The business world is experiencing a combinatorial explosion as a surge of generative AI startups come to market – and it’s only begun. Creators of the new technology say that businesses will soon be inundated with new products and services now under development.
“In 18 months, the product space is going to look completely different because right now everything is shifting behind the scenes,” said Aidan Gomez, one of the brains behind the transformer algorithm that lies at the heart of generative AI models like GPT-4.
With $1.7 billion of investment having already poured into dozens of AI startups this year and another $10 billion worth of deals in the pipeline, what’s a C-suite executive to do? Throw a dart at this new product map and hope to hit a winner? Or delay buying new products or services and risk being left behind by bolder competitors?
“The rapidly evolving tech landscape, lack of standardization, and complex ecosystem creates a paralysis as leaders navigate trade-offs, uncertainties, and limited resources while considering the long-term implications of their choices,” said Sesh Iyer, North American chair of BCG X, Boston Consulting Group’s technology build and design division.
It’s far from clear which AI applications are most worth investment, nor how to successfully integrate these technologies into their operations.
Business leaders are caught like deer in the AI headlights, overwhelmed by the growing number of AI solutions and their far-reaching implications.
“Though choice is good, there can be too much of a good thing,” said psychologist Barry Schwartz in his 2004 book, “The Paradox of Choice: Why More Is Less.” “It leads to paralysis, to bad decisions, to a loss of self-control, and to dissatisfaction with even good decisions.”
The solution may lie in focusing on the mundane. Businesses should consider AI solutions that automate the many operationally vital tasks which don’t add direct customer value, thus freeing up their staff for more strategic work. This starting point can also serve as a stepping stone, allowing businesses to familiarize themselves with AI technologies before moving onto more general applications.
Where do employees spend their most unproductive time?
Business leaders should adopt a strategic approach to automation by starting with the most discrete tasks within their organization before venturing into more complex automation scenarios. This incremental approach allows for a smooth transition and ensures a solid foundation that empowers the organization to optimize other aspects of the software development lifecycle and unlock further productivity gains.
Take software development – a fundamental driver of success in most businesses today. A practical starting point is to automate unit test writing for developers. Unit testing is a fundamental and essential aspect of the development process, and automating this repetitive and time-consuming task can yield significant benefits. By implementing AI-powered unit testing writing tools, businesses can streamline the development process, increase efficiency, and reduce the risk of introducing bugs or errors into the codebase.
One such solution is Diffblue Cover, which automates the process of writing unit tests for the kind of Java software that’s ubiquitous in large enterprises. Diffblue Cover analyzes an existing Java program and autonomously writes unit regression tests that reflect the current behavior of the code. By automating this process, Diffblue Cover saves development teams as much as a third of their time and ensures high-quality code, helping businesses achieve DevOps goals like continuous integration and delivery.
Once engineering leaders have successfully automated key pain points like unit test writing, they can consider implementing more general AI tools like Copilot to further accelerate their developers’ work. Copilot, powered by OpenAI’s GPT-4 AI model, is an innovative code generation tool developed by GitHub.
Copilot acts as a programmer’s assistant, saving developers time and effort by automatically providing them with instant code suggestions and solutions. Tools like Copilot can be used across a wider range of languages and scenarios and can provide a real efficiency boost.
There are other tools, but these two alone can accelerate development. Business leaders need to survey their workforce to determine what tasks are taking up valuable time and which can be automated with new AI tools.
By starting with a narrow focus on automation of clearly defined tasks, whether large or small, businesses can gradually build their AI capabilities and navigate their way through the AI landscape.
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