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The human-machine relationship is dynamic and evolving. Generative AI, in particular, is set to completely transform enterprise processes, decision-making, strategy and other elements that have yet to be considered.
For this reason, AI adoption should no longer be considered an IT initiative, but an enterprise initiative. Furthermore, to keep pace and take full advantage, executives must prioritize their AI ambitions and AI-ready scenarios for the next 12 to 24 months.
“Generative AI is not just a technology or business trend — it is a profound shift in how humans and machines interact,” Gartner distinguished VP analyst Mary Mesaglio said in an opening keynote. “We are moving from what machines can do for us to what machines can be for us.”
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The year generative AI becomes democratized
Nearly three-quarters (73%) of CIOs polled by Gartner said their enterprise will increase funding for AI/ML in 2024. Similarly, 80% said their organizations are planning on full gen AI adoption within the next three years.
This strategizing, along with the confluence of massively pretrained models, cloud computing and open source, will make 2024 the year that gen AI becomes democratized. Boldly, Gartner predicts that by 2025, the technology will be a workforce partner for 90% of organizations globally.
In turn, this will lead to the need for AI Trust, Risk and Security Management (TRiSM), which will provide tooling for ModelOps, proactive data protection, AI-specific security, monitoring for data and model drift and risk controls for both inputs and outputs, according to Gartner.
The firm also predicts a rise in machine customers (‘custobots’) that can autonomously negotiate and purchase goods and services. In fact, by 2028, 15 billion connected products will have the potential to behave as customers, and this will eventually become more significant than the arrival of digital commerce, Gartner asserts.
As Gartner distinguished VP analyst Don Scheibenreif posited in a virtual press briefing: “What happens when your best customers are not human?” Enterprises must be thinking about how that will impact their sales, marketing, HR and other efforts.
The coming year will also bring an increase in AI-augmented development; continuous threat exposure management; sustainable technology; platform engineering; and industry cloud platforms that address specific outcomes by combining SaaS, PaaS and IaaS.
Everyday AI, game-changing AI
There are two emerging types of AI in enterprise, Mesaglio said in the virtual press session: everyday AI and game-changing AI.
“Everyday AI is your productivity partner,” she said. “It enables workers to do what they already do faster and more efficiently.”
Ultimately, though, it will go from “dazzling to ordinary with outrageous speed,” she said. Everyone will have access to the same tools, so there will be no sustainable competitive advantage — meaning that everyday AI is the new table stakes.
Game-changing AI, meanwhile, is a “creativity partner,” said Mesaglio. It doesn’t just make people faster or better, it creates new results, products and services, “or it creates new ways to create new results.”
“With game-changing AI, machines will disrupt business models and entire industries,” she said.
Establishing AI ambition, readiness
In defining their ambitions with AI, CIOs and other members of the C-suite should examine opportunities and risks in the back office, the front office, new products and services and new core capabilities, according to Scheibenreif.
In moving towards AI-readiness, enterprises should establish “lighthouse principles” that align with organizational values, he advised — and the CEO should set the tone in this area.
“They should help drive the values for the organization,” he said, “and the application of AI and the human-machine relationship should emanate from those values.”
Another critical element is to make AI data-ready — meaning it is secure, enriched, fair, accurate and governed by lighthouse principles. Finally, enterprises should implement AI-ready security, preparing themselves for new attack vectors and creating an acceptable use policy.
Ultimately, Scheibenreif pointed out that “generative AI is not everything, there’s a whole bunch of technologies that are connected to it.”
As humans work more closely with those technologies, we’ll gain a better understanding of “how we interact with machines and what they can do for us,” he said.
Don’t just focus on the ‘tyranny of the quarter’
In implementing new technologies, enterprises can tend to be a bit short-sighted — take the frenzied race to digital transformation over the last few years, for example.
“Organizations were saying ‘We just want to be digital,’” said Mesaglio. “Digital is never an outcome. It’s only a means to an outcome. The outcome is something that’s working.”
She emphasized the importance of being intentional and having meaningful conversations about the kinds of relationships people want to have with machines.
“Yes, there are ROI considerations,” she said. “Yes, there are productivity considerations. Yes, there are technological considerations. How do we make stuff work together?”
Many enterprises make mistakes in looking exclusively at productivity gains and “focusing on the tyranny of the quarter,” agreed Gartner distinguished VP analyst Erick Brethenoux.
“We call that within boundaries,” he said.
Innovators push and break boundaries when they explore and build new, innovative products and services. What he called “the fringe” is where breakthroughs are made.
“And 3% [of organizations] will be dedicated to that,” he said. “And it’s fun to do.”
The rise of decision intelligence
The new wave of AI application within the enterprise is what Brethenoux called decision intelligence. And in order to aid strategic decision-making that is actionable and explainable, machines need to interact properly and efficiently.
While anthropomorphism can often lead to fear and skepticism, in this case the humanizing of machines can be helpful, he contended. While machines don’t have sentience — and that they will “get very, very, very, very close but won’t reach singularity” — an anthropomorphic interface can help us better relate to them.
“It has a human-like voice, it can answer my questions, it can interact with me,” he said. “There’s a double-sided thing to be very careful not to push it too far, but at the same time exploit it to allow that direct interaction.”
The art of the question
As gen AI becomes ever more pervasive throughout enterprise, prompt engineering will be a critical skill, Brethenoux noted.
“Engineering is a very important part of what’s coming,” he said.
He pointed out that “answers are less important than questions,” and that humans must know how to correctly question technology so that it provides useful answers.
“So the way you ask questions is important,” he said. And it’s typically not a technical question — it’s more often a business question or a process question, which requires both technology and content and domain expertise.
This doesn’t necessarily necessitate new hires, he emphasized. Enterprise leaders should look at their existing technology and business experts and invest in upskilling them.
“You already have technology experts,” he said, “you have people working together, they know your business problems.”
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