Since late last year, the global conversation about AI has been focused on large language models like OpenAI’s GPT-4 and Google’s Bard. LLMs have improved with remarkable speed, and ChatGPT is now the fastest-growing consumer application ever. It’s no wonder that LLMs have captured the public imagination, but AI has countless applications – including many that we can’t possibly predict today. VCs need to have an open mind about these applications, while working to identify the companies that are already using AI to generate value.
The use cases for AI are functionally limitless. While there has already been a huge proliferation of companies focused on everything from AI-powered customer engagement to cybersecurity, the field is only going to pick up momentum. AI is helping developers code more efficiently, improving employee productivity, generating content, allowing companies to leverage proprietary data, and even enabling users to create their own purpose-built AI. The technology has already become integral to many industries, and adoption rates are constantly rising.
VCs have to be capable of recognizing which applications have the potential to maximize ROI and get ahead of the most promising trends in the field. While there’s no doubt that we’ve entered the AI era, there’s still plenty of uncertainty about which companies are capable of putting the technology to the best possible use. This is why it’s useful to take a look at the companies that are using AI effectively.
Identifying the most promising AI applications
Certain industries are more exposed to AI-driven disruption than others, and the first-movers in these industries have a significant advantage. For example, consider the call centers that airlines, retailers, and companies in many other industries rely on to meet their customers’ needs. The global call center market is projected to grow from $314.5 billion to $494.7 billion by 2030, and AI will be a critical competitive differentiator in the industry.
ASAPP recognized this fact early and developed an AI-powered platform to personalize customer experiences, increase agent productivity, and generate actionable data that helps contact centers assess and improve their performance. ASAPP’s AI tools help customer service agents compose messages more quickly, summarize customer interactions, and transcribe messages. These tools have a proven record of increasing agent productivity, reducing costs and NPS scores, and driving higher sales.
In industries like customer service, AI helps companies respond to emerging trends and customer demands. Over 70 percent of consumers now expect personalized interactions, and 76 percent are frustrated when this isn’t provided. When customers receive personalized communication, they’re 78 percent more likely to repurchase. AI gives companies access to individual customer data, develops personalized forms of engagement, and gives each customer a more positive and unique experience. These are advantages that will help companies secure long-term loyalty and sustainable revenue growth.
A revolution in software development
Just as customer service is a prime candidate for disruption in the AI era, fields such as software development are in the middle of a fundamental AI-powered transformation. AI can help developers code more quickly and with fewer errors, which is why adoption rates in the software industry are rising rapidly. According to a 2023 McKinsey report, developers that use generative AI tools are “25 to 30 percent more likely than those without the tools to complete those tasks within the time frame given.”
IBM reports that the top three AI user groups are IT professionals, data engineers and scientists, and developers. For example, Tabnine is an AI assistant for software developers which uses generative AI to suggest the next line of code based on context and syntax, which automates and accelerates the coding process. Tabnine supports a range of major programming languages, such as JavaScript, Python, TypeScript, and many more. Because each AI model is trained from the ground up and optimized for a specific language, the code is hyper-relevant and highly accurate – which saves developers time and effort.
Tabnine reports that its solution can automate over 30 percent of code production – a clear sign that AI has a growing role to play in software development. Instead of fearing this shift, developers should embrace it as a way to augment their performance and improve efficiency.
A vast and growing array of use cases
While it’s unsurprising that AI is already pivotal in fields like customer service and software development, it’s rapidly becoming a key competitive differentiator in many other industries. AI adoption is growing steadily – 35 percent of companies are using AI in their business, while another 42 percent are exploring the technology. A recent Accenture report found that LLMs alone will have an impact on 40 percent of working hours.
The top ten user groups of AI include security, marking, sales, HR, and finance professionals. Consider a few of the diverse AI-focused companies that are already valued at over $1 billion. Moveworks is a generative AI platform that uses hundreds of machine learning models to automate tasks and create a centralized enterprise knowledge system. Typeface uses AI models to help companies automate brand management and create personalized content at scale. MosaicML (which was recently acquired by Databricks for $1.3 billion) empowers companies to securely build their own AI around their proprietary data.
This is just a brief glimpse at how quickly AI innovation is spreading and advancing, and it’s a sign of what’s to come. AI is a once-in-a-generation technology, and its role in our lives and businesses will only continue to grow – often in ways that we can’t possibly imagine today. While this will lead to disruption and displacement, it will also unleash a new era of innovation and complement human ingenuity in countless ways.
About the Author
Steve Schmidt is a General Partner at Telstra Ventures, a global venture capital firm with a strong track record that incorporates data science and quantitative analysis of non-financial data into its mix of investing criteria. Prior to joining Telstra Ventures, Steve held various leadership positions at Telstra and Westpac.
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