“Worldwide IT spending is projected to total $4.7 trillion in 2023, an increase of 4.3% from 2022,” said Gartner in its July 19 forecast of IT spending. “As CIOs continue to lose the competition for IT talent, they are shifting spending to technologies that enable automation and efficiency to drive growth at scale with fewer employees.” The forecast adds that “While generative artificial intelligence (AI) is top of mind for many business and IT leaders, it is not yet significantly impacting IT spending levels. In the longer-term, generative AI will primarily be incorporated into enterprises through existing spending.”
The forecast includes a link to an excellent article, “Gartner Experts Answer the Top Generative AI Questions for Your Enterprise.” The overall assessment of the Gartner experts is that “Generative AI isn’t just a technology or a business case — it is a key part of a society in which people and machines work together.” Let me summarize their answers to some key questions.
What is Generative AI? In answer to a question or prompt, generative AI can create an impressive variety of content based on the characteristics of its training data, including images, videos, music, speech, text, software code and product designs. Generative AI is based on foundation models, - the name coined by the Stanford Institute for Human-Centered Artificial Intelligence to large machine learning models that are trained on vast amounts of data, and can thus be adapted to a wide range of tasks.
Foundation models have been enabled by transfer learning and scale. Transfer learning is a technique in which knowledge learned after training one task can be applied to a different task, which may require some additional fine-tuning. The impressive power of foundation models is due to their vast scale, the result of improvements in computer hardware, the use of highly parallel transformer architectures, and the availability of huge amounts of training data.
Generative AI can be used to create content in response to a natural language prompt using chatbots like ChatGPT that are capable of human-seeming interactions. While GenAI has been developed over the past few year, it didn’t hit mainstream headlines until the release of ChatGPT by OpenAI on November 30, 2022. Within two months, ChatGPT had been accessed by over 100 million users, propelling AI into a whole new level of interest, investments, and hype. In addition, established companies and startups have been working hard to integrate generative AI capabilities with a variety of existing and new business applications.
What are the benefits and applications of generative AI? “The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case.” Companies should be realistic about the economic business value they’ll be able to achieve with GenAI projects in the near- and mid-term, given the accompanying risks that need to be carefully managed, such as the production of inaccurate or biased information .
“In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).” Gartner’s findings are consistent with those of a recent McKinsey report on “The economic potential of generative AI.” The study estimated that generative AI could add an additional $6.1 trillion to $7.9 trillion annually, increasing the overall impact of all artificial intelligence to the yearly global economy by $17.1 trillion to $25.6 trillion. Four business functions, — customer operations, marketing and sales, software engineering, and research and development — are likely to account for approximately 75% of the total annual value from generative AI use cases.
What are the risks of generative AI? The risks associated with GenAI are significant and rapidly evolving. These range from authentic-looking deep fakes, — images, videos or audio recordings that use AI algorithms to replace the person in the original with someone else, — to character defamation as a result of libelous statements.
Gartner lists a series of generative AI issues that need to be closely monitored:
- Lack of transparency. “Generative AI and ChatGPT models are unpredictable, and not even the companies behind them always understand everything about how they work.”
- Accuracy. “Generative AI systems sometimes produce inaccurate and fabricated answers.” We’re starting to see lawsuits against companies whose chatbots or search engines generate allegedely libelous statements.
- Bias. “You need policies or controls in place to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.”
- Intellectual property (IP) and copyright. “Users should assume that any data or queries they enter into the ChatGPT and its competitors will become public information, and we advise enterprises to put in place controls to avoid inadvertently exposing IP.” Writers, artists, actors, news organizations and others are increasingly fighting back against AI companies consuming their online content without consent.
- Cybersecurity and fraud. “Enterprises must prepare for malicious actors’ use of generative AI systems for cyber and fraud attacks, such as those that use deep fakes for social engineering of personnel, and ensure mitigating controls are put in place.”
- Sustainability. “Generative AI uses significant amounts of electricity. Choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on your sustainability goals.”
How will generative AI contribute business value? According to Gartner, generative AI contributes business value in three key categories: increased revenues, reduced costs, and improved risk management.
Revenue opportunities include using the technology to help create complex products significantly faster, such as new drugs, less toxic household cleaners, and new alloys. Generative AI could also help enterprises find new revenue channels for their existing products and services.
Cost reduction opportunities include the use of GenAI-based assistants to improve the productivity of workers in a variety of tasks, from drafting and editing documents to developing and testing software code. The partnership between generative AI assistants and workers can significantly improve their proficiency and greatly extend their range and competency across the board.
Risk mitigation opportunities include “Generative AI’s ability to analyze and provide broader and deeper visibility of data, such as customer transactions and potentially faulty software code, enhances pattern recognition and the ability to identify potential risks to the enterprise more quickly.”
Which industries are most impacted by generative AI? Gartner expects that GenAI will affect a wide variety of industries, — from pharmaceutical and medical to manufacturing and engineering, — by augmenting their core processes with AI models.
For example, “by 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. … In the manufacturing, automotive, aerospace and defense industries, generative design can create designs optimized to meet specific goals and constraints, such as performance, materials and manufacturing methods.”
What are the best practices for using generative AI? Gartner recommends that executive leaders should follow a number of tried and true best practices in their use of AI. These best practices are similar to those that have been used in the early stages of other transformative technologies, like the internet and World Wide Web in the 1990s:
- Start inside. “Before using generative AI to create customer- or other external-facing content, test extensively with internal stakeholders and employee use cases. You don’t want hallucinations to harm your business.”
- Prize transparency. “Be forthcoming with people, whether they be staff, customers or citizens, about the fact that they are interacting with a machine by clearly labeling any conversation multiple times throughout.”
- Do your due diligence. “Set up processes and guardrails to track biases and other issues of trustworthiness. Do so by validating results and continually testing for the model going off course.”
- Address privacy and security concerns. “Ensure that sensitive data is neither inputted nor derived. Confirm with the model provider that this data won’t be used for machine learning beyond your organization.”
Take it slow. “Keep functionality in beta for an extended period of time. This helps temper expectations for perfect results.”
What does Gartner predict for the future of generative AI use? “Generative AI is primed to make an increasingly strong impact on enterprises over the next five years”:
- By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.
- By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.
- By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps.
- By 2026, over 100 million humans will engage robocolleagues to contribute to their work.
- By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.
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