Generative AI use cases
<div class="insights_cta-component">This article is the second in our new series of articles on Generative AI. See the full series below</div>
In our previous article, we discussed Generative AI, how it works, and its potential effects on consumer behavior. In this second issue, we hope to create some urgency and encourage you to take the first step in using Generative AI.
Generative AI is one of the few technologies in recent years that has immediate applications in businesses across all industries – the hype has made that much clear. However, to feel the urgency and take the first step, you need to understand what impact Generative AI will have on your business and how to identify use cases for your business to get started.
IMPACT ON YOUR BUSINESS
Three categories of disruption
Generative AI will impact business differently, but its impact can largely be categorized in three main buckets:
- Direct – There is a direct risk of business model obsolescence. Businesses that are directly impacted may find that their core services and offerings may completely, or to a large degree be substituted by Generative AI models
- Transformative – Significant impact on business models and propositions. Businesses here will find that their core proposition is under pressure from competitors and new entrants with better propositions using Generative AI
- Incremental-only – The impact will not affect the core proposition of companies. Instead, there will be opportunities to optimize and improve efficiency
During the technology wave of internet & mobile in the mid-late 2000’s some 5% of businesses faced direct disruption, 65% faced transformative change and only 30% were just incrementally affected. We believe we’ll see similar numbers in the Generative AI wave.
Identifying impact on your business
So how do you identify the impact of Generative AI on your business?
Clearly, this is a business-specific question rather than an industry-wide one. However, with a few questions you can establish a rough idea of where your business might sit
With Generative AI…
EXAMPLE | Direct impact
Chegg provides an online platform for textbook renting and access to online tutoring. The company saw its share price fall some 50% in May of 2023 following its first-quarter 2023 earnings announcement. Chegg management noted that it “saw a significant spike in student interest in ChatGPT” and that they “now believe it’s having an impact on new customer growth rates”.
In response, Chegg has launched CheggMate a Generative AI homework assistant fine-tuned on their proprietary student data.
EXAMPLE | Transformative impact
Law firm Baker McKenzie is reliant on large pools of junior manpower to perform tedious and detailed document revision tasks. Generative AI is ideally suited for tasks like document analysis & information retrieval, potentially making a large part of this manual work unnecessary.
Baker McKenzie was one of the first firms to react and is already experimenting with Generative AI models to augment attorney workflows, and improve the delivery of client services.
EXAMPLE | Incremental impact
Coca-Cola has already signed as a partner for OpenAI’s ChatGPT and DALL-E to create personalized ad copy, images, and messages. This will create not only efficiency for one of their largest cost-drivers but can also increase the quality & personalization level of their marketing output.
The Incremental-Only Fallacy
Direct impact is often obvious to companies – and in the mobile wave, CEOs of companies directly impacted quickly noticed they were in trouble and pivoted towards the new technology. Transformative impact is much harder to judge – with many CEOs expecting incremental-only changes at first. Precisely because of this ‘fog-of-war’, identifying & dealing with transformational change early can lead to enormous value creation.
The main reason companies don’t start with transformational change, is that they underestimate competition using new technology. Impact of early disruptors is often dismissed or disregarded until it’s too late.
Picnic, an early disruptor in the grocery industry, is a clear example of this. Initially, brick-and-mortar supermarkets didn’t believe Picnic’s business model would have large-scale impact. Then, after a few years, they thought its proposition could never be profitable. Only once Picnic became an actual competitor did they finally realize grocery delivery would transform the entire industry, and started scrambling to keep up.
Just like inthe digital wave, the key takeaway is to act fast. As the speed of development & size of impact are uncertain, companies can’t act too early, but can act too late.
USE-CASES – STARTING TO GET VALUE OUT OF GENERATIVE AI
Starting to use GenAI
Hopefully, by now, you’re feeling the urgency to act. The question is ‘how?’ and the simple answer is that there are 3 main categories of actions you can take:
- Encourage usage of applications available on the market – this will include both paid and free online tools like ChatGPT, Dall-E, Dreamstudio, and many others.
- Enhance your existing tools through Gen AI APIs – many major SaaS companies are integrating tools like ChatGPT into their proposition allowing you to use these tools seamlessly with existing tools. One of many examples: Hubspot has added ‘Content Assistant’ & ‘ChatSpot’ that are ChatGPT-based tools that have access to your Hubspot data to massively simplify life.
- Finetune proprietary models – here you can finetune your own Generative AI model on your own data. With companies like Amazon SageMaker and Hugging Face, you can develop a proprietary model for a specific task
Whichever approach you take: be pragmatic. It’s better to get started soon than to wait for the perfect use case. Finding use cases to pursue should not be a month-long process – it can be a quick top-down and bottom-up ideation, followed by an impact/feasibility prioritization.
Impact across business functions
Finding use cases is very business-dependent. As its name suggests, Generative AI is generally better suited for tasks that involve generating new content or enhancing creative processes. As such, more content heavy functions such as marketing, customer service, and IT (development) stand to be more heavily impacted.
In contrast, business functions that are more exact or factual in nature, such as finance and operations, may not benefit as much on the short term. For now, these areas are better suited to traditional analytic AI models, which can provide more effective solutions in terms of data analysis and quantitative decision-making.
Marketing – Driving content creation and personalization at scale
Generative AI finds its most prominent application in marketing, where the driving factors are personalization and the ability to swiftly create personalized content at scale. Generative AI can help both during brainstorming as well as final content creation; especially for copy & images.
Using dreamstudio, users can generate images using natural language prompts, with the option to provide specific prompts for certain styles (e.g., “van Gogh style” or “oil painting”) – source: dreamstudio.ai
Customer service – Improving the customer experience
Customer service has already seen a significant rise in AI-based chat solution in the past years. However, Generative AI has the advantage that it can automate and personalize responses to almost any question (instead of a pre-selected list of options). While it’s still necessary to keep the human in the loop – customer service requests can be handled much faster
Using Salesforce Service GPT, customer service agents can automatically generate responses to customer service questions
IT (Development) – Helping coders and providing input
Generative AI tools can interpret & write snippets of code from natural language. Developers are already turning to tools like GitHub’s CoPilot, Amazon’s Code Whisperer, and increasingly ChatGPT, to speed up their development processes. Such tools allow developers to ask coding related questions in natural language and receive suggestions for improvement.
Amazon’s CodeWhisperer can provide coding suggestions, scan for vulnerabilities of give improvement recommendations
(source: amazon.com/codewhisperer)
In the next article of this series, we'll get pragmatic on how to get started with Generative AI and discuss practical strategies and approaches that span across tech, data, processes, and organizational aspects.
<div class="insights_cta-component">Questions? Comments? Want to have a conversation with our experts about the contents of this article? Get in touch with our team now!</div>
NEXT UP IN THIS ARTICLE SERIES
We have learned a lot through helping our clients over the years, and we’ll be sharing our key insights with you in a number of publications – see below the list of topics we will cover:
- Introduction to Generative AI – This article is the first in our series on Generative AI and focuses on the what & how of Generative AI
- Generative AI use cases – Exploring what Generative AI applications are out there already, and which we expect to be possible in the (near) future
- Implementing Generative AI – What is required to successfully implement Generative AI: tech & data, processes, and organization
- Generative AI and the future of work – How Generative AI will impact our work, which jobs will change or disappear, and which new roles will be required
- The importance of data in Generative AI – Why high quality data is (even more) important for Generative AI, and how you can get your data ready for Generative AI
- Tooling & prompting – What tools are available to implement Generative AI, and how can you write efficient prompt to leverage tools like ChatGPT in your work
- Generative AI risks/pitfalls/ethical/diversity concerns – What can go wrong and how to make sure to avoid it
Stay tuned!
We hope you’re as excited as we are and please let us know if you have specific topics or questions you would like us to share with you.
