Four years ago, I wrote a similar article, and now, as the Chairman, President and CEO of DXC Technology, I am reminded of its ongoing relevance as I read the latest headlines and engage with customers worldwide who are eager to assure their boards and investors that they are embracing innovation to drive growth.
Every CIO is looking to invest in the latest artificial intelligence (AI) and machine learning (ML) technologies to gain a competitive advantage, achieve cost efficiencies, accelerate speed to market and enhance the overall experience. Believe me, you don’t want to be late to this party. But the pressure to act fast may hinder your ability to achieve desired business objectives.
One aspect that many CIOs overlook as they embark on their AI/ML journey is how to evolve their talent strategy to effectively scale their initiatives in this field.
The new 80/20 rule
At DXC, we have learned that to maximise the benefits of machine learning for your business, you need to adopt a hybrid approach that combines technology to take you 80% of the way there, and people who will take it the rest of the way.
Many software products today boast embedded AI and ML capabilities, suggesting that businesses can simply rely on a plug-and-play approach. Take it out of the box, plug it in and it magically works.
But the true essence of machine learning — its groundbreaking nature and the value it brings to businesses — is impossible to deliver without human intervention.
A perfect example of this is DXC Platform XTM, our data-driven intelligence platform that efficiently manages our customers' IT estates. Platform X integrates advanced AI and ML technologies with a vast catalog of automation bots. However, it is designed to include our engineers, who operate from a virtual "control room" to ensure its optimal performance.
How ML works
To make machine learning effective, you must first feed your data into the platform, clean it, configure the ML model and then calibrate the model as it encounters data. This process of running data through the model, and continuously monitoring and gathering feedback, improves the model's performance and generates accurate results.
To put it in perspective, consider the human brain. People are better at thinking and drawing conclusions when they have more data, experience and accumulated wisdom to process that data.
Relying solely on what's written on the ML "product box label" or the vendor's response to an RFP can lead you down a risky path. Every CIO should prioritise three critical success factors when implementing ML in their company.
1. An ML model without data is like a car without gas.
To train machine learning models effectively, you need high-quality and high-volume data. This data must be easily accessible, in the right format, and diverse enough to ensure unbiased results.
At DXC, our data model is built on more than 60 years of managing essential systems for over 6,000 customers. This rich history enables us to train our models with better quality data, resulting in faster and more accurate recognition of service-impacting issues, leading to fewer disruptions. Each individual customer benefits not only from their own data but also from the collective wisdom derived from our customer base. In other words, our data (secured and anonymised) becomes your data.
2. There is no "set-it and forget-it" solution.
As mentioned earlier, ML models require human oversight. While software solutions will continue to improve over time, the success of ML in business relies on skilled professionals who can make it work effectively.
At DXC, we have an elite team of specialised data scientists who experiment, design and create models. Our global engineering workforce across 70 countries enables us to deploy, monitor, audit and optimise models at scale.
One crucial capability our engineers possess is the ability to determine the right mix of ML products and features. Many products offer similar functionalities, but the knowledge of what to apply where and when for optimal results is a uniquely human trait that requires practical experience and judgment.
While AI and ML may disrupt certain job markets, they also create new and exciting opportunities for tech-savvy individuals who embrace change.
3. Don't forget about integration.
An often-overlooked aspect of the plug-and-play approach is the effort required to configure all the integrations. Only new startups have the luxury of building a greenfield estate. The majority of us are faced with the challenge of integrating new technologies with existing ones. Most Fortune 500 companies have sprawling and complex IT estates, comprising thousands of servers, hundreds of strategic applications, and distributed endpoint devices supporting a dynamic and often virtual workforce.
This is where DXC Platform X excels. Its open, modular architecture allows for easy integration and flexible deployment options, leveraging our customers' current and future IT investments. We have done the heavy lifting by providing pre-configured integrations for top enterprise SaaS platforms, ready-to-use ML models, a catalog of hyper-automation assets and experienced engineers who seamlessly connect all the pieces.
The AI and ML revolution will undoubtedly continue to accelerate, driving advancements across all industries. At DXC, we are excited to be at the forefront, thoughtfully evaluating and applying emerging technologies for the benefit of our customers and our people.
Regardless of your strategy — whether you choose to buy, build, or partner for AI and ML advancements — you must prioritise more than just ML itself. High-quality data, the right talent, and a well-rounded integration strategy.