstream adoption within two to five years.
AI TRiSM ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection.
It includes solutions and techniques for model interpretability and explainability, data and content anomaly detection, AI data protection, model operations and adversarial attack resistance.
Gartner said that by 2026, enterprises that operationalise AI transparency, trust and security will see their AI models achieve a 50 per cent improvement in terms of adoption, business goals and user acceptance.
“Organisations that do not consistently manage AI risks are exponentially inclined to experience adverse outcomes, such as project failures and breaches,” Chandrasekaran said.
"Inaccurate, unethical or unintended AI outcomes, process errors and interference from malicious actors can result in security failures, financial and reputational loss or liability, and social harm."
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