Generative Artificial Intelligence’s rapid evolution holds the promise to transform enterprise operations and decision-making across many industries. Several emerging key generative AI (GAI) trends can profoundly impact automation, productivity, and human expertise, but harnessing GAI’s many opportunities will come with risks that will require enterprises to make complex choices and strategically adapt. Read this blog for valuable insights to prepare for this new frontier.
Developing Generative AI Trends and Innovations
The trends to watch in the near and mid-term:
- The move from general to specialized models – As generative AI moves into specific industries and domains, more examples of models fine-tuned for specific purposes are expected to emerge. For instance, models could be specifically trained for banking, insurance, or Human Resources domains, with the capability to speak the language of these narrower fields
- Applications built on top of foundational GAI models – Apps built on top of large language models (LLMs) or conditioned LLMs to solve for specific needs will likely proliferate. Beyond ChatGPT, we already see early-stage web navigation concierges, code development assistants, and more. Initially, business-to-consumer (B2C) contexts will rise, but once the risks around GAI are solved, business-to-business (B2B) or business-to-employee (B2E) applications also will surge in activity
- Lower costs – GAI is still relatively expensive but prices already have dropped significantly. As infrastructure, hosting, training, and inference become more efficient and economies of scale improve, we expect further cost reductions
What the generative AI trends mean for enterprises
- Automation, productivity, and skills – Automation of tasks by GAI will boost employee productivity and also change the nature of expertise. This shift will require enterprises to rethink their talent agenda, workforce planning, learning and development (L&D) programs, and so on. Consider the example of an entry-level developer. With the benefits of GAI, the traditional “skill” of knowing a particular syntax for a specific language will become much less important. As a result, the bar of “valuable” human expertise will be raised. Enterprises need to account for these changes by rebuilding skill taxonomies and subsequently reassessing talent planning
- Focus on enterprise data strategy – The true power of GAI comes into play once enterprises go beyond the low-hanging fruit of using it to generate generic outputs, like text, images, or other media. For instance, we could envision a world where GAI creates appropriate business or IT workflows, creates complex documents from scratch, or generates marketing collateral tailored to a company. Getting to these use cases will require seamless access to enterprise data, regardless of the approach (whether specialized models built from scratch, fine-tuning, or in-context learning). While GAI will unlock the power of this data, enterprises will need to surface it for use. The enterprise data journey is not new, but GAI will require a renewed focus and potentially more investments to advance it
- Competition, disruption, and lowered barriers to entry – As GAI enables significant automation, organizations can do more with less. With lower costs, fundamentally new business models will become more feasible in multiple domains. Similar to how digital banks, built from the ground up, started nipping at the heels of established brick-and-mortar ones, this technology can potentially give birth to new contenders. One possible scenario to imagine is a new video game company creating complex video games relying heavily on GAI with a dash of human ingenuity. Similarly, GAI has the potential to disrupt stock media, customer service, entertainment, and other industries.
Enterprises may face difficult future choices, including making massive pivots, cannibalizing existing revenue streams, etc. While these decisions will naturally be difficult, enterprises must be willing to make hard calls to rapidly evolve and stave off existential threats further down the line.
However, there is no need to press the panic button yet. By investing in leadership education, keeping on top of developments, being open to innovations, and investing in home-grown and external GAI solutions, enterprises can position themselves well for when the time comes to make those hard choices
But before putting the horse before the cart, the many primary risks around GAI need to be addressed for broad-based enterprise adoption. These include regulatory concerns (including intellectual property), data and privacy, explainability (to some extent, at least), and others. Based on early trends, at least partial workarounds or mitigation mechanisms will be developed, in the short-term.
Everest Group provides insights and guidance on the risks, use cases, pricing, and implementation strategies to best position enterprises across industries for GAI adoption success. To learn more about Everest Group’s generative AI research or to discuss generative AI trends, reach out to Anil Vijayan.
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