The technology industry is currently undergoing a major transformation. The combination of executive and board-level attention, well-defined outcomes, and speed of adoption makes generative AI unlike anything you’ve seen before.
This blog sheds light on the rapid rise of Generative AI (GenAI), its implications for tech companies, and fundamental questions about AI technology.
GenAI – A breakthrough moment in technology
In just seven months, GenAI has simultaneously captured the attention, imagination, and fear of technology and business leaders around the world.
- Note. Executives can easily understand how this technology impacts productivity levels and profits. The Brookings Institution predicts that GenAI will increase productivity and output by 18% over the next decade.
- imagination. GenAI has a wide range of applications, from horizontal use cases such as software development and marketing content creation to industry-specific use cases such as drug discovery and manufacturing design. The business benefits of use cases are obvious, and companies don’t wait until a business case is developed before they start experimenting. According to IDC research, knowledge management, marketing, and code generation are the top use cases being considered.
- anxiety. Executives recognize that this technology can quickly disrupt their business models. 20 years for the cloud to make up 50% of his core IT spending and 10 years for him to become a digital business accelerates the timeframe it will take for enterprises to implement generative AI use cases at scale. It will look very slow compared to what is done. . Well-founded concerns about ethics, regulatory compliance, and governance must also be incorporated into this new business model.
hide out of sight
How did a technology with such a big impact sneak up on most business leaders? The foundational elements have been developed over the last decade.
- Era of Multiplexing Innovation. What IDC calls the “era of multiple innovations” was driven primarily by cloud, mobility, and the Internet. Low-cost semiconductors and virtualization enabled the cloud, making computing flexible and abundant. Mobility has made computing ubiquitous. And the Internet has made the cost of distributing those computing bits nearly zero.
- Platform and Community. With a rich, ubiquitous and resilient infrastructure in place, platforms, communities and digital ecosystems have emerged. These platforms have sparked large-scale data integration processes and the birth of the Transformer model architecture, which enables the creation of underlying artificial intelligence models, including large-scale language models (LLMs).
- Era of AI Everywhere. Generative AI utilizes unsupervised and semi-supervised algorithms to generate content from previously created content, such as text, audio, video, images, code, etc. Trigger technology ushering in the era of AI. . This new era includes a shift from narrow AI to broad AI, completely changing how we relate to data and how we extract value from both structured and unstructured data.
Generative AI ushers in this new era as it dramatically reduces the time and cost of developing solutions for a wide range of use cases related to automation and intelligence. The rapid adoption of generative AI moves AI from an emerging software segment in the stack to a key technology central to platform transitions. The market generally assumes that this kind of platform migration requires a hardware migration, similar to a mainframe to client/server or client/server to cloud migration. But IDC thinks this time will be different. This platform migration puts more emphasis on data. Now let’s talk about how we use the data. input (for basic model training, fine-tuning, inference) and as a business result (As part of developing new use cases).
GenAI and the market disruption of the technology industry
As generative AI impacts most technology markets, from semiconductors to professional services, technology suppliers are rapidly revising their product roadmaps and rethinking their business, pricing and customer service models.
infrastructure. Today, much of the value is captured by semiconductor vendors, especially his NVIDIA, as training underlying models and running inference workloads require massive GPUs. Semiconductor providers need chips specifically designed for AI workloads, creating opportunities for new challengers. Training AI models will also drive investment in storage and networking, and the cost of on-premises-only training of the underlying models will put public and hybrid cloud providers in a strong position to capture share.
software. In the medium term, well-established platform and application vendors will benefit if they can pivot their products and business models fast enough. You need to determine which generative AI use cases can directly support monetization and which are important to implement from a defensive perspective. For example, generative AI could transform the way we interact with enterprise software. This is potentially the biggest change in his UX design since point-and-click, preparing for startup disruption of GenAI native applications.
As many of the costs associated with managing generative AI models in terms of scale, security, and privacy will likely fall on the shoulders of software providers, the following key decisions are being evaluated to protect their interests:
- Should you train your own underlying model or partner with a model provider?
- What is the new pricing model for supporting Generative AI capabilities?
- Should grounding be included in the SLA for some use cases, and if so should an additional level of support be added to address context and data drift?
- Will access to customer data to train models be part of a new set of licensing terms?
- Should compensation be provided for assets generated by AI?
service. While service companies are busy helping their clients identify use cases for GenAI, they are also looking at how GenAI will affect demand for their services in the long term, as well as software development, accounting and legal services. We are investigating how the delivery model for is automated. Services companies are increasingly bringing their own AI software platforms into their operations, blurring the line between software and services.
security and trust. Generative AI’s ability to generate fake code, data, and images that closely resemble the real thing can lead to an increase in identity theft, fraud, and counterfeiting incidents. LLM is also vulnerable and subject to attacks and manipulation. Security vendors have a ripe opportunity to develop new solutions to address these new challenges.
new market. Of course, any disruptive technology creates new technology markets. Startups are already emerging that provide tools to personalize models, provide model contextualization, speed up LLM training, and tune the process. There is a huge opportunity for software companies to respond to the current market. That might mean offering full-stack translation services rather than translation software.
While there are many unknowns facing the tech industry, what is clear is that fundamental questions about generative AI and how it will drive future business models need to be answered immediately. That’s it.
If your organization is interested in partnering with IDC to better understand how generative AI impacts the markets most critical to its success, please contact us.
We also encourage you to take advantage of the following up-to-date resources provided by our thought leaders and technology market experts.
IDC Webinar: Enabling Business Success with Generative AI
IDC Blog: Generative AI: Reducing Data Security and Privacy Risks
IDC eBook: Unlocking the Power of Generative AI
IDC Blog: Can ChatGPT Transform Your Customer Experience?
