The rush is on to adopt generative AI. Image: Getty Images
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- Companies are rushing to adopt generative AI to enhance their productivity.
- But many AI solution providers don't do the work needed to make the technology work in specific use cases.
- A new report compiling the opinions of 1,600 machine learning experts and business leaders shows how to successfully adopt the technology.
Over the past several months, the hype about generative AI has flooded popular discourse. Many companies have already added generative AI to their business strategy with the understanding that if they don't adopt this technology, they will fall behind their peers. Consultancies and Fortune 500 companies are investing billions into the technology.
That is why we are excited to share the second edition of Scale Zeitgeist: AI Readiness Report, which looks past the hype surrounding generative AI to see what it really takes to adopt it in an enterprise. The report reflects the responses of over 1,600 machine learning practitioners and business leaders that work with AI, covering industries including insurance, financial services, logistics, retail and e-commerce and software. We included companies that are more advanced in their adoption of AI, and those that are just beginning to experiment.
We found that 70% of business leaders believe that AI is either critical or highly critical to their business in the next three years. And 72% are increasing investments in AI in 2023, across nearly every industry.
However, while most respondents (60%) are experimenting or plan on working with generative AI in the next year, only 21% have models in production. This reflects the amount of work that it actually takes to make generative AI work for enterprise use cases. These models are not just plug-and-play. Domain-specific business data needs to be carefully fed into these models using a process called fine-tuning, and the models need to be aligned with company policies and desired behaviour using a technique called reinforcement learning from human feedback (RLHF).
For most, there is a skills and technology gap to overcome to make generative AI enterprise-ready. One-third of respondents cite a need for more expertise, software and tools as their most significant challenge in implementing generative AI. As we mentioned earlier, most solution providers are also AI tourists and are taking advantage of the current hype to convince companies that they can help them implement the technology when they are unable to adopt it themselves.
Since most companies don't have the necessary resources or mandate to build generative models from scratch, they must rely on third parties. Of those companies that plan on working with generative models, the vast majority are looking to leverage open-source models (41%) or Cloud API models (37%), while very few are looking to build their own (22%). There are exceptions, but these are not the norm. Training models from scratch is expensive, time-consuming and requires deep machine learning expertise. But, by using existing base models that are fine-tuned with proprietary company data, you can get similar performance without building your own model.
When properly adopted, the impact of AI is overwhelmingly positive, with 89% of respondents benefitting from the ability to develop new products or services, 78% from improved customer experience, and 78% from enhanced functionality of existing products or services.
To give you a sense of how all industries will benefit from generative AI, here are a few specific use cases:
- Insurance companies are focused on AI for claims processing (51%), a major component of operational efficiency. Claims are often highly complicated, and generative AI excels at properly routing, summarizing and classifying these claims.
- Retail and e-commerce companies are focused on customer chatbots (61%), which have historically failed to live up to their promise of streamlining operations and providing a better user experience. However, the latest generative chatbots that are fine-tuned on company data provide engaging discussions and recommendations that dynamically respond to customer input.
- Financial services companies are building assistants for investment research (47%) that analyze financial statements, historical market data, and other proprietary data sources and provide detailed summaries, interactive charts, and even take action with plugins. These tools increase efficiency and effectiveness of investors by surfacing the most relevant trends and providing actionable insights to help increase returns.
Through our research and work building AI with customers, we expect to see the following trends over the next year:
- Increased investment in AI: As generative AI is now more capable and widely available, companies are quickly incorporating it into their operations. Seventy-two per cent of companies will significantly increase their investment in AI each year for the next three years.
- Growth in application of fine-tuned models with proprietary data: On their own, base generative models are valuable tools. Paired with a business's proprietary data, they become strong differentiators, improving customer experience, product development and profitability.
- Models will also become multimodal: Models will be able to consume and generate text, images and video, making them even more useful than they are today. Capable assistants can extend developer, data science and business analyst productivity.
How is the World Economic Forum ensuring the responsible use of technology?
Billions of dollars will be spent on generative AI in 2023. To optimize these investments, you need treat your proprietary data as a strategic advantage. The best way to unlock the power of your data is through fine-tuning and RLHF of base foundation models. You should start working with generative AI today, in any capacity, as your competitors are certainly doing so. Be wary of AI tourists and look for AI natives with real world experience. Most importantly, educate yourself about AI and read the full report today.
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The views expressed in this article are those of the author alone and not the World Economic Forum.
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