Markets and Trends

Generative AI: The Path to Impact

The Key Activities, Core Technologies and Emerging Use Cases
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IDC believes that the tech industry is at a seminal moment. Never have we seen a technology emerge with this much executive support, clearly defined business outcomes, and rapid adoption. In eight short months, Generative AI has simultaneously captured the attention, imagination, and concern of most tech and business leaders across the world. However, as Generative AI becomes front and center of conversations in the technology industry and beyond, the underlying question being asked by the market is: how do organizations accelerate their journey to deliver business value?

…the ambiguities over the authorship and copyright of AI-generated content are creating question marks around intellectual property management and ownership. All these risks need to be incorporated into a well-orchestrated trust and oversight program to ensure that these technologies can be deployed in a sustainable manner.

Philip Carter – Group Vice President, Worldwide Thought Leadership Research

To help answer this question, IDC has developed a framework highlighting the path to business impact. This framework helps organizations work through the key activities that need to be put in place, provides an explanation of the core technologies required, and proposes how organizations should be thinking about the new use cases to accelerate their own path to business impact for Generative AI technologies.

Key Activities

Before any of the core technologies are explored, IDC believes that the following set of key activities needs to be put in place:

  • A Responsible AI Policy that includes defined principles around fairness, transparency and accountability relating to the data that is being used to train models as well as the usage of the results. This activity should also include a methodology to provide explainability of any generative AI model output with clear transparency on roles and responsibilities of developers, users and any stakeholder involved with these initiatives.
  • Strategy and Roadmap with a set of defined and prioritized use cases to align the organization on the key areas that will most likely deliver the maximum business impact in the short, medium and longer term.
  • Intelligence Architecture to manage the lifecycle and governance of data, models and business context for every use case. This should also include protocols around data privacy, security, intellectual property (IP) protection.
  • Reskilling and Training to create a skills map for core AI technologies, adjacent AI tech as well as broader tech and business capabilities to deploy Generative AI at scale across the organization.  This activity should also include a training program personalized for key roles and an organizational readiness assessment to ensure that a change management program is incorporated.

Core Technologies

Once the foundational activities are in place, it is critical to develop a clear understanding of the core Generative AI technologies. At the center of this are the generative foundation models – including the well-known large language models (LLMs). The ability for these models to be trained on extraordinarily large amounts of data (primarily semi-structured and unstructured content) and then generate new content based on previously created data in response to prompts is the real game changer in the market. And we are not just talking about text (which is the basis for ChatGPT); it’s also about generating and managing images, videos, structures (e.g. DNA), audio and software code. The model lifecycle including ingesting, training, tuning, inferring, and running these models is hugely important, and will determine their quality over time. From the changing dynamics in the platforms and infrastructure down to the shift from CPUs to GPUs at the semiconductor level really highlight how this transformative set of technologies is impacting every part of the technology stack. The way these models are being infused in custom applications, generic enterprise applications, and other software development platforms are critical ‘sub-paths’ to impact that also need to be explored.

The Emerging Use Cases

IDC defines a use case as a business funded initiative enabled by technology that delivers a measurable outcome. There are three broad types of Generative AI use cases that need to be assessed:

  • Industry-specific – these use cases will generally require more custom work (and in some cases may even require building your own generative AI model). Examples include generative drug discovery in life sciences, or generative material design for manufacturing.
  • Business Function – these use cases will tend to integrate a model (or multiple models) with corporate data for a specific function (e.g. marketing, sales, procurement etc).  Many organizations are testing these types of use cases but are concerned about IP leakage and data governance.
  • Productivity – these are basic use cases such as summarizing a report, generating a job description or code generation in Java. This functionality is being infused into existing applications (E.g. Co-Pilot for Microsoft, or Duet AI for Google).

There are a mix of internal and externally facing use cases – each with their own level of potential risk and business impact which needs to be incorporated into a use case prioritization framework.

Trust & Oversight

Finally, there are numerous well-founded concerns around ethics, regulatory compliance, and governance associated with generative AI.  Due to its ability to generate fake code, data and images closely resembling the real thing, generative AI is likely to increase identity theft, fraud, and counterfeiting cases. The models are also vulnerable and will likely be a source of attack and manipulation and often generate hallucinations. Additionally, the ambiguities over the authorship and copyright of AI-generated content are creating question marks around intellectual property management and ownership. All these risks need to be incorporated into a well-orchestrated trust and oversight program to ensure that these technologies can be deployed in a sustainable manner.

As the industry moves forward with this fundamental transition, IDC believes that every CEO will need to have an AI strategy – and generative AI is the trigger. It is best to get started quickly; we are hopeful that this framework will help every organization develop their own ‘path to impact’.

If your organization is interested in partnering with IDC to better understand how Generative AI will impact the markets most critical to your success contact us.

We also recommend you take advantage of these recent resources from our thought leaders and tech market experts:

IDC Webinar: Unlocking Business Success With Generative AI

IDC Blog: Generative AI: Mitigating Data Security and Privacy Risks

IDC eBook: Unlocking the Power of Generative AI

IDC Blog: Can ChatGPT Transform the Customer Experience?

IDC Blog: No Turning Back: AI Everywhere Causes a Seminal Shift in the Tech Market

Philip Carter is Group Vice President, European Chief Analyst and WW C-Suite Tech Research lead. His global responsibilities focus on creating research that assesses tech spending and buyer preferences across the C-Suite, with a focus on business leadership as it relates to technology objectives, priorities, programs and investments. As Chief Analyst for Europe, he continues to drive innovation and thought leadership in new research topics linked to digital transformation, C-Suite dynamics and the transformation of technology business models in the region.