While generative AI applications like ChatGPT have received much public attention in the past year, experts are only beginning to examine their impact on business. In a recent Ernst and Young survey, most corporate leaders reported being worried that they were not doing enough to address and manage the consequences of AI.
Below, we look at emerging generative AI technologies, their evolution, and their potential effect on enterprise technology.
Emerging Tech Driven by Generative AI
Today's generative AIs are built on large language model (LLM) architectures. In other words, they're capable of learning and interpreting human speech. As a result, they can accept and extrapolate from more data than ever.
To harness their unique capabilities, AIs are springing to life in more and more industries as companies seek to leverage them.
Some examples of the global generative AI expansion cited by industry mavens TechTarget include:
- Self-Teaching, Specialized Models:
Prior AIs needed to be fed large amounts of structured data to generate insights. However, the next generative AIs can ingest and learn from the unstructured data that companies already own and find previously undetectable patterns. In addition, AIs will develop specializations in industry verticals, permitting them to understand technical language and deliver more profound insights.
- User Accessibility and Generative AI-Built Applications:
As generative AI applications expand, they will likely appeal to a broader range of users beyond the IT workforce. At the same time, newer applications built on existing LLM infrastructures will enable easier code development and web navigation.
- Open Source LLMs:
While popular LLM AIs like ChatGPT are primarily proprietary, other open-source AIs will also gain prominence, allowing for more significant operational and data control. However, these models will also require more IT expertise to manage, govern, and maintain.
- LLM Plugin Ecosystems:
For their part, OpenAI, the makers of ChatGPT, and others like them are offering support for plugins that can modify its LLM and enable it to specialize in specific tasks. Therefore, companies can take their existing processes and augment them with AI.
Effects on the Enterprise
Just as AI-related technology continues to grow and expand at an increasing pace, so do its potential effects on the enterprise and the people within it.
Consider some of the following downstream changes due to generative AI:
- A redirection in corporate training to AI:
As companies begin to see the potential of generative AI, they'll invest more in training so employees can leverage it for better performance and results.
- Redefining Meaning of Expertise:
The focus of human expertise is likely to change from mastering lower-level tasks like programming syntax to higher-level concepts. In addition, Gartner predicts a 1000% increase in the unionization of knowledge workers to combat job displacement in 2024.
- The Rise of New and Expansion of Existing Use Cases:
Growing at the same exponential pace of AI is the list of ways it's being leveraged. Some use cases include:
Research- In medicine, law, governance, and more. Generative AI will enable the analysis of larger datasets, whether from general sources or company-specific data.
Cybersecurity- Given the expanding attack surface of the average enterprise, AI is being used increasingly to aid cybersecurity teams in making better technology investments, assessing risk, and establishing KPIs.
AIOps- Whether within the IT organization or elsewhere, companies will use AI to improve operational efficiency.
- Growing Risks and Ethical Concerns:
Real concerns, however, have surfaced as the initial excitement over generative AI's capabilities fades and its use becomes more mainstream. Today's LLMs have occasionally hallucinated or generated false or biased results. Legal concerns have also arisen regarding data privacy and intellectual property protection. Often, these are cases in which current governance has yet to catch up to technological changes.
- Shadow AI and Enterprise Management:
Just as shadow IT has been a growing challenge for organizations, users across the enterprise will begin to use unsanctioned AI tools, presenting additional security and management challenges.
- Energy Usage:
Given its unprecedented demands on computing resources, generative AI will bring efficiency and sustainability concerns to the forefront for IT leaders. Better-performing yet more efficient GPUs and CPUs will be needed to power the servers running AI systems to enable enterprises to grow affordably and meet local power usage requirements.
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