Artificial intelligence (AI) is quickly becoming ingrained into the fabric of life. Yet, for enterprises, adopting the tech at scale is not as simple as flipping a switch. In fact, many organizations still struggle to move beyond pilot projects into full-scale deployment.
The challenges? Unclear business value, poor data quality, inadequate risk controls or difficulty calculating return on investment (ROI). Gartner predicts that by the end of 2025, at least 30 percent of generative AI (GenAI) projects will be abandoned due to those reasons1.
That is why Hitachi is committed to working with customers across industries to develop their AI projects deliberately with production and scalability in mind.
Hitachi has a long history of AI research, development and productization, helping organizations, from manufacturing and finance to healthcare and transportation, leverage the technology to achieve greater efficiencies and improve business performance. What sets Hitachi apart is its structured approach to AI, based on three core principles: Outcome-Oriented, Purpose-Built, and Responsible & Reliable.
1. Outcome-Oriented: Identifying AI Goals
Hitachi’s approach to AI is designed to drive tangible results — whether that means reducing costs, enhancing customer experiences or improving safety. It begins by working with customers to identify their objectives, assess their resources and then setting clear, measurable goals.
For example, Hitachi partnered with Penske Truck Leasing, a leading fleet management company, to help them reduce vehicle downtime and avoid costly roadside delays. Hitachi and Penske co-developed Proactive Diagnostics, a model that uses AI and machine learning to help better predict mechanical issues before they happen. This model transformed Penske’s fleet maintenance approach for over 400,000 vehicles on the road and delivered on the objectives. Predictive measures now help fleet operators and drivers save valuable time by keeping deliveries moving.
By setting clear, measurable objectives from the start, we maintain focus, minimize surprises and increase the likelihood of success — ultimately driving returns on investment and delivering impactful benefits for both businesses and customers.
2. Purpose-Built: AI Tailored to Industry Needs
Not all AI is created equal. What may work in manufacturing may not work in retail. From Hitachi’s research and development, we are keenly aware of the value of purpose-built AI and the creation of industry-specific tools and accelerators crafted for use within specific industries for specific types of data.
Take Hitachi Digital Services' Reliable, Responsible, Observable and Optimal AI suite (R2O2.ai) as an example. R2O2.ai is specifically designed to bridge the gap between conceptualizing AI workloads and scaling them for real-world applications seamlessly to meet the unique needs of industries. A key component of R2O2.ai is its “periodic table” of AI accelerators, which include some of the most needed and requested core capabilities from the industrial sector – ranging from Model Based Yield Prediction in manufacturing to Asset Failure Prediction in mobility.
Every enterprise has its distinct challenges, which is why a purpose-built approach to AI is crucial to achieving impactful results.
3. Responsible & Reliable: AI that Businesses Can Trust
A responsible and reliable approach to AI is an inextricable concept for Hitachi. The more attention placed on creating responsible AI, the more reliable the outcomes will be.
With this vision, GlobalLogic, a Hitachi Group company, developed a new Platform-of-Platforms architecture that deploys AI and GenAI at scale, ethically and seamlessly. It is a first-of-its-kind platform where businesses can build AI-powered applications that are secure, compliant with regulations, drive improved business performance, and speed up the ROI of enterprise-grade AI. In fact, GlobalLogic is already transforming industries like rail, energy, and nuclear with AI solutions.
This is how Hitachi envisions and encourages customers’ approach to AI, with strong governance and transparency at its core, so businesses can rely on their AI systems to grow and better serve their business.
As the pace of AI development grows faster, so does the need for clarity. Without it, projects will continue to be funded but stall before reaching production. Hitachi’s three core principles provide a contextual guideline for successful AI and GenAI experimentation and scalable implementation. Through an approach that is outcome-oriented, purpose-built, and responsible and reliable, organizations can begin to harness the overwhelming power of AI.
To learn more about Hitachi’s approach to AI, click here.
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