What does ChatGPT, the “fastest growing consumer app in history,” mean for the future of work? More broadly, will generative AI soon graduate from cutting-edge consumer entertainment to become an important business application and a new basis for competitive advantage? And are enterprises ready for AI, any kind of AI?
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Forrester just released a report on generative AI that tells its enterprise customers not to ignore or minimize its impact. Enterprises should start experimenting with generative AI now, Forrester recommends, focusing on existing processes that can be improved by technologies “that use massive corpora of data, including large language patterns, to generate new content (p .eg text, video, images, audio, code)”
It would be “a costly mistake,” says Forrester, to ignore the potential of generative AI to enable content production at scale, to accelerate the speed and accuracy of data science practices and application development, to produce synthetic data for AI and machine training. learning models and to provide new protection opportunities for security professionals. In short, generative AI presents an opportunity to augment and even automate existing work processes in IT, marketing, customer service and other business functions.
ChatGPT became publicly available on November 30, 2022, and given the attention the PR stunt has received, it’s safe to say that the era before that date will be labeled BG (Pre Generative AI). We are now living in the new, exciting and scary era of GA (generative AI), where executive FOMO can lead to embarrassing public failures (like at Google, which started paying attention to GA already in 2017 , losing $100 billion in market value in one day).
Are enterprises ready for the new era, the pressures to do something about generative AI, even just careful experimentation, as Forrester recommends?
We can understand the state of AI in the enterprise by looking at recent surveys of business and IT leaders reporting on their current experiences with AI. The surveys—by Deloitte, cnvrg.io, Run:ai, and LXT—were conducted over the six months just before the advent of the GA era, so they reflect what respondents knew about “generic AI,” not necessarily generative AI.
Perceptions of AI are certainly positive in the business world. 94% (Deloitte) say AI is critical to success over the next five years and 89% (cnvrg.io) are seeing the benefits of their AI solutions. In 48% of organizations, “AI is in production, or already part of the business DNA” (LXT). 91% of companies are planning to increase their GPU capacity or other AI infrastructure by an average of 23% in the next 12 months according to the Run:ai survey, which concludes that “despite the uncertain economic climate, companies are still investing in AI because of the potential and value they see in it.”
According to the Deloitte survey, 79% say they have fully deployed three or more AI applications, up from 62% a year ago, with the top applications being cloud pricing optimization, voice assistants, chatbots and conversational AI, maintenance predictive and time/reliability. optimization. LXT found that Natural Language Processing (NLP) and speech/voice recognition solutions are the most prevalent AI applications, followed by predictive analytics and conversational AI.
But the challenges are many. Only 37% (Run:ai) of AI designs make it to production and 46% (LXT) of all AI projects fail to achieve their goals. Deloitte found a 29% year-over-year increase in the number of respondents who self-identified as “unreachable” and the top challenges related to scaling were AI-related risk management (50%), lack of executive engagement (50% ), lack of maintenance and support after launch (50%). 57% (cnvrg.io) reported low AI maturity with fewer than 4 models running in production and only 28% (Run:ai) reported having timely and sufficient access to on-demand computing power.
Challenges abound with AI deployment in general, but when it comes to generative AI, businesses face a “maze of problems,” according to Forrester: Generating coherent nonsense; recreating prejudices; vulnerability to new security challenges and attacks; trust, reliability, copyright and intellectual property issues. “Any fair discussion of the value of adopting generative AI,” says Forrester, “must acknowledge its significant costs. Training and retraining models takes time and money, and the GPUs required to run these workloads jobs remain expensive.”
So what’s a business leader to do? What is the right response to the pressures of “missing something new that could be a very costly mistake”?
As is always the case with the latest and greatest enterprise technologies, tools and techniques, the answer to “what to do?” it boils down to one word: Learn. Study what your peers have been doing in recent years with generic AI. A good starting point is the newly published one All in AI: How smart companies win big with artificial intelligence. Tom Davenport and Nitin Mittal profile companies (outside of Silicon Valley) that “are making big, smart bets that this technology will lead to big business improvements, and they already have evidence that those bets are paying off.”
Another kind of lesson is scrutinizing the landscape of what’s on offer (Sequoia Capital counts 109 AI-generating startups and CB Insights lists 250 in 45 categories). Like the hundreds, perhaps thousands, of startups that added “AI” to their profile over the past decade, it’s a safe bet that by the end of this year many more will be claiming “generative Artificial Intelligence” as their bread and butter. What’s important is their proven expertise in what matters to your company and your customers.
The startup most relevant to you may not even claim the mantle of “generative AI,” but it has demonstrated its benefits and what it can do for your business in recent years. One example is Anyword, a startup that predicts the audience your content (eg ad copy) will resonate with and how well it will perform. It provides a predictive performance score based on its analysis of millions of pieces of copy in a way that correlates conversion rate, audience profile, and message style and content. It has done this for publishers since 2013 and, as of 2021, for every marketer.
Most importantly, remember that there is no magic, and that the men and women behind the curtain have been steadily advancing the state of “machine intelligence” since the first computers were called “giant brains” seventy-five years ago. “AI” is just another step in the evolution of modern computing and the continuation of already popular data-driven computing applications, e.g. machine learning and predictive analytics. “Generative AI” is just another step in the evolution of modern AI, ie deep learning or statistical analysis of very large volumes of data.
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