How to navigate GenAI’s micro-macro dichotomy

When generative AI entered the mainstream in early 2023, it promised to revolutionize the business landscape, transforming every aspect from strategy to innovation. However, business leaders are still trying to understand the value and risk it brings.

A dichotomy has created where macro benefits – such as productivity, resource optimization and cost savings – are hampered by short-term decision-making challenges that not only hold businesses back but also overwhelm them with seemingly endless choices about where, when and how to implement generative artificial intelligence.

Because of this, it’s easy to become paralyzed by the myriad opportunities and challenges that generative AI presents. From challenges such as ethics and regulatory implications to transformational benefits such as the ability to modernize legacy code bases, there is a lot to digest, understand and turn into value for companies. But one thing is certain: Boiling the ocean won’t move the needle, and neither will inaction.

So where does a business start?

Targeted, industry-specific use cases are key

Generative AI presents use cases unique to each company and the challenges they face in their industries. Choosing the right problem to solve first can pay off later. Some of Australia’s largest banks are already leading by example, and many companies can learn from their early successes.

For example, the problem of fraud and extortion in Australia is constantly growing. As Commonwealth Bank of Australia processes transactions for nine million Australians, the bank uses this transaction data to refine its artificial intelligence model to identify unusual transaction patterns and prevent such risks for customers. This is a small step towards integrating AI to solve a problem, but it creates a tangible use case that the bank can now learn from and base its actions on.

Elsewhere, Westpac’s Growth Lab observed a 46% increase in productivity after testing the impact of generative AI on software development – an outcome that resulted in no loss of code quality. The study measured two groups: people who used generative AI tools and a control group who did not. What’s more, 83% of junior engineers said it helps them learn early in their careers, helping to lower the barrier to entry for new and aspiring tech talent at a time when the economy is crying out for more such professionals.

Building on this, AI generative coding tools can also help newer generations of software developers translate COBOL – the older code base best known by a generation of retiring software engineers – into newer, more flexible software development standards. Not only does this help the next generation of software engineers maintain and migrate the code base that powers Australia’s largest banks, but it also allows them to do so in a compliant way.
Additionally, we see industries that have typically been slower to adopt new technologies – such as construction, engineering and law – challenging these norms and using AI in targeted applications. For example, John Holland, a leader in the construction industry in Australia and New Zealand, has equipped all employees with a private ChatGPT solution so they can write and summarize content such as project briefs more quickly.

These are just a few examples to illustrate that by starting small and with an industry-specific problem, companies can begin to make strides in generative AI in a meaningful and effective way. The key is to start small and build from there.

Translating solving micro problems into macro benefits

Improvements in automation and efficiency at the micro level contribute to increased productivity at the macro level throughout the organization. On a scale, this applies to the entire industry and then to the economy. At the same time, micro-level improvements in customer interactions and personalization contribute to a positive impact on brand reputation, customer loyalty and market positioning.

We are witnessing the development of this reality in the world of software. As the widespread adoption of generative AI tools like GitHub Copilot demonstrates, AI has begun to rapidly reinvent software development, delivering up to a 55% increase in developer productivity. Developers are now creating high-quality products and bringing them to market faster than competitors. But this is just the beginning – these productivity benefits have a ripple effect, and AI tools for developers alone are expected to add $1.5 trillion to global GDP by 2030.

The journey toward generative AI integration begins with recognizing its dual potential to transform individual tasks and entire operational landscapes. Companies that can navigate this micro-macro dichotomy, prioritizing immediate, achievable goals while keeping an eye on the broader horizon, will not only thrive, but also set new standards for innovation and performance in their industries. What’s most important now is how quickly these organizations can enter the era of artificial intelligence. The first to do so will operate on a completely different productivity spectrum, outperforming the competition and delivering products to market faster than their competitors. Those who don’t will simply be left behind.