Generative AI Takes the Spotlight in Gartner’s 2023 Hype Cycle

“What will be needed is investment to recreate that developer experience in private clouds — delivering the API-first, cloud-native, consumption experience that Gartner identifies,” he said. DEI leaders today are tasked with delivering more with less while operating in increasingly challenging economic and corporate environments. As CEOs and Boards focus on the bottom line and some employees push back against DEI initiatives, heads of DEI must become even stronger advocates for their functions and themselves. In this session, Jane Alancheril, Senior Director, Advisory, Gartner, shared how DEI leaders can think more like CFOs to manage resources and prioritize their own time and energy to maximize their functional and personal effectiveness. We are bringing you news and highlights from the Gartner ReimagineHR Conference, concluding today, in London, U.K. Below is a collection of the key announcements and insights coming out of the conference. While I firmly believe that generative AI has the potential to make us smarter, I also believe that we have to be smart about deploying it.

“It’s well understood that the majority of organizations today have very significant workloads that just don’t, and may never, work in the public cloud and need to live on-prem,” he continued. “What’s key though — and this applies to the challenge of delivering on DevX as well — is don’t equate that merely to deploying in public clouds,” he told TechNewsWorld. Vitorio Bretas, Director at Gartner, shared that organizations can reduce the percentage of managers at risk of failing by 43% if they successfully address the top predictors. Managers are at a higher risk of underperformance, which can negatively impact the talent outcomes of their teams. In this session, Vitorio Bretas, Director, Advisory, Gartner, shared how to spot the “early warning indicators” that can identify the managers most at risk. Jane Alancheril, Senior Director, Advisory at Gartner, shared lessons DEI leaders can learn from CFOs.

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However, retaining a focus on higher-level critical thinking is essential for individuals and institutions in the academic sector (figure 1). AI has the potential to be so disruptive that it’s essential that it is developed and used in a responsible way that minimizes its potential to cause harm. We already know some of the dangerous factors – bias, a lack of transparency, the potential to displace human jobs and our inability to say, with 100 percent certainty, that it’s never going to get out of control. In 2024, we will see continued focus on mitigating these problems and remaining vigilant for new ones. AI ethicists will be increasingly in demand as businesses seem to demonstrate that they are adhering to ethical standards and deploying appropriate safeguards. Generative AI is a growing use case for smartphones as assistants like ChatGPT, image generation, and other apps that rely on the technology become more common.

With millions of people around the world still working from home due to the COVID-19 pandemic, Gartner explained that the distributed enterprise will most likely replace the traditional office-centric organizations of past years. “Unlike automated or even autonomous systems, autonomic systems can dynamically modify their own algorithms without an external software update, enabling them to rapidly adapt Yakov Livshits to new conditions in the field, much like humans can,” Gartner explained. Gartner predicts generative AI and decision intelligence, which involve teaching predictive AI how to affect predicted outcomes, will reach mainstream adoption in two to five years. Master your role, transform your business and tap into an unsurpassed peer network through our world-leading virtual and in-person conferences.

No. 2: Generative AI in material science

The ban on calculators was a limiting factor in our development, not the cause of it. “If all the information in the world is at our fingertips, why will we need to remember anything? Rather than making us dumber, Google became such an essential part of our lives—including at work—that it became a verb. If this rapid movement and the disillusionment seem contradictory, that’s because the Hype Cycle isn’t an ascent from obscure to mainstream.

At the same time special apps are also being developed for diagnostic and therapy services that will help to recognize conditions such as depression or help children with autism. Despite the real and potential Yakov Livshits promise of generative AI applications in higher education, several risks remain. Across the world, faculty and institutions acknowledge that banning generative AI is a transient response to change.

Responses show many organizations not yet addressing potential risks from gen AI

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

IBM Watson technologies help integrate AI-powered experiences with the systems, processes, and people that run businesses without migrating your tech stack. It helps extract information and insights from existing text and other documents with Natural Language AI and Smart Document Understanding to accelerate and augment business decisions and processes. The use of AI to advance automation and enhance efficiency is another example of intelligent automation as a powerful tool for CIOs. Code generation, enterprise content management, marketing, and customer experience applications are some of the key areas for generative AI use cases in the enterprise, per IDC.

This makes it an ideal testbed for integrating AI into processes, automating the mundane in order to free up human time for issues that need a human touch. AI can be used to triage initial contact calls, generate personalized solutions to common problems, and generate reports and summaries of customer interactions. A Boston Consulting Group survey found that 95 percent of customer service leaders expect their customers will be served by AI bots at some point in the next three years. The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers.

But quantum computing – capable of massively speeding up certain calculation-heavy compute workloads – is increasingly being found to have applications in AI. Quantum algorithms process data using qubits that spookily exist in more than one state at a time, as opposed to traditional computer bits that can only ever be 1 or 0. This is one of the features that makes them far more efficient than classical algorithms for problems like optimization – determining the best combinations of many different variables – that are commonly tackled with machine learning. During 2024, I expect to see more progress in applying quantum computing in order to power ever larger and more complex neural networks and algorithms.

• The technology aids in customer segmentation, rapid creation of personalized content and further automation of customer journeys.
• Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.
• In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey.
• This has led to much debate about how generative AI will automate and replace humans, from writing code to creating content to detecting fraud.
• Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.

Most Hype Cycles have a few emerging technologies that end up being rated low or moderately beneficial; all of the technologies in the AI Hype Cycle were rated high or transformative. The benefit rating Yakov Livshits ranks how much of a positive impact the innovation could have across industries. All content on Gartner ThinkCast is owned by Gartner and cannot be repurposed or reproduced without Gartner’s consent.

Gartner also called out in its report technologies that enhance a software developer’s experience. DevX refers to all aspects of interactions between developers and the tools, platforms, processes, and people they work with to develop and deliver software products and services, the report explained. For example, the technology can draft marketing materials, optimize SEO and improve customer service by analyzing existing data, preferences and trends to produce engaging assets that resonate with the target audience.

Gartner recommended that data and analytics leaders focus on products that do not require team members to have extensive, specialized engineering or data science skills. Rather than fearing the looming changes, understanding how this technology can augment human efforts is key. This requires learning how to effectively prompt the technology, which will make humans increasingly valuable and in demand. Undoubtedly, roles will change, and some will disappear; however, new AI-driven jobs will emerge, including strategists, data analysts, content curators, training managers and ethicists.

For example, your request for a data-driven bar chart might be answered with alternative graphics the model suspects you could use. In theory at least, this will increase worker productivity, but it also challenges conventional thinking about the need for humans to take the lead on developing strategy. The models understand the pattern in the data fed to it and generate a new “item” out of it.