With talk of artificial intelligence in the enterprise moving from hype to implementation, Deloitte’s State of AI 5th Edition research report finds that 94% of business leaders agree that AI is critical to success over the next five years.
At the same time, one of the more surprising outcomes is that as AI deployments increase, outcomes are lagging Beena Ammanath, executive director of the global Deloitte AI Institute, told TechRepublic. Although 79% of respondents reported achieving full-scale deployment for three or more types of AI applications—up from 62% last year—the percentage of organizations in the underachiever category (high deployment/low outcomes) rose from 17% last year to 22% this year, the report said.
Proving AI’s value when it’s no longer the ‘shiny object’ and other challenges
This may be because survey respondents reported varying challenges depending on where they are at in their AI implementation. When starting new AI projects, the top challenge reported was proving AI’s business value (37%). This was followed by a lack of executive commitment (34%) and choosing the right AI technologies (33%).
At 29%, choosing the right AI technologies also made the list of the top three challenges in a follow-up question about both starting and scaling projects. Respondents cited insufficient funding for AI technologies and solutions (30%) and lack of technical skills (29%) as their two other main challenges.
SEE: Key insights that will help you make the most of AI (TechRepublic)
“As organizations attempt to scale up their AI projects over time, key impediments such as managing AI-related risks (50%), lack of executive buy-in (50%) and lack of maintenance or ongoing support (50%) push toward the top of the list,” the report said. “This emphasizes the resounding importance of clear leadership and focused investment that a successful AI transformation requires, reiterated by respondents.”
Further, it demonstrates the ongoing challenge of establishing the coordination and discipline needed to consistently fund initiatives after they have ceased to be the shiny object, the report observed.
“Much of building an AI-fueled organization requires discipline and focus to maintain systems and algorithms so that they can continue generating ongoing value instead of noise,” the report said.
That discipline and focus extend to understanding of all associated challenges that may not be obvious in the early stages of an AI initiative, according to the Deloitte report.
Reaping the results
Notably, 87% of respondents reported that they are now finding the length of the payback period to land within their expectations or faster, the report stated.
“While on the one hand, this indicates an increased understanding of implementation requirements, it could also suggest that the vision for AI may be too focused on cost savings and that the transformational opportunities that AI can offer, which often have less predictable timelines, are being overlooked or ignored.”
This is further underscored by the wanted outcomes respondents reported most frequently—reduced costs (78%). When organizations prioritize efficiency, more transformational outcomes, like revenue generation or business innovation, can fall by the wayside.
That said, some organizations have begun to find a path. Respondents from high-outcome organizations were significantly more likely to report revenue-generating results such as entering new markets or expanding services to new constituents, creating new products and programs or services, or enabling new business or service models.
Organizations that overcome the cited challenges will find that “rewards can be lucrative,” the report said.
Steps to take to improve AI outcomes
The report offered four actions it recommends leaders should consider helping improve the outcomes of their AI efforts.
Invest in culture and leadership
Leaders could do more to harness optimism for culture change, by establishing new ways of working, and to drive greater business results with AI.
“Leaders should embark on reinventing work to capitalize on the growing optimism and opportunity that their human workforce sees in AI,” Ammanath said. “People are still at the core of a business’s success, and AI can help unleash the power of a combined human and machine workplace.”
Respondents reported that agility and willingness to change combined with executive leadership around a vision for how AI will be used are the most important factors in the development of an AI-ready culture (42% and 40% reported this as extremely important, respectively), Ammanath added.
An organization’s ability to build and deploy AI ethically and at scale largely depends on how well it has redesigned operations to accommodate the unique demands of new technologies.
As part of this, the Deloitte research found that risks around lack of explainability and transparency in AI decisions, data privacy and consent management “all loom large as ethical risks that concern organizations.” Organizations often achieve better results when they adopt an ethical AI framework, the report said.
SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)
Orchestrate tech and talent
No longer should technology and talent acquisition be considered separate. Organizations should strategize their approaches to AI based on the skill sets they have available, whether derived from humans or pre-packaged solutions.
“Given that even the most advanced organizations are still early in their AI transformations, a majority of surveyed organizations reported they still prioritize bringing new AI talent into the business from outside, rather than retraining existing workers,” the report noted.
For more guidance on hiring an AI professional, the experts at TechRepublic Premium have a hiring kit for artificial intelligence architects that offers a framework for recruiting and hiring.
Action 4: Select use cases that can help accelerate value
Determining the value drivers for your business, depending on your sector and industry context will help organizations select the right use cases to fuel their AI journey.
“AI is fueling transformations across all industries, and many leaders have begun to unlock which use cases are driving the most value within their given context,” Ammanath said. “The important takeaway is to orchestrate a strategy of both near- and long-term differentiating applications of AI.
“Focusing on use cases that are too challenging or have very long-term or small benefits can reduce a company’s enthusiasm to invest more, stalling further innovation and slowing down AI transformational changes.”
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