Doing more with less in the age of digital transformation

Though enterprise leaders have made strides in advancing their digital agendas, economic headwinds and a rapidly changing business landscape are forcing them to do more with less – fewer people, lower budgets, and minimal costs.

In years past, such a disconnect between available resources and business mandates would have created an impasse. Today, rather than slowing digital transformation, teams can use technology to thrive amid the constraints.

Consider the importance and wide adoption of artificial intelligence technology across industries, which is quickly transforming how we work. Whether it's generative AI like DALL·E 2, using machine learning (ML) algorithms to create images from text, or ChatGPT, an AI chatbot that uses deep learning techniques to produce human-like speech, the potential for business use is endless.

AI, data, and analytics have the potential to help businesses do more, even when they have less. Here, we'll explore how AI supports a data-driven enterprise and the three ingredients for success.

For most organizations, AI is far from a golden ticket. The rapid transformation it fuels has also brought new challenges. According to a recent study by McKinsey, more than 75% of enterprises have piloted some form of AI, yet less than 15% have realized a meaningful, scalable impact.

Even worse, many leaders often see data governance as a tax instead of a strategic initiative, leading to substandard risk management. As a result, teams often struggle to manage, access, understand, and – most importantly – trust their data.

And these issues only increase when data engineers layer black-box models on top of biased data. This approach leads to unintended consequences such as discrimination, prompting teams to waste time and resources retraining models.

For artificial intelligence and machine learning projects to succeed, it's crucial to have strong data governance and a well-defined framework for responsible innovation, such as machine learning operations (MLOps). By embracing industry best practices to manage complex ML models from the start, teams can operate efficiently, improving the speed, quality, and reliability of artificial intelligence in business.

What's more, through MLOps and a solid data foundation, enterprise leaders can effectively scale and replicate ML solutions across the business for a greater return on investment.

After nearly 100 years at the top of the global reinsurance market, a reinsurer was in danger of falling behind the competition. To keep up with digitally transformed insurtech companies, it needed to cut underwriting response time from over a week to under two hours.

And with minimal investments, the company achieved maximum results. We started by optimizing its IT infrastructure and securely transitioning its data to the cloud. With this foundation in place, we connected data from multiple systems into one data pipeline. Then we put this data to use, feeding it into two new ML models we created to empower underwriters to identify the best opportunities rapidly.

By combining the best of human and artificial intelligence, the reinsurer can now produce results for 200 entries in just 14 seconds. With greater accuracy and efficiency, teams can respond to queries faster while improving the customer experience.

Digital transformation has been an elusive target for many companies, more of an aspiration than a reality. However, this trend will likely change. We believe emerging technologies will propel companies to realize their vision faster and with fewer resources.

AI, analytics, and other emerging technologies will become the vehicles of digital transformation, data will become the fuel, and the alignment of people, processes, and partnerships will become the compass.

Let's explore each point of the compass in more detail:

With outdated inventory management and forecasting processes, a global aircraft engine manufacturer struggled to deliver replacement service parts on time. For its clients, delayed parts meant delayed flights – a shortcut only to customer dissatisfaction.

So, we jumped into action. First, to expedite operations, we partnered with the manufacturer's team to digitize the supply chain, using machine learning techniques to analyze demand patterns and anticipate the most common requests. Then, we introduced an automated order fulfillment solution. Finally, we helped the manufacturer identify problems before they happened with an internet of things (IoT) solution for predictive maintenance.

In the first year, the manufacturer saved over $300 million from new planning and supply chain efficiencies and additional savings of $250 million each year from predictive maintenance powered by AI and advanced analytics.

The challenge of the past few years was scaling for hyper-growth. And at times, leaders had seemingly unlimited resources to make it happen. Looking ahead, the mandate to use technology to scale insights across the enterprise and spur innovation remains, yet the unlimited resources have dwindled.

Connecting people and processes with the right AI and ML partnerships will educate, equip, and empower employees to make critical decisions using data. In addition, this approach elevates teams from technical and non-technical backgrounds to high levels of data literacy and analytical competency.

With a new operating model, enterprises can tap into their treasure trove of data to spark innovation, manage resources efficiently, and deliver business impact, without delaying their digital transformation goals.