The 2023 Gartner IT Symposium/Xpo kicked off on October 16 with a focus on generative AI and how enterprises are leveraging the technology. TechRepublic attended a press-only virtual event on Monday where Gartner’s opening keynote speaker and special vice president analysts Mary Messario, Don Scheibenleif and Eric Bretenou spoke about the relationship between humans and machines. and gave a talk on how businesses can face the new AI era.
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How is the relationship between humans and machines changing?
Gartner analysts said the changing relationship between humans and machines is being driven by new generative AI innovations. When discussing this topic, Gartner analysts use the term machine broadly from a historical perspective. When we talk about new changes in the relationship between humans and machines, the term machine refers to automated systems and new AI technologies.
of 2024 Gartner CIO and Technology Executive Survey We found that 73% of CIOs say their companies will increase funding for artificial intelligence/machine learning in 2024. Additionally, 80% of CIOs reported that their organization plans to fully implement generative AI within three years.
“We think generative AI is the starting gun for that big change,” Schabenreif said during a virtual press conference. Schabenreif explained that the way humans interact with machines is moving in a new direction historically, as machines become more conversational and human-like.
From tools to teammates
Scheibenreif emphasized the changing human perception of machines. In the past, machines were viewed as tools, but now major companies and companies are engaging with them as teammates, Schabenleif said. Gartner predicts that by 2025, generative AI will be a workforce partner for 90% of companies worldwide.
“We’ve had a complicated history with (machines) for literally thousands of years,” Schabenreif said. However, Schabenreif says that this relationship has changed with the introduction of the World Wide Web, smartphones, and more recently ChatGPT and other generative AI chatbots.
“Machines have gone from being our tools to being our teammates,” Schabenreif said. “We have seen examples around the world of machines taking on different roles. This reinforces the idea that this is more than just a technology or business trend. It’s a change in the way we interact with machines.”
When the machine becomes the customer
Companies are also rethinking and transforming the human-machine relationship by creating systems, technologies, and machines that act as customers. According to Schabenreif, this trend is expected to accelerate further.
for example, tesla car You can self-diagnose and order parts if necessary. Similarly, industrial robots and the Industrial Internet of Things can monitor performance and automatically issue alerts or schedule maintenance tasks. Meanwhile, numerous smart home IoT devices can order groceries, cleaning supplies, and other household items based on residents’ needs.
Please refer to HP instant ink printerThis applies the same premise as the machine as a customer and can automatically order ink if the level is low, Schabenreif said. “HP is, in effect, actually manufacturing its own customers.”
“What if your best customers aren’t human? What changes does that make to your sales strategy, marketing approach, human resources approach?” Scheibenreif asked.
How to identify everyday and transformative AI opportunities
At the Gartner AI press conference, Mesaglio spoke about how companies can identify opportunities for AI in different areas.
“On the one hand, AI is being used every day to make things faster, more efficient, and better,” Mesaglio said. “Then we will have innovative AI as a partner in creativity, creating entirely new AI-enabled products, services, and even industries.”
There are internal and external opportunities for both everyday and game-changing AI. “This creates his four opportunities for companies to consider,” Mesario says. The four opportunities are external everyday AI, internal everyday AI, internal game-changing AI, and external game-changing AI.
Everyday AI in your company operates in back-office and back-end systems to drive decision-making, productivity, risk management, development, and many other areas. In contrast, external everyday AI is deployed by businesses into customer-facing systems. These advanced AI solutions are used by businesses to add value to their portfolios, differentiate themselves in competitive markets, and stay on top of trends.
“Then you have revolutionary AI,” Mesaglio said. She explained that innovative in-house AI is being applied to the core functions of the business to develop new ways to drive new results. External innovative AI will be provided to customers.
An example of external game-changing AI would be the use of AI in the development and production of products that use science, technology, and innovation to achieve a specific function or purpose. These products may include solutions using AI, machine learning, big data, and other advanced technologies.
By considering these four opportunity areas, Messario said companies can break through the AI hype and analyze where they want to invest and where they don’t.
AI beyond the “tyranny of the quarter”
Since the first public-generated AI models were deployed globally by major technology companies, the same companies (OpenAI, Microsoft, IBM, AWS, Google, and other top cloud vendors and AI startups) have released enterprise-generated AI models. started.
Many industries and companies from different sectors are rushing to adopt these enterprise AI solutions to reap the promised benefits. But Bretenou called on companies to tread carefully and not push boundaries.
“One of the biggest mistakes our clients have made with generative AI over the past nine months is thinking only about productivity gains,” Bretenou said. “So they’re looking at ways to eliminate a lot of positions within the organization, because that’s a good outlook at the end of the quarter.”
Bretenou explained that replacing workers with AI is a bad idea in the long term. As companies introduce new products and services or grow, they will need human workers.
“Therefore, there is a danger here that is happening in these organizations that are solely focused on increasing productivity,” Bretenou added. “We’re seeing this today. Many people are looking at the tyranny of this quarter.”
“We recognize that, too,” said Messario, who leads the executive leadership dynamics team in another part of Gartner. (Brethenoux heads Gartner’s Artificial Intelligence Governing Council, and Don Scheibenreif is a Gartner customer. He works in the Experience Research Group.) You’ll miss the bigger conversation. ”
Gartner experts believe that the key for businesses is to think, discuss, evaluate, and consider the kind of relationship they want to have with machines, and which business areas they want to enter and avoid. I agree that it is something to explore. In this way, AI projects need to be more intentional and consider risks and consequences, especially related to security, privacy, and compliance.
Gartner guarantees By 2026, organizations that operate with AI transparency, trust, and security will achieve a 50% improvement in AI model adoption, business goals, and user acceptance.
How to build a healthy relationship between humans and machines
During this virtual event, TechRepublic asked Gartner experts how business leaders can build healthy human-machine relationships and avoid the risks associated with implementing experimental technologies.
Messario replied, “I think the basic and most important mechanism that you should always use when exploring new and unexplored territory, different and unclear territory, is to use principles.”
Mr Messario warned companies to reconsider their principles as many organizations have them but they are not effective. He added that principles need to be specific, clear and aligned with business values, objectives and priorities.
“If you’re going to be the most customer-centric organization on the planet, the principle should be customer-centric,” Messario says. “If you’re looking to be the cheapest and most efficient in your operations, the principles have to be about that.”
According to Mesaglio, a more rigorous business mindset is achieved by aligning principles with business outcomes. This also determines which boundaries a company is willing or unwilling to cross, helping leaders better assess risks and threats and mitigate them.
Mesaglio added that leaders need to have conversations and participate in workshops and exercises to determine what their company is comfortable doing when it comes to AI and machines. Security and privacy are fundamental to this idea.
Bretenou highlighted the risks associated with unnecessarily pushing the adoption of AI and innovation, saying the technology can be difficult to manage and make business operations more complex.
“One of the principles is that digital is never an outcome,” Mesario says. “The results go beyond that.”
Who is responsible for ensuring a healthy relationship between humans and machines?
Schabenreif added that it is ultimately the CEO’s responsibility to ensure that the human-machine relationship within the company is healthy. “It’s the leaders of the business. They need to set the tone and help drive the value of their organizations and applications of AI.”
On the other hand, CIOs are well-positioned to lead their organizations in day-to-day applications of AI. Scheibenreif said all aspects related to the integration of generative AI technology should be under the purview of his CIO. Naturally, the leaders of the various departments also have clear roles and responsibilities. However, when it comes to the innovative AI space, Schabenreif said that ultimately the CIO is just one part of a larger team led by the CEO.