Growth Drivers | October 16, 2024

Preparing for an AI future

In today’s fast-paced digital world, clinging to outdated technology is akin to steering a luxury yacht with a broken compass. No matter how impressive your vessel is, you'll never reach your destination.

And as AI and emerging technologies reshape the business landscape, outdated systems and approaches are holding businesses back.

The limitations of legacy systems

Let's face it, yesterday's platforms weren't built for the demands of AI. If they want to keep up, companies cannot keep relying on dated legacy systems; they have to evolve and understand what they need for the years ahead.

The problem is that many organizations have systems in place that were not designed with modern AI capabilities in mind. These systems often lack the scalability and flexibility required to support AI workloads, making it challenging to integrate new technologies or update existing ones.

This can result in suboptimal performance, higher maintenance costs and difficulties integrating new AI solutions with existing systems. Poorly maintained interfaces and outdated APIs can create barriers to seamless integration, impeding the effective use of AI technologies.

The German energy company Bayernwerk needed a modern cloud-based application development platform to support its plan to become a digitized and data-driven enterprise.

Partnering with DXC, the company developed an Azure-based integration architecture enhanced with machine-learning capabilities to simplify processes, improve accuracy, save time and support the use of new technologies such as AI.

Getting ready for AI

By updating their IT infrastructure, organizations can harness the full potential of AI and emerging technologies. Up-to-date systems can better handle the processing power and data demands of AI applications, ensuring improved performance and scalability.

For example, legacy systems often lack the latest security features and patches, making them vulnerable to cyber threats. But new updated systems help address security vulnerabilities, protecting sensitive data and ensuring compliance with current regulations.

Outdated systems can also lead to inefficiencies and increased operational costs. But with new updated systems, organizations can streamline processes, automate routine tasks and reduce the time and resources required for maintenance. And AI and emerging technologies often rely on vast amounts of data. Legacy systems may struggle to manage and analyze this data effectively. Modern systems, with advanced data management and analytics capabilities, can unlock valuable insights and drive data-driven decision-making.

Using outdated systems and infrastructure can also stifle innovation by limiting the ability to implement new features or adopt cutting-edge technologies, whereas modernizing technology infrastructure makes it easier for organizations to stay competitive by integrating the latest advancements and fostering a culture of continuous improvement.

A European-based global manufacturer brought in DXC to build a cloud-based platform that makes it easy to quickly collect machine and sensor data on the factory floor, provide real-time access to information used to optimize its business, and use industrial AI models for enhancing workflows.

In addition to reducing costs through increased standardization across locations, removing the sprawl of temporary solutions has freed up the manufacturer to be more innovative.

How do you begin?

For starters, regularly updating and improving code to eliminate inefficiencies and make it more maintainable is critical. Refactoring (a systematic process of improving code without creating new functionality that can transform a mess into clean code and simple design) helps in making systems more adaptable to new technologies.

Upgrading to modern platforms and tools ensures compatibility with the latest AI technologies and reduces the risk of system failures. Cloud-based solutions, for instance, offer scalability and flexibility that legacy systems may lack. Implementing continuous integration/deployment practices allows for more frequent updates and faster integration of new technologies. This approach helps reduce legacy technologies incrementally rather than in large, disruptive overhauls.

Remember, addressing legacy systems, adapting to evolving needs, and embracing digital transformation is a strategic imperative for businesses seeking to thrive in the era of AI.

On its journey to becoming a fully digital insurance company, MassMutual wanted to boost the value of its software investments by migrating critical applications to the cloud. It has also wanted to decrease operating costs and bring new products to market faster.

By applying machine learning and other advanced technologies, the company can better put data at the center of what it does and integrate applications with digital systems of engagement, which ultimately pays off in an enhanced customer experience.

The cost of doing nothing

By not addressing outdated systems and operational inefficiencies, companies risk: security vulnerabilities due to outdated software; systems that become more complex and prone to failure; and performance degradation over time, making it harder to maintain operational efficiency.

The longer this goes unaddressed, the more expensive it becomes to fix. This can lead to delays and increased costs in the future. And as more workarounds are added, the system becomes more complex and prone to failure.

And the impact of increased value depreciation can much greater if this is not enabled and unleashed with AI.

Modern technology often translates to improved customer experiences through faster, more reliable and personalized interactions using AI. Satisfied customers are more likely to become loyal to a brand or business, leading to increased retention and higher lifetime value.

Using AI to empower AI

AI has the potential to be part of the solution, making it easier to move off of legacy applications and systems that no longer scale to current business needs. This includes:

Automating repetitive tasks such as code generation, documentation and test case creation.

Analyzing and documenting legacy code and logic to the generation of future state design, artifacts and build pipelines, developer augmentation and code conversion.
Automating the execution of test cases.

By automating code generation, testing and debugging, organizations can accelerate the modernization process and deliver results faster.

The big picture

The message is clear: keeping the status quo is like being on a sinking ship, with those who cling to outdated technology destined to flounder in turbulent waters.

But by replacing outdated technologies with modern systems, businesses can be best positioned to set sail with AI and discover new horizons of innovation and opportunity.

And the good news is DXC Technology's consulting, engineering and technical experts are uniquely positioned to advance our customers' complex modernization journeys by helping them gain value from their workloads irrespective of where their data sits—on prem, in multiple clouds or at the edge.