Anish Jacob is the Chief Information Officer and Head of FinTech at Jenius Bank, the retail banking division of SMBC MANUBANK, a SMBC group company in the USA. He is a visionary technology leader with over 25 years of experience in technology leadership and digital transformation within banking and financial services. As a founding force behind Jenius Bank, he played a crucial role in launching a new retail banking division for SMBC in the USA, creating a dynamic technology team and infrastructure poised for future growth. Formerly, as CIO at SMBC MANUBANK, he spearheaded enhancements in technology capabilities and led transformative initiatives. His expertise spans across business-aligned tech, data, and AI strategy, digital applications, cloud, platform engineering, cybersecurity, corporate tech, workplace tech, FinTech partnerships, and strategic technology management, all enriched by a robust foundation in leadership, architecture, mobile, digital experiences, and data from his previous experiences at American Express, Start-ups, and IBM. As a member of Executive Committee and leadership team, he works with business leaders, CxOs, and stakeholders to provide technology and data solutions that support the company’s vision, mission, and goals. He holds multiple patents and serves on the advisory board for Arizona CIO.
In a recent interview with CIO Magazine, Anish Jacob discussed his experience with digital transformation and AI. He shared his views on integrating AI, digital and AI transformation, and many more.
In your experience, what are the most critical factors for a successful digital transformation in today’s organizations?
In my experience leading digital transformation, you need to clearly understand your current state and your target state. Develop a clear vision, secure executive sponsorship, align with business goals, and create a shared vision that can cascade from leadership to all levels. Transformation requires a cultural change across the organization, fostering innovation, readiness to change, willingness to take calculated risks, empowering people, and breaking silos.
Another key aspect of digital transformation is bringing agility, modular architecture, and advanced technologies to your infrastructure to scale both vertically and horizontally. It is also important to adopt a customer-centric mindset instead of a product-centric approach, understanding your target customers’ evolving needs, and enhancing customer experience and personalization to engage and grow with them.
Additionally, making data-driven decisions with high-quality, well-governed data and modern data infrastructure is crucial. Build trust in data across the organization and enable the development of meaningful insights in a timely manner. Business process optimization and automation can simplify and bring efficiency to your operations.
Talent and skill development is also essential, such as hiring and upskilling your workforce for digital, cloud, data, and AI roles. Lastly, develop measurable KPIs and track progress to monitor and adjust the transformation to achieve your objectives.
How do you see the role of AI evolving from a tool for efficiency to a driver of strategic differentiation?
In my opinion, AI has transcended its role as a mere tool for improving efficiency; it has evolved into a catalyst for innovation, creating entirely new avenues for business value creation. AI offers organizations the power to drive efficiency through the automation of repetitive tasks, enhancing process accuracy, and reducing operational costs. Examples include chatbots, fraud detection systems, and AI agents that augment human decision-making with predictive analytics. In today’s environment, AI-driven efficiency has become a fundamental necessity. However, the true differentiator lies in harnessing AI for strategic growth. Imagine reinventing business models or conducting AI-powered risk assessments. Envision enhanced customer experiences through hyper-personalization, emotional AI, and dynamic user interfaces.
AI is not just a tool; it’s a force that accelerates innovation by enabling businesses to identify new opportunities and develop novel products and services. In a recent conversation with a VC firm in the B2B space, they revealed that 70% of their portfolio consists of AI or AI-related start-ups at various stages. This underscores the immense innovation happening in the AI realm.
What challenges do organizations face when integrating AI and data systems into legacy infrastructure, and how can they overcome them?
Integrating AI and modern data systems into older systems brings several challenges for many organizations. The main challenges include limited computing power and infrastructure in legacy systems, data silos, and poor data quality. AI models need large amounts of high-quality data, which may not be easily accessible due to fragmented data across old systems. Compatibility issues also arise as older technologies may not work well with new AI frameworks, leading to integration complexities. Additionally, there can be security and compliance risks, skill gaps, and resistance to change.
To overcome these challenges, consider migrating to the cloud to handle AI workloads, modernizing your data foundation, creating an AI-driven data strategy, and using hybrid architecture with APIs and middleware to connect modern AI and data systems to legacy systems. Leveraging cloud-based AI services can provide scalable infrastructure and pre-trained models. Investing in training and developing AI skills within the organization, along with implementing robust security measures and regulatory compliance through policies and governance, can also help address these issues.
What steps should companies take to ensure ethical governance, transparency, and fairness in AI applications?
To ensure ethical governance, transparency, and fairness in AI, organizations need a strong AI governance framework and accountability. This includes setting clear AI policies and procedures for development, deployment, and controls that follow ethical standards, privacy, and bias mitigation.
An AI board or governance structure with clear oversight roles is necessary for responsible AI technology development and deployment. Establish an ethical framework that aligns with global standards like OECD, NIST, and the EU AI Act, prioritize fairness, transparency, privacy, and foundational values relevant to your industry and region. Ensure data responsibility by auditing datasets for bias, representativeness, and provenance, using privacy-preserving techniques like anonymization and differential privacy (DP). Emphasize explainability in model design and tools, whether developed in-house or purchased, and ensure users understand how decisions are made, especially in highly regulated industries like finance and healthcare. Maintain human oversight and control on critical decisions, so AI systems can be overridden when necessary. Regularly monitor risk and impact, perform impact assessments, and continuously audit models to identify and mitigate potential harms. Develop a strong culture of training and ethical awareness across the organization by educating the workforce on AI ethics and encouraging ethical escalation when concerns arise. Be transparent with data usage, capabilities and limitations with your consumers, and stakeholders.
What skills and mindsets do leaders need to effectively guide digital and AI transformation efforts?
In my perspective, to lead digital and AI transformation, leaders need to combine technical skills, visionary ideas, and teamwork. Start with a clear vision of how digital and AI technologies can add new business value, drive growth, and improve customer experiences. Create a strategy that uses digital and AI to motivate teams with clear goals. Leaders should have strong digital and data skills to work well with stakeholders and technical teams, and to make smart strategic decisions.
Be flexible and see change and uncertainty as opportunities. Encourage trying out new things within what’s possible for the organization and promote quick learning. It’s important to practice ethical and responsible AI by being fair, transparent, and accountable. Work together across different departments, break down barriers between business, tech, and others, and set shared goals that align with common business outcomes.
Invest in people through upskilling and nurturing a growth mindset to prepare the workforce for continuous learning and transformation. Finally, tie all efforts to measurable business outcomes related to ROI and customer impact.
With AI evolving so quickly, how can organizations continuously reskill their workforce without falling behind?
Continuous learning should be part of your organization’s culture. AI will change how we work in the future, so we need to identify skill gaps and make upskilling part of employee development. We can achieve this through partnerships with learning providers, platforms, conferences, and knowledge-sharing sessions. Leaders should identify roles at risk of disruption and important future skills to prioritize focused learning programs.
To stay ahead, the organization should reward learning behaviors, set goals for ongoing development, and align career goals with future skill needs. Learning should be seen as a strategic investment, not a cost.
What do you believe are the most promising innovations in AI and data that will shape the next decade?
AI is a rapidly evolving field, and the next decade will bring big changes. In my view, these three areas would be leading the way: Generative AI, Autonomous Vehicles, and Quantum Computing.
Generative AI can create new content like images, code, music, and text, which were once thought to need human creativity. This technology is pushing the limits of innovation and automating tasks in many industries.
Self-driving cars are about to change transportation, promising safer and more efficient travel, less traffic, and fewer accidents.
Quantum computing could change the world by solving problems that regular computers can’t handle. In AI, quantum computing could speed up and improve calculations, leading to big advancements in drug discovery, material science, and climate science. In financial industry, it could improve trading strategies, financial modeling etc.
How is generative AI influencing your industry, and what opportunities or risks does it present?
I work in the banking industry. In my view, Generative AI can revolutionize the banking industry by elevating customer experience, driving operational efficiency, igniting strategic growth, and fortifying defenses against fraud and cyber threats. Banks could harness generative AI to provide hyper-personalized financial insights, deploy intelligent chatbots offering 24/7 support with natural human-like interactions, and enhance fraud detection and cyber defense capabilities. Additionally, banks could utilize agentic AI to automate complex business processes, resulting in greater operational efficiency and faster decision-making.
However, this transformation comes with its own set of risks. Bias in AI-generated decisions, data privacy concerns, and evolving regulatory frameworks can pose significant compliance challenges. There is also the potential risk of eroding trust if customers receive AI-generated responses that lack transparency or empathy.
Despite these challenges, the opportunities are immense. GenAI empowers banks to innovate rapidly, enhance customer experiences, reduce costs through efficiencies, and stay competitive. In my opinion, to succeed, banks must balance innovation with responsible and ethical AI practices, ensuring transparency and human oversight where needed.