Generative AI is more trendy than ever.
This year, AI research received Nobel Prizes and the world’s largest technology companies integrated AI into as many products as possible. The American government Promoted AI as an engine for creating a clean energy economy and a strategic pillar of federal spending. But what’s next for 2025?
The trend in generative AI in the final months of 2024 indicates a greater desire for adoption from technology companies. At the same time, results on whether AI products and processes generate ROI for enterprise software buyers are mixed. While it’s difficult to predict how AI will continue to shape the tech industry, experts offer predictions based on current trends.
Respondents to a IEEE study in September, AI was ranked among the top three technology areas that will be most critical in 2025 58% of the time. Conversely, almost all respondents (91%) agree that 2025 will see “a generative reckoning with AI” regarding what the technology can or should do. Expectations for generative AI are high, but the success of projects that leverage it remains uncertain.
1. AI agents will be the next buzzword
According to my research and observations, the use of AI agents will increase in 2025.
AI agents are semi-autonomous Generative AI which can chain together or interact with applications to execute instructions in an unstructured environment. For example, Salesforce uses AI agents to call prospects. As with generative AI, the definition of an agent’s capabilities is unclear. IBM defines it as an AI capable of reasoning about complex problems, such as OpenAI o1. However, not all products touted as AI agents can reason this way.
Whatever their capabilities, AI agents and their use cases will likely be at the forefront of generative AI marketing in 2025. AI “agents” could be the next stage of evolution for this year’s AI “co-pilots”. AI agents could spend time completing multi-step tasks independently while their human counterpart attends to another task.
2. AI will both help and harm security teams
Cybersecurity attackers and defenders will continue to leverage AI in 2025. 2024 has already seen the proliferation of generative AI security products. These products can write code, detect threats, answer tricky questions, or serve as a “rubber duck” for brainstorming.
But generative AI can present inaccurate information. Security professionals can spend as much time double-checking the result as if they had done the work themselves. Failure to review this information may lead to broken code and even more security issues.
“As AI tools like ChatGPT and Google Gemini become deeply integrated into business operations, the risk of accidental data exposure skyrockets with new data privacy challenges,” said Jeremy Fuchs, evangelist in cybersecurity at Check Point Software Technologies, in an email to TechRepublic. “In 2025, organizations must act quickly to implement strong controls and governance over the use of AI, to ensure that the benefits of these technologies do not come at the expense of privacy and security data. »
Generative AI models are susceptible to malicious actors like any other software, particularly through jailbreak attacks.
“The growing role of AI in cybercrime is undeniable,” Fuchs explained. “By 2025, AI will increase not only the scale of attacks, but also their sophistication. Phishing attacks will be harder to detect as AI continually learns and adapts.
Generative AI can make conventional methods of identifying phishing emails (bad grammar or unexpected messages) obsolete. Keeping misinformation safe will become more important as AI-generated videos, audios, and texts proliferate. As a result, security teams must adapt to both the use and defending against generative AI — just as they have adapted to other significant changes in business technology, such as large-scale migration to the cloud.
3. Companies will evaluate whether AI generates ROI
“The pendulum has swung from ‘AI innovation at all costs’ to a resounding imperative to prove ROI in boardrooms around the world,” said Uzi Dvir, the company’s Global CIO digital adoption platform WalkMe, in an email. “Similarly, employees are wondering if it’s worth spending the time and effort to understand how to use these new technologies for their specific roles. »
Organizations struggle to determine whether generative AI adds value and in which use cases it can make the biggest difference. Organizations adopting AI often face high costs and unclear objectives. It can be difficult to quantify the benefits of using generative AI, where those benefits manifest, and what to compare them to.
This challenge is a side effect of integrating generative AI into many other applications. Some decision makers are therefore wondering whether generative AI add-ons actually increase the value of these applications. AI tiers can be expensive, and over the next year, more companies are expected to rigorously test – and sometimes reject – features that don’t deliver results.
Many companies integrating generative AI at scale are seeing success. To his Third Quarter Earnings CallGoogle attributed this result to its AI infrastructure and products such as AI Insights. However, Meta reported that AI could significantly increase capital expenditureeven if the number of users decreases.
SEE: Google Cloud previews its sixth generation of AI Accelerator Trill.
4. AI will have a major impact on scientific research
In addition to impacting business productivity, contemporary AI has seen significant advancements in science.
Four of the 2024 Nobel Prize winners used AI:
- Demis Hassabis and John Jumper from Google DeepMind won the Nobel Prize in Chemistry to predict protein structure with AlphaFold2.
- John J. Hopfield and Geoffrey Hinton won the Nobel Prize in Physics for their decades-long work on the development of neural networks.
The White House held a summit on October 31 and November 1 on the use of AI in life scienceshighlighting how AI is enabling solutions to complex challenges in a way that impacts the world. This trend is expected to continue into the next year as generative AI models develop and mature.
5. Environmental tools created with AI will not offset its energy balance
Energy efficiency is another buzzword in the AI space.
But for every use case in which AI can help predict weather conditions or optimize energy consumption, there is another story about the environmental cost of building the system. data centers necessary to run generative AI. Such construction requires enormous amounts of electricity and water – and rising global temperatures are only making the problem worse. A balance is unlikely to be achieved in this large-scale problem.
For businesses, however, expect to see companies touting dubious and real claims of energy savings and environmental friendliness around AI. Consider resource usage related to your organization’s AI strategy.
What are the most popular generative AI products?
The best known generative AI products are:
- ChatGPTthe OpenAI chatbot
- Google Gemini
- Microsoft Co-pilot
- GPT-4the big language model behind ChatGPT
- DALL-E 3, an image generator
What is the most advanced generative AI?
Various tests have been proposed as potential criteria for determining the most advanced generative AI. Some organizations evaluate their models based on human education criteria, such as the International Mathematics Olympiad or Codeforce competitions.
Other assessments, such as Measuring Mass Multitasking Language Understanding, were explicitly created for generative AI. Google’s Gemini Ultra, China Mobile’s Jiutian and OpenAI’s GPT-4o are at the top of the rankings. MMLU Ranking Today.
