In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.
That’s one of the reasons why critical prioritization is key to selecting the right use cases for implementation and deployment at scale – not just to fit within an organization’s budget, but to also deliver value to the organization.
Having said that, 59% of C-level leaders overseeing the adoption of Generative AI in Fortune 1000 companies say they lack the necessary budget and resources for successful Generative AI adoption, hindering value creation. 66% of respondents face challenges in quantifying the impact and ROI of their AI/ML projects on the bottom line due to underfunding and understaffing. And 42% need more expert machine learning personnel to ensure success. We have a hard time understanding how a double-digit increase in revenue would actually happen when the majority of respondents say they don’t have enough budget, can’t quantify impact, and need more resources. This is a significant gap between hyper- inflated expectations and the reality on the ground – one that we found in our second survey as well.
If you’ve read our second survey report, The Hidden Costs, Challenges, and TCO of Gen AI Adoption in the Enterprise, you’ll recall the worrying budget gap between expectations and reality in considering use cases. Specifically, our survey of 1,000 C-Level Fortune 1000 executives found that while 82% of them are considering 4-9 use cases for their organization (with the number of end users ranging from 501-5,000), an alarmingly low 20% of respondents have allocated an annual budget of more than $2 million. That is worrying, as according to ClearML’s TCO calculator the first year of training, fine-tuning, and serving a model for 3,000 employees hovers around $1 million (depending on data corpus and use case) using an in-house team, with future economies of scale possible through shared compute usage.
Budget concerns aside, we wondered about the specific Gen AI use cases that businesses are planning for.
When it comes to business use cases for Gen AI, the majority of respondents highlighted five critical use cases, with 43% highlighting “Strategy, analysis, and planning (corporate planning, risk management, finance)” as their leading use case, followed closely with 40% choosing “Feature for customers within the product” as their leading use case.
38% of respondents chose “External chatbot/automation to handle low-level tasks (customer support, sales, etc.)” as a key use case, with 37% flagging “Content generation (sales, marketing, HR, etc.)” as a critical use case. Closing the top-five use case list was “Content recommendation/generation engine for enabling talent (customer support or sales representatives)” with 32% of AI leaders ranking it as top priority.
Businesses with content generation needs can get those needs met with popular out-of-the-box single use case applications such as Jasper™ and Copy.ai or available LLM APIs such as Cohere™ Generate. This use case is the least expensive to address, as organizations can simply purchase off-the-shelf apps with a low-cost subscription per user. Best of all, there is no need for organizations to share proprietary data with the app developer, so it is also a low-risk activity, security-wise, one that is easy to outsource.
Having said that, when looking at a wide-scale business adoption of Generative AI, especially within the enterprise, three of the most commonly requested use cases require significant access to internal documents and internal organizational data in order to produce accurate and helpful results. These are:
- Content recommendation engine for supporting internal teams
- Assistant for strategy, corporate planning, and finance; and
- Gen AI as a product feature
The models for these use cases will likely need to be in-house and most likely on-prem in order to protect company data and IP, which means businesses will need to make the investment to build their in-house teams to support multiple models for multiple use cases.
So, what Gen AI use cases is your business planning for? How many users will you support, and do you have the necessary budget? To help you get a clearer view of what your Gen AI true hidden costs are, we have two resources to share:
- ClearML is developing a TCO Calculator to help companies understand the various cost drivers and unpredicted expenses of bringing in and implementing an LLM within their organization. Please let us know if you’d like to be notified of its availability by emailing us at [email protected].
- We recently hosted an expert panel, which discussed the hidden costs and unforeseen “gotchas” that can unravel the expected ROI and TCO related to the different ways to adopt Gen AI and LLMs within an organization – while comparing the pros and cons of AI-as-a-Service, such as building your own or using low-code, secure Generative AI platforms. Visit this page to watch the webinar.
Lastly, you should know that ClearML offers ClearGPT, a secure, out-of-the-box platform for enterprise-grade LLMs. Request a demo here.