ITTech Pulse Exclusive Interview with Michael Jacobs, Head of Social Innovation at IBM

Michael Jacobs, Head of Social Innovation at IBM, shares his journey from leading capital projects at MTA New York City Transit to advancing AI-driven sustainability and equity initiatives globally, in this exclusive conversation with ITTech Pulse.

Can you walk us through your path from NYC Transit projects to leading IBM’s social innovation efforts, and what pivotal moments ignited your focus on tech-driven sustainability and equity?

I began my career as a consultant focused on technology systems for public sector clients and later served as Superintendent for Capital Projects at NYC Transit, where I saw firsthand how infrastructure and technology shape access and opportunity. Those experiences reinforced my belief that well-designed systems can improve essential services and scale economic opportunity for entire communities. At IBM, I now lead social innovation efforts that apply AI and hybrid cloud to help nonprofits, governments, and academic institutions address environmental and economic challenges. Throughout my career, acute shocks including man-made and natural disasters have demonstrated the need for effective decision support tools, which has been a focus of many IBM Impact Accelerator projects.

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As Head of Social Innovation, what key lessons from past IBM Impact Accelerator cohorts, like resilient cities, shape your approach to scaling AI for vulnerable communities globally?

One of the most important lessons is that scaling AI means designing for real adoption and durability, not just a successful pilot. In past cohorts, including work focused on resilient cities, we’ve seen that solutions scale when they’re co-designed with organizations closest to the problem, grounded in local data and constraints, and built for long-term use. We also define measurable outcomes early so we can assess what’s working, make adjustments, and responsibly extend impact to other communities without losing local relevance.

In the IBM Impact Accelerator RFP, we covered targeting AI education for underserved groups. How does this build on prior successes in agriculture and water management to drive equitable access?

In agriculture and water management, we’ve seen how AI can help communities facing environmental stress make better, real-time decisions, whether that’s smallholder farmers accessing localized climate insights or communities improving water resource planning. The common thread isn’t the sector but using AI to close information and systems gaps that limit access and opportunity. With education and workforce development, we’re applying that same model of co-designing practical tools that help people learn more effectively, navigate career transitions, and strengthen the pathways that connect learning to quality jobs, while helping institutions modernize the systems that prepare people for work.

Our research highlights IBM’s hybrid AI stack with watsonx, Red Hat OpenShift, and open-source tools in accelerators. What’s the core architecture enabling scalable AI education solutions for nonprofits?

The architecture is built around IBM’s AI and hybrid cloud capabilities, including watsonx, IBM Cloud, IBM Quantum, and Red Hat open-source technologies. That hybrid approach allows solutions to run across different environments and integrate with the systems nonprofits already use, rather than requiring them to start from scratch. Through IBM Garage, we co-design and prototype in Phase 1, then build and implement in Phase 2, ensuring solutions are practical, helpful, measurable in impact, and built for broader implementation. The goal is to combine enterprise-grade AI with open, flexible infrastructure that works in real-world nonprofit settings.

How does IBM measure success in the Impact Accelerator, from AI model training metrics to real-world outcomes like literacy gains, and what data pipelines ensure transparency for partners?

The IBM Impact Accelerator’s top metric is the number of people who directly benefit from the work, which to date is approximately 2.5 million people globally. This is measured by our grantees in-line with project scopes, such as the number of people served by improved water management programs and reviewed by IBM. We also track the Net Promoter Score for Accelerator-developed solutions, to measure and improve upon helpfulness for users. Throughout projects, we assess whether AI solutions are performing reliably and responsibly within the intended environment. Organizations are selected in part based on their ability to define and track measurable impact, and during co-design we establish clear success metrics. We also measure real-world outcomes defined at the start of each engagement. We build and implement solutions that allow partners to monitor performance and outcomes over time using IBM technologies, with shared visibility into data and results throughout the engagement.

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For our readers in edtech and CSR, what AI education tools or frameworks from this RFP should they explore to upskill workforces in emerging markets?

For edtech and CSR leaders, the opportunity is less about a single tool and more about a framework. Through this RFP, we’re encouraging AI-driven solutions that personalize learning and career navigation, connect learners to real job opportunities, and help institutions modernize how they prepare people for work. That includes, for example, AI-enabled teaching and assessment tools, data platforms that link education and employment systems, and responsible AI approaches that support broad access. The goal is to combine flexible, open technologies with strong local partnerships so solutions can adapt to different contexts globally.

What actionable advice do you have for our readers, educators or leaders embarking on AI-for-education initiatives amid this RFP, to maximize social return on tech investments?

First, start with the problem. Be clear about the learner or workforce gap you’re trying to close and define measurable outcomes before introducing technology. Second, co-design with educators, employers, and the communities you serve. Adoption depends on building solutions that reflect real constraints identified on the ground. Third, invest in responsible AI and governance from the outset, including transparency around data use and clear accountability for results. Finally, think beyond pilots. Build for integration with existing systems and long-term sustainability so your investment continues delivering impact over time.

Thank you, Mr. Michael, for taking the time to share your insights with us.

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