Muddu Sudhakar, Investor, Board Member and CEO of AISERA, Inc., an AI Service Management (AISM) Company.
Killer applications are breakout software systems that prove the core value of a larger technology such as a platform, operating system or gaming console. They are essential for driving growth and adoption. They also mark a sharp change in the direction of the technology industry.
During the 1980s, the killer applications were for the PC platform such as spreadsheets and word processors. As for the 1990s, it was the internet that took center stage with Amazon’s e-commerce juggernaut, the Yahoo portal and email. Then in the mobile era, the killer applications were Uber, WhatsApp and Instagram.
As of today, the new platform is generative AI. This was sparked with ChatGPT, which has helped boost OpenAI’s market value to about $80 billion.
What about the other killer apps? Interestingly enough, they are mostly not in the consumer space. Rather, they have emerged in the enterprise. This is due to the inherent capabilities of generative AI. The technology is particularly good at handling natural language, reasoning, automating tedious processes and interpreting huge amounts of data. These capabilities can lead to significant improvements in productivity, cost savings and even revenue opportunities.
Keep in mind that generative AI has emerged as one of the top priorities for executives. This is highlighted by a recent IDC report that predicts spending on the technology will go from $16 billion in 2023 to $143 billion by 2027. This is a 73.3% annual growth rate. Predictions are far from foolproof, but there is certainly significant momentum.
Let’s take a look at some of the early killer applications for generative AI in the enterprise.
AI-Assisted Coding
In June 2022, Microsoft’s GitHub launched Copilot for AI-powered code generation. According to GitHub CEO Thomas Dohmke: “When you type code or comments, GitHub Copilot suggests the next line of code. But it’s not only a single word or line of code. GitHub Copilot can suggest complete methods, boilerplate code, whole unit tests, and even complex algorithms.”
Copilot would go on to be one of Microsoft’s fastest-growing applications. In Q2 2024, the company announced that there were over 1.3 million paid users, up 30% on a quarter-over-quarter basis. There are over 50,000 organizations that use Copilot, like Goldman Sachs, Etsy, Autodesk and Dell Technologies.
Other megatech companies such as Google and Amazon have aggressively launched their own AI-powered code generation tools. There are also numerous startups in the market like Cursor, Tabnine and Replit.
The value proposition for AI-assisted tools is clear. A 2023 McKinsey study showed that developers could create code 35% to 45% faster. It also has been shown to effectively onboard new developers to projects. With severe shortages of technical talent and the strategic importance of creating applications, generative AI can be a force multiplier for an enterprise.
Cybersecurity
There continues to be significant growth in the number of cybersecurity threats. In 2023, the number of global data breaches soared by 72%. The average cost of a breach is about $4.45 million, according to research from IBM.
Generative AI can be critical for mitigating the risks. After all, a breach can happen within minutes or even seconds. There needs to be a sophisticated defense system that can act in real time and is highly adaptable. Generative AI’s ability to decipher complex patterns can be instrumental. It can also learn at high speed, allowing for the defenses to anticipate problems.
Additionally, generative AI can help alleviate the severe talent shortage for security professionals. On a worldwide basis, the shortfall is expected to reach 3.5 million by 2025. The technology can help automate many of the tedious processes while also bolstering employee training.
According to a 2023 survey from KPMG, 63% of IT professionals say that generative AI in cybersecurity will have the greatest impact on their organization, and 64% expect to use this technology within the next six to 12 months.
Then again, the productivity gains can be substantial. For example, Checkpoint Software’s cybersecurity copilot has saved 90% of the time on common security tasks. According to a Microsoft whitepaper (download required), its offering has improved security professionals’ speed by 22% across all tasks. As for security novices, they were 44% more accurate.
Customer Service
For many years, chatbots have been mostly a disappointment. One of the main reasons is that they have generally been preprogrammed with scripts and decision trees, meaning chatbots cannot understand certain queries. They can also be impersonal and not effectively take into account a user’s background information. Then, there are the issues of making handovers to human agents.
However, generative AI is poised to change the game. Consider the case with Klarna, which is a fintech startup. The company partnered with OpenAI to use generative AI to create an AI assistant for customer service.
One month after the launch, the company announced that its tool could handle about the same amount of work as 700 full-time agents and that satisfaction scores were roughly at the same levels. The AI assistant also had a more accurate resolution rate, which meant a 25% drop in repeat queries. Klarna estimated that the technology would result in a $40 million profit improvement.
Conclusion
Unlike previous technological shifts, which were primarily consumer-focused, generative AI has become fertile for killer applications in the enterprise. As spending on generative AI software skyrockets, these early applications are just the beginning of a wider and deeper integration of AI technologies in the enterprise—signaling a major shift in how businesses operate and compete in the digital age.
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