Hitachi Digital Services (HDS) is committed to embedding AI deeply and systematically into next-generation architectures. The Center for Architecture and AI (CAAI) is a key initiative designed to lead the integration of AI into core business applications. The approach emphasizes the creation of intelligent architectures that blend rule-based and reasoning-based logic to deliver measurable business impact.
Q1: Could you please take us through your Hitachi journey, and role and responsibility as the CTO at Hitachi Digital Services. How has the organization evolved in the last 4-5 years – shift from traditional IT, and infrastructure services to digital and AI services (from Hitachi Vantara to HDS)?
My journey at Hitachi Digital Services has been an exciting one, spanning over four years in this role. During this time, I also had a period where I founded krestwolf.ai, an innovative software platform focused on Agentic-AI for Site Reliability Engineering. This experience significantly deepened my expertise in autonomous agents and their potential.
My core responsibilities as the CTO of Hitachi Digital Services involve setting the Technology strategy and direction for the company, enabling adoption of new technologies and delivery enablement. A key part of this is building thought leadership in critical areas such as Artificial Intelligence, application modernization, and IT/OT integration. We aim to be recognized as an AI-Native Service provider and Agent builders of the world.
Hitachi Digital Services spun off from Hitachi Vantara to become a dedicated Digital and AI Services company, moving beyond traditional IT infrastructure. This strategic separation allows Hitachi Digital Services to solely focus on providing advanced digital solutions, cloud, data, IoT, and IT/OT integration services, with a strong emphasis on leveraging AI/ Gen AI. This shift empowers Hitachi Digital Services to drive digital transformation and create significant value for clients by applying cutting-edge technology and collaborating closely with other entities in Digital Systems and Services group at Hitachi.
Our overall vision for AI is two-fold: applying AI in every service we provide to customers to accelerate delivery and enhance their competitive positioning, and designing every solution as an intelligent AI-Native solution1. To achieve this, we emphasize:
- AI Powered System Integration.
- Intelligent or Agentic Managed services.
- Being an AI-Native Integration Partner.
- Delivering Future-Proof, Intelligent Architectures, particularly focusing on architecting and building Agentic systems
The development and implementation of frameworks like Hitachi Digital Services' R2O2.ai (Responsible, Reliable, Observable, and Optimal AI) are crucial for building trustworthy AI solutions and fostering greater confidence for enterprise-wide adoption
We have also unveiled HARC for AI (Application Reliability Centers for AI) to improve enterprise AI reliability, observability, and cost control.
Q2: As you currently lead the AI mandate for Hitachi Digital Services, please enlighten us with the top three disruptive trends reshaping the future of AI. How will those affect the enterprises of the future, and how should enterprises respond in order to benefit from AI.
The future of AI will be shaped by three major disruptive trends. First, we will see the industrialization and scaled deployment of Agentic AI, moving beyond pilots to enterprise-wide implementation. This shift is driven by Agentic AI's ability to tackle complex challenges, deliver significant efficiency gains, and improve reliability in both OT and IT environments, ultimately driving cost reduction and operational improvements. Second, the paramount importance of trustworthy and responsible AI solutions will become even more critical. As AI scales, robust frameworks like Hitachi Digital's R2O2.ai will be essential to address concerns around data governance, security, reliability, and ethics, fostering greater confidence and accelerating adoption. Finally, the evolution towards AI-native and intelligent architectures will be crucial, enabling seamless integration of AI capabilities into existing ecosystems and allowing systems to self-optimize and adapt proactively, bridging the gap between OT and IT systems.
These trends will profoundly impact enterprises by enhancing operational efficiency, potentially leading to new revenue streams beyond just cost savings, and increasing the demand for robust data and security frameworks. IT infrastructures will transform to integrate AI seamlessly, requiring adaptive and intelligent architectures. This complexity will also necessitate specialized expertise and strategic partnerships to navigate the challenges of implementing and scaling AI.
To effectively capitalize on these changes, enterprises must respond strategically. They should adopt Agentic AI selectively, focusing on specific use cases where its unique capabilities deliver the most value, and prioritize trustworthy AI solutions by investing in strong data governance and security frameworks. Evolving IT architectures for seamless AI integration, particularly bridging OT and IT, is also vital. Critically, enterprises should demand and measure tangible business outcomes, shifting the focus from technology adoption to demonstrable ROI. Finally, seeking strategic partnerships with experienced digital transformation providers will be essential to navigate the complexities and establish best practices for secure and compliant AI adoption.
Q3: What is HDS’s approach to AI adoption for enterprises? How do you identify value driven enterprise AI use cases? Please highlight your key AI offerings, and engagement models offered to clients.
HDS’s approach to AI adoption for enterprises
Hitachi Digital Services (HDS) is committed to embedding AI deeply and systematically into next-generation architectures. The Center for Architecture and AI (CAAI) is a key initiative designed to lead the integration of AI into core business applications. The approach emphasizes the creation of intelligent architectures that blend rule-based and reasoning-based logic to deliver measurable business impact.
Identifying value-driven enterprise AI use cases
HDS identifies value-driven enterprise AI use cases by leveraging its modular system of domain-specific, prebuilt AI agents developed within the HARC framework. These agents are designed to plug directly into real-world workflows across various domains such as Industrial, Security, Analytical, Operations, Engineering, and Cloud. This approach ensures that AI is not just an enhancement but the foundation for modern digital services.
Key AI offerings
HDS offers a range of AI solutions, including:
- Generative AI, Agentic AI, Analytical AI, and Trustworthy AI: These offerings focus on rapid prototyping, scaling, and ensuring the reliability and security of AI systems.
Engagement models offered to clients
HDS offers several engagement models to clients, including:
- Fixed price Agentic PODs: Human/AI collaborative pods delivering at unprecedented speed and scale.
- Outcome based: Tailored solutions to meet specific client needs.
- Classic T&M: Providing maximum flexibility and control for clients.
HARC.agents and R2O2.ai are foundational frameworks and accelerators that enable delivering the above service offerings.
These engagement models ensure that clients can achieve significant productivity gains and long-term value from their AI investments.
Q4: Could you briefly talk about HARC (Hitachi Application Reliability Centers) for AI. How does the integration of AI in HARC helps in further optimizing cloud workloads? Please highlight few client case examples.
HARC, or Hitachi Application Reliability Centers, for AI is a powerful new service introduced by Hitachi Digital Services on April 15, 2025. It is designed to help enterprises run AI and Generative AI (Gen AI) applications with greater reliability, efficiency, and governance. Building upon the broader HARC platform launched in 2022, HARC for AI specifically addresses key challenges faced by companies when scaling AI, such as unpredictable costs, performance degradation over time, and limited oversight of complex models like Large Language Models (LLMs).
The integration of AI in HARC pushes the boundaries of optimizing cloud workloads through several key capabilities:
- Cost Optimization: HARC for AI dynamically adjusts workloads to minimize compute waste and reduce cloud costs. This is a crucial aspect for addressing the challenge of unpredictable costs associated with scaling AI. The service aims to turn AI into a trusted, efficient engine for transformation.
- AI Observability: It continuously monitors AI behavior and performance using advanced tools and dashboards, providing the necessary insights for optimization.
- AI Resilience & Performance Management: HARC for AI ensures stable and available AI applications by monitoring reliability metrics and implementing effective recovery strategies. This directly contributes to efficient workload management by preventing disruptions and maintaining high performance.
- Operationalization of AI Frameworks: HARC operationalizes Hitachi Digital's R2O2.ai framework (Responsible, Reliable, Observable, and Optimal AI), bringing AI observability, lifecycle management, and performance tuning into real-world use. This structured approach enables organizations to monitor, govern, and optimize AI systems, ensuring they remain reliable, responsible, and cost-effective.
A central goal for Hitachi Digital Services is to apply AI in every service they provide to customers to accelerate delivery and enhance their competitive positioning, as well as designing every solution as an intelligent AI-Native solution. HARC contributes to this by helping to build trustworthy AI solutions and addressing roadblocks like cost overruns due to the proliferation and management of agents.
Client Case Examples:
HARC has demonstrated significant tangible business value for its clients by boosting efficiency and optimizing cloud costs. To date, HARC has helped over 40 Fortune 500 companies achieve reliable, secure, and cost-effective operations across various industries, including finance, healthcare, defense, and automotive.
- Global Building Management Firm: HARC helped this firm maintain a consistent 99.9% uptime and saved them over $3 million annually in infrastructure costs. This highlights how HARC's focus on reliability and efficiency directly translates into significant cost reductions in cloud infrastructure.
- U.S. Travel Tech Company: This company utilized HARC to cut revenue losses by 67% in one year. This demonstrates HARC's impact beyond just infrastructure costs, by improving operational performance and reducing financial losses related to system inefficiencies or outages.
- Overall Impact: Since its inception, HARC has cumulatively saved clients over $150 million by enhancing efficiency, minimizing outages, and optimizing cloud expenses. It also strengthened security by fixing over 150 high-risk vulnerabilities and building cloud environments that comply with top standards like FedRAMP High and DORA.
Q5: Given uncertainties in the tech services market resulting from recent tariffs imposed by US, how do you see the future of enterprise technology and AI spending over the next 12-18 months? What is the way forward for technology service providers?
Given the uncertainties in the tech services market due to recent US tariffs, I anticipate enterprise technology and AI spending over the next 12-18 months to be characterized by a focus on demonstrable business value and efficiency, rather than speculative investments. Companies will likely prioritize AI initiatives that offer clear ROI through cost reduction and efficiency gains, such as predictive maintenance, invoice processing automation, and optimized operations in industrial settings. While the confidence in achieving cost savings from Agentic automation is high, many customers are yet to realize meaningful cost reduction, and new revenue streams remain a focus area for investment in AI/GenAI technologies. The emphasis will shift towards practical application and execution, moving beyond conceptualization.
For technology service providers, the way forward involves:
- Delivering Tangible Business Value: Providers must emphasize how their AI solutions translate into measurable business outcomes, such as efficiency gains, cost reductions, and improved customer experience. The focus will increasingly shift from solely cost efficiency to demonstrating revenue generation and clear ROI.
- Building Trustworthy and Reliable AI Solutions: Addressing concerns around data governance, security, and the reliability of AI systems is crucial. Frameworks like Hitachi Digital's R2O2.ai (Responsible, Reliable, Observable, and Optimal AI) and services like HARC (Hitachi Application Reliability Centers) will be essential to ensure trust and stability in AI deployments.
- Focusing on Practical Application and Integration: Moving from theoretical possibilities to effective implementation is key. This includes seamlessly integrating AI capabilities into existing IT infrastructure and workflows13. Providers with "edge-to-core expertise" will be well-positioned to bridge the gap between IT and OT systems for comprehensive AI solutions.
- Developing Specialized Expertise in Agentic AI: While Agentic AI is not a "silver bullet for all automation problems", its unique value in autonomy, adaptability, goal-oriented execution, and handling complexity makes it a critical area of focus. Service providers should guide clients on when and how to best leverage Agentic AI for specific challenges, potentially enhancing existing AI applications with agentic workflows.
- Industry-Specific Solutions: Tailoring AI strategies to cater to the diverse expectations and applications across different sectors, such as private equity, manufacturing and logistics, and mobility, will be vital.
- Upskilling the Workforce: Empowering employees with AI co-pilots and knowledge systems to boost productivity and foster continuous learning will be an internal focus for service providers, enabling them to better serve their clients.
