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Forrester Research’s recently-released predictions report for artificial intelligence highlights what most have already observed: AI adoption has evolved from an emerging, nice-to-have trend to experiment with to a legitimate, must-do priority for enterprises.
Basically, get on board the AI train or be left behind.
The “get on board with AI now” message has been hammered home for several years, but this year’s stats do seem to point to a significant evolution: According to Forrester’s Data and Analytics Survey, 2022 [subscription required], 73% of data and analytics decision-makers are building AI technologies and 74% see a positive impact on their organizations from the use of AI.
No vertical industry is failing to find opportunities to implement AI, and companies at all maturity levels are transforming fundamental functions in the organization, the predictions report found, while in 2023 AI adoption will “continue to expand and be more creative, trustworthy and optimized.”
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Forrester put forth five evidence-based predictions for the use of enterprise AI in 2023, which were culled down from about 15-20, Rowan Curran, AI and ML analyst at Forrester, told VentureBeat.
“We narrowed it down to the predictions that we felt were likely to happen and had a good amount of evidence to stand behind,” he said.
With the evolution in transformer networks and pretrained models (particularly large language models like BERT and GPT-3), generative AI is fully part of today’s zeitgeist.
Forrester’s prediction report mentioned leading players like Baidu and Huawei, which have launched digital content services powered by computer vision (CV); startups like Synthesia and HourOne.ai, which are using AI to accelerate video content generation; and Taichi Graphics, which raised $50 million for CV-powered digital content creation. And, of course, popular text-to-image tools like DALL-E and Stable Diffusion are enabling content-creators of all types (even tech industry analysts) to quickly generate content.
Curran said that the pace of AI change is happening so fast, with such a broad adoption of large language models across different use cases, that this prediction already feels almost out of date.
“I should probably have scaled it upwards,” he said. “I think maybe I would have revised it to 10% of Fortune 500 workers will use these tools, because to me that speaks to the way this AI trend is evolving — I think it’s going to bubble up from below as much as it’s going to come down from above.”
He added that he could also have talked more about the broad set of use cases for content generation, from image generation for design ideation to the text side — generating marketing copy at scale or doing quick and easy summarization of complex textual topics.
The development of AI that writes code — which Forrester coined “TuringBots” in 2020 — was a huge 2022 trend that will only accelerate in 2023, said Curran.
“There’s been a lot of excitement in the smaller developer and open-source community for the past couple of years,” he explained. “But in the past year more enterprise-oriented companies have started to integrate [TuringBots] directly into broader developer tools, fine-tuning them into Codex, for example.”
According to the report, reinforcement learning and large language models have accelerated the development, accuracy and deployment of these products, which automatically generate clean code from requirements expressed in natural language. In 2023, Forrester expects tools like Amazon Code-Whisperer, Code Bot, GitHub Co-Pilot and Tabnine to take on more aspects of the software development lifecycle.
“People are developing TuringBots that are more focused on the infrastructure and connective tissue of applications, which can be a really repetitive task,” said Curran. “The adoption of TuringBots for all of these minutia-type tasks that are critical for enterprise applications, specifically when they need to hook into all sorts of different systems, is what’s really is going to cause the change here.”
With AI regulation mounting and demand for trust in AI rising, it likely comes as no surprise that one in four CIOs and CTOs will likely be leading AI governance in 2023. The Forrester report predicts that AI
governance will become a board-level topic, joining cybersecurity and compliance, which shows the significant necessity for risk mitigation and oversight.
“The warning is really looking at enterprises that recognize that they have to both understand and audit their AI capabilities, but also have an ongoing capability to understand whether they’re falling out of accuracy, whether they’re meeting business needs and have that business-to-technical communication flow,” he said. “I think that is what we’ll see a lot more of.”
The report highlighted Forrester data that shows 46% of data and analytics business and technology decision-makers seek out partners to implement AI critical to the business. It pointed out that Accenture, BCG, Deloitte, EY and McKinsey already offer auditing and executive training on AI governance.
Tech execs, the report said, “should embrace their new AI governance role and use the opportunity to
put ethical technology strategy into practice across the organization.”
“This one was ideated by my colleagues on our healthcare team,” said Curran. “They were able to identify that no-shows to appointments is one of the biggest problems in healthcare, a huge issue that can actually be attacked by artificial intelligence in a way that it never could be before, because there was so much complexity around the actual scheduling itself.”
Now, retail healthcare will use intelligent scheduling to chip away at the $150 billion problem of healthcare no-shows, the Forrester report predicts. For example, Walgreens is partnering with Nuance to schedule COVID-19 vaccine appointments 24/7, and Minute Clinic at CVS partnered with Google to enable same-day scheduling via Google Search.
In 2023, the report said, AI will “use insurance coverage, diagnosis, location, availability, and cancellation risk factors to optimize scheduling workflows. Innovative companies will use this data to fill costly gaps from last-minute cancellations — intelligent systems will reach out to waitlisted patients
based on the predicted likelihood to respond. Solving this problem will reduce the 20.6 day average wait to see a physician by 25%.”
“This just speaks to how we are seeing AI moving into the most fundamental and basic problems of some of these spaces, in addition to more flashy types of use cases,” said Curran. “The retail healthcare piece will be a game changer if effective — a 25% reduction in time to care would be huge.”
As conversational AI use cases expand across the organization, the Forrester report predicted that companies will drop the human-like pretense for virtual assistants, in order to improve trust, particularly in the B2B space.
Currently, Forrester found that 65% of B2B marketers use AI-powered virtual assistants to engage with customers and employees. Sometimes the virtual assistants pretend to be human, which organizations have found makes customers feel tricked. So companies will embrace transparency in identifying the AI assistants as virtual.
“For something like enterprise sales, when a customer is spending thousands or millions of dollars on a thing, you probably want to talk to a human throughout that process and not necessarily through a digital system,” said Curran. “So now, enterprises are just going to be fully transparent about whether this is a digital system or whether it’s a human system at every step along the way.”
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