ChatGPT 4 will be released next week
10 March 2023
21 March 2023
The year 2022 was a watershed year for artificial intelligence (AI), with the release of several consumer-facing applications like ChatGPT, DALL.E, and Lensa. The common theme the use of Generative AI–a paradigm shift in the world of AI. While current generations of AI use pattern detection or rule-following to help analyze data and make predictions, the advent of transformer architectures has unlocked a new field: Generative Artificial Intelligence. Generative AI can mimic the human creative process by creating novel data similar to the kind it was trained on, elevating AI from enabler to (potentially) co-passenger. In fact, Gartner estimates that more than 10% of all data will be AI-generated by as early as 2025,1 heralding a new age, the Age of With.
Although early traction has been through consumer releases, which could be era-defining, Generative AI also has the potential to add contextual awareness and human-like decision-making to enterprise workflows, and could radically change how we do business. We may be only just beginning to see the impact of solutions like Google’s Contact Center AI (CCAI), which is designed to help enable natural language customer service interactions,2 and industry-specific solutions like BioNeMo from NVIDIA, which can accelerate pharmaceutical drug discovery.3 As such, Generative AI has attracted interest from traditional (e.g., Venture Capital (VC), Mergers & Acquisitions (M&A)) and emerging (e.g., ecosystem partnerships) sources. In 2022 alone, venture capital firms invested more than $2B,4 and technology leaders made significant investments, such as Microsoft’s $10B stake in OpenAI5 and Google’s $300M stake in Anthropic.
Predict what’s possible in the Age of With, then translate insight into trustworthy performance. Deloitte brings end-to-end AI offerings together with domain and industry insight to drive stronger outcomes through human and machine collaboration.
Ultimately, Generative AI could create a more profound relationship between humans and technology, even more than the cloud, the smartphone, and the internet did before. Various analysts estimate the market for Generative AI at $200B by 2032.7 This represents ~20% of total AI spend, up from ~5% today.8 Said another way, the market will likely double every two years for the next decade. Numbers aside, we believe the economic impact could be far greater. To help understand the potential, this paper is equal parts primer and provocateur, adding structure to a rapidly changing marketplace. We start with a brief explainer of the foundational elements, delve into enterprise and consumer use cases, shift focus to how players across the market can build sustainable business models, and wrap up with some considerations and bold predictions for the future of Generative AI.
In 2022, OpenAI’s DALL·E 2 captured the world’s attention with its text-to-image capabilities.11 The model creates images from simple prompts, from something as direct as “a lion in a jungle” to something more comical like “two lions playing basketball in the style of Picasso.”Ever since, Generative AI has occupied the news cycle, punctuated by other launches like ChatGPT and previews like MusicLM. No wonder we’ve seen broad-market consumer use cases, like Bing’s internet search powered by OpenAI’s ChatGPT.12 These are emblematic of a Cambrian explosion in consumer apps, touching everything from search to therapy.
Generative AI could transform business models, processes, and value dynamics and change how individuals work, learn, and interact. As with other disruptive technologies, this is likely to transpire slowly at first and then rapidly.
Take software development as an example. By some estimates, less than 1% of people know how to code.24 Yet, software is integral to many businesses and business models today. Generative AI, if harnessed strategically, can democratize coding and reduce the gap between ideas and revenue by synthesizing product requirements, converting prompts to code, auditing code to find and address bugs, suggesting code optimizations, and proactively provisioning environments optimized for test and run use cases
Similarly, Generative AI can optimize the end-to-end customer acquisition funnel. If you are in sales and marketing, consider demand generation, where LLMs could author marketing copy across channels and run digital marketing campaigns. Gartner estimates that 30% of outbound marketing will be synthetically generated by 2025.25 Further down the funnel, Generative AI could gather account intelligence, create a first-call presentation, suggest a talk track to account executives, and document and track outcomes and actions. Finally, Generative AI could proactively suggest pricing and discounting, author a contract, and update customer and CRM records. This would allow marketers and sellers to focus on higher-value activities, such as developing relationships and applying pricing judgment. We’ve discussed other ways that adopters can leverage Generative AI across industries (see section 2), from market research to note taking and improving customer support interactions. Further, there are sectorized use cases like customized financial planning for wealth managers, medical diagnoses in health care, generating new worlds and experiences in media and entertainment, and outfit curation for retailers.
A new frontier in artificial intelligence
Implications of Generative AI for businesses