Topic: Technology and AI

Solution Spotlight: IBM SkillsBuild

Posted on by Brianna Provenzano

As emerging technologies continue to shape the future of work, the skills people need are evolving just as quickly. AI, cybersecurity, cloud computing, and data analytics are changing how work gets done across sectors, while employers place increasing value on practical experience, durable skills, and the ability to keep pace as technology changes.

IBM SkillsBuild was designed to meet those needs, engineered around the simple premise that more people should have access to clear, practical ways for building the skills that evolving and emerging roles require. 

A free education program aimed at increasing access to technology education, IBM SkillsBuild can be accessed directly through SkillsBuild.org or through the partner organizations already working with learners. Those partners — including schools, universities, nonprofits, government agencies, and employers — can help tailor IBM SkillsBuild content to the learner groups they serve, making learning pathways more relevant to local needs and workforce opportunities.

At its core, IBM SkillsBuild reflects IBM’s belief that access to skills can expand access to opportunity. By combining digital learning with credentials, mentorship, applied projects, and partner-led support, the program aims to help learners both understand emerging technologies and see a realistic path forward in a changing economy.

“The technology skills may get you in the door, but those durable skills are what will keep you there.”

— Lydia Logan, Vice President, Global Education and Workforce Development, IBM

The Problem:

The pace of technological change has created a widening gap between the skills workers need and the pathways many learners have access to. Employers increasingly need workers with fluency in fields like data analytics, cybersecurity, cloud computing, AI, and other emerging technologies, but traditional education and hiring systems do not always move quickly enough to help learners build, validate, and apply those skills in real time.

That disconnect is especially consequential for those who have faced historical barriers to accessing technology careers. For many learners, the challenge lies in knowing where to start. Many learners reported to IBM that they did not see a career in technology as a viable option for themselves, in part because they believed training would be too expensive — signaling both a workforce challenge and an opportunity gap. 

The lightning-fast evolution of AI has only made skilling challenges more acute. And as IBM has recognized, the problem won’t be solved through technical training alone; learners also require durable workplace skills, career-relevant digital credentials, hands-on experiences, and support from real people who understand what prospective employers are actually looking for.

In our current environment, the problem is not simply helping learners to acquire a fixed set of skills, but also to help them build the confidence, adaptability, and good judgment they will need to keep learning as the world of work continues to be transformed.

The Solution:

IBM designed IBM SkillsBuild as a free technology education program that gives learners access to technical and workplace skills training, digital credentials, mentorship, and hands-on projects.

The program offers multiple levels of engagement, from webinars and workshops to cohort learning experiences, capstone projects, and industry-recognized certificate pathways. As AI reshapes entry-level work, these hands-on projects give learners opportunities to apply what they have learned and build a portfolio of work they can share with potential employers. Unlike many learning programs, IBM SkillsBuild also makes its IBM-branded digital credentials available at no cost, giving learners a way to validate their skills without taking on the added financial burden of paying to earn credentials. 

That kind of flexibility is integral to the model. IBM collaborates with organizations to identify the learning pathways and experiences most relevant to their learners, while IBM’s global network of Academic Ambassadors serves as mentors and guest lecturers, bringing industry expertise directly into the learning experience and helping students build their professional networks. In that way, IBM SkillsBuild ultimately functions as a partner-enabled learning ecosystem designed to help people build, validate, and apply skills in ways that connect to workforce opportunities.

“It’s very hard, if not impossible, to know what will be needed even two years from now. Maintaining that mindset of continuous learning is critical.”

— Lydia Logan, Vice President,  Global Education and Workforce Development, IBM

Why It’s Different: 

IBM SkillsBuild is designed around the understanding that learning does not happen in a vacuum. Through its collaborations with schools, employers, and other institutions, IBM works with the organizations that already have a working understanding of the learning needs of their communities, allowing for more bespoke pathways and formats. 

IBM SkillsBuild is not positioned as an alternative to a university education; it can also complement and strengthen existing degree pathways. University partners such as University College London integrate IBM SkillsBuild digital credentials into courses, allowing students to earn industry-recognized credentials as part of their degree programs and giving employers a fuller picture of what graduates know and are able to do. Other universities might incorporate IBM SkillsBuild into semester-long capstone projects rather than simply directing students to standalone online courses. Through a collaboration between IBM and Salesforce, learners can also complete a joint IBM SkillsBuild/Trailhead pathway, earn a joint credential, receive a coupon for a Salesforce certification, and potentially enter a hiring pool of Salesforce clients.

In turn, IBM contributes its technology expertise, learning content, employee mentors, and industry knowledge. That combination of access, validation, and partner-led support comes together to allow IBM SkillsBuild to function as a way for learners to build skills, demonstrate them, and connect that learning to real-world workforce expectations. 

Impact Highlights: 

  • IBM has committed to skilling more than 30 million people globally by 2030
  • More than 22 million learners reached through IBM SkillsBuild and other programs to date
  • IBM mentors help learners build professional networks and understand industry expectations.
  • IBM SkillsBuild is consistently updated to map cleanly onto the skills employers are actually hiring for.

“We were in an era where you had the computer and data science majors, and they were the builders and everyone else was a user. Now we’re in a world where everyone can be a builder.”

— Lydia Logan, Vice President,  Global Education and Workforce Development, IBM

Key Enablers:

  1. IBM’s internal technical expertise. IBM SkillsBuild draws from IBM’s own internal knowledge base, which helps ensure that learners are building skills that accurately reflect where technology and employer needs are headed.
  2. Free credentials. Offering IBM-issued digital credentials at truly no cost gives learners a more viable way to validate their skills and demonstrate them to employers.
  3. Human support from IBMers. By giving learners access to people already working in the industry, IBM SkillsBuild helps participants build professional networks and connect to real career pathways.
  4. Ecosystem partnerships. Through partnerships with universities, nonprofits, governments and employers, IBM is able to explore ways to connect completed learning pathways to more certifications, hiring pools, and employer demand — helping learners build evidence of their skills and helping employers recognize talent more seamlessly.
  5. Responsible AI and workforce frameworks. IBM SkillsBuild incorporates outside frameworks and best practices from organizations like the EdSafe Alliance, the World Economic Forum, and Cisco-led consortium work on ICT jobs. Those inputs help the program stay grounded in responsible technology use while keeping pace with how AI is reshaping education and work.

Scaled Impact:
In addition to offering direct support for learners, IBM SkillsBuild is designed to function as a global skilling infrastructure that can be adapted across geographies and learner groups. That scale is what allows IBM to align IBM SkillsBuild with emerging workforce priorities across different markets. In the U.S., for example, IBM has committed to skilling 2 million people on AI by the end of 2028; in India, the company has committed to skilling 5 million people in AI literacy and fluency.

IBM is also working to make IBM SkillsBuild a stronger bridge between education and opportunity over time. By mapping to the capabilities its own talent team looks for in new hires, IBM SkillsBuild points toward a workforce approach in which degrees, credentials, and demonstrated competencies can work together to give employers a fuller understanding of what learners know and can do.

Lessons for Other Leaders:

  1. Build from what your organization already does well. IBM SkillsBuild draws on IBM’s existing core strengths: technology expertise, skilled employees, and a deep understanding of workforce needs. Start with the assets you already have.
  2. Make credentials attainable without additional barriers. Free courses might help learners begin, but credentials help them validate their skills.
  3. Lean on collaborations instead of building everything from scratch. Access improves when programs are designed around learners’ organic starting points.
  4. Teach responsible use alongside fluency. As more and more people gain access to powerful tools like AI, leaders need to be intentional about pairing skill-building with safety, security, and responsible use frameworks. 
  5. Treat continuous learning as part of the model. In a workforce shaped by AI and other fast-moving technologies, no program can be built around a fixed endpoint. The goal should be helping people stay curious, adaptable, and ready to keep building new skills over time.

Solution Spotlight: Salesforce Accelerator – Agents for Impact

Posted on by Brianna Provenzano

In January 2023, leaders at Salesforce looked around and saw a world that was changing. Defying the initial predictions of its detractors, crescendoing ChatGPT usage had already pushed the platform past 100 million active monthly users only two months after its launch, and LLMs in general were showing every sign of being poised to take the world by storm. But at the same time, nonprofits were contending with what has by now become a familiar reality: rising demand, shrinking resources, and limited capacity to experiment with emerging technology.

Seeking to address those widening funding gaps and deficits of technical support, Salesforce launched the Salesforce Accelerator — Agents for Impact: a cohort-based program designed to help nonprofits responsibly adopt AI through a combination of unrestricted funding, technical coaching, pro-bono support, and access to Agentforce, Salesforce’s enterprise agentic AI solution. By leveraging its organizational superpowers, Salesforce set out to help nonprofits experiment with, implement, and scale the AI solutions that hold the potential to help them better deliver on their missions and meet their current demand.

“Historically in the social sector, the organizations that are on the front lines are left behind in technological revolutions.”

— Amy Guterman, Senior Director, AI for Impact at Salesforce

The Problem: Nonprofits are under increased pressure to adopt transformative new technologies at a moment when they are also contending with persistent resource constraints. While AI tools hold the potential to rapidly reshape how organizations operate, many frontline organizations lack the technical expertise, implementation support, flexible funding, and organizational capacity to safely experiment with them — despite the irony that they often serve the very communities most likely to be affected by the emergence of new tech.

Those challenges are compounded by a philanthropic landscape that has historically tended to underfund operational infrastructure and devalue early-stage experimentation, leaving many nonprofits without the resources needed to responsibly test and implement new tools.

The Solution: Salesforce designed the Accelerator program as a comprehensive support system that uses three primary levers to support nonprofits: unrestricted grant funding (typically between $200,000 to $400,000); access to Salesforce’s proprietary technology, including Agentforce; and hands-on technical guidance from Salesforce’s own employees. Through an 18 month, cohort-based model, participating nonprofits receive strategic coaching, implementation support, and training in the form of a dedicated six-month curriculum on issues like governance, data strategy, and agentic AI best practices that they can convert into mission-aligned deployment.

That pro bono support has become one of the program’s defining features. Rooted in Salesforce’s “1-1-1” philanthropic model — which commits 1% of the company’s equity, product, and employee time to supporting nonprofits and schools — participating organizations are paired with volunteer technical architects, project managers, and solutions engineers from Salesforce who work alongside them as ad hoc consultants throughout the implementation process. According to Salesforce, these volunteers function not only as technical advisors but as strategic thought partners, helping nonprofits build the confidence, governance structures, and strategy needed to use AI tools effectively and responsibly.

Why It’s Different: 

“A check is great. The technology is great. But unless you have the pro bono volunteers — the technical experts helping you best use the funding or best use the technology — you either don’t use it, or you burn through all your funding hiring consultants before you’ve even built the solution.”

— Amy Guterman, Senior Director, AI for Impact at Salesforce

Rather than simply providing funding or software licenses, the Accelerator was intentionally designed around the understanding that many nonprofits lack not only the internal capacity to independently navigate rapidly evolving AI systems, but the space to experiment, navigate challenges, and determine ROI prior to fundraising. This “risk-tolerant” support, in particular, is what sets the Accelerator apart: Rather than requiring organizations to arrive with fully articulated use cases, the program was designed as a space for learning and experimentation — a proving ground that is critically needed given the rapid pace of technological change.

And in the spirit of that learning, the Accelerator also has broader ambitions to become an ecosystem that acts as a central repository for those collected insights around responsible AI implementation, allowing organizations to share in each others’ learnings, avoid duplicating each others’ mistakes, and build on one another’s successes over time.

Impact: 

  • $16 million deployed through the Accelerator to date
  • 100% of participating nonprofits reported increased AI capacity
  • 94% of organizations predicted that the Accelerator would have a meaningful impact on mission delivery
  • The Accelerator has expanded internationally, including new India- and UK-based cohorts
  • 94% of Salesforce volunteers reported improved AI and agentic-AI skills through participation, a powerful secondary outcome

Some of the clearest evidence of the Accelerator’s impact has been reported anecdotally by the program’s early participant organizations:

College Possible: College Possible — an organization focused on expanding college access by helping first-generation students finish post-secondary education — reported that the Accelerator had helped to usher in a 400% increase in its coach-to-student efficiency and significantly reduce costs per student, even amid budget constraints that initially threatened to derail operations.

Good360: Another participant organization, Good360 — which distributes in-kind donations from major retailers and corporations to disaster-affected communities — reported using AI tools developed through the Accelerator to save their disaster recovery team over 1000 hours annually, connecting donated goods with communities 3x faster. 

“Our premise is that even if the solutions the cohorts are developing aren’t successful, at least they’re building the capacity and the skills and that thought process to their other work, so that the skills around AI capacity are durable to other projects in the future.”

— Amy Guterman, Senior Director of AI for Impact at Salesforce

Key Enablers:

  1. Salesforce’s 1-1-1 model: Baked into Salesforce’s core value system, the 1-1-1 model grants employees 56 hours of heavily-encouraged, paid volunteer time off annually, which is what allows the company to mobilize teams of technical experts to work directly with participating nonprofits. That pro bono support has proved especially valuable in helping organizations navigate the “fuzzy front end” of AI adoption: defining viable use cases, building governance frameworks, pressure-testing strategy, and developing confidence around responsible implementation before investing significant resources into full deployment.
  2. Risk-tolerant capital: While traditional philanthropy is often hesitant to fund technological experimentation before the outcomes are fully proven, the Accelerator was specifically designed to absorb some of that early uncertainty and give nonprofits the room to experiment responsibly before needing to demonstrate clear ROI.
  3. Embedded technical expertise: According to Salesforce, the real secret sauce of the Accelerator is the integrated recognition that it’s not just the funding and technology itself that nonprofits lack, but sustained support around enablement. By pairing participating organizations with Salesforce volunteers who can offer personalized guidance, nonprofits are better equipped to clarify strategy, pressure-test ideas, develop governance frameworks, and build confidence around responsible AI deployment before investing significant resources into full-scale deployment.   

Future Plans:
Beyond acting as a nonprofit support program, Salesforce’s systems-level ambition is for the Accelerator to help shift how the philanthropic sector approaches the adoption of emergent technologies. Over time, the company hopes that the model will encourage more risk-tolerant investment in AI experimentation while simultaneously creating stronger mechanisms for nonprofits to share lessons they’ve learned, their implementation strategies, and any evidence they’ve seen of impact. According to Salesforce, the ultimate goal is to reduce duplicative efforts across sectors and help organizations build on existing successes rather than repeatedly funding similar early-stage experiments in isolation.

Lessons for Other Leaders:

  1. Pair funding with implementation support. Providing capital or technology alone is often insufficient for organizations navigating complex technological change. Embedding technical guidance, coaching, and governance support is the lever that drives long-term sustainability and helps organizations dealing with persistent capacity constraints take their impact to the next level.
  2. Treat operational technology as mission-critical. As AI tools and data systems become increasingly embedded in organizational workflows, investments in data systems and implementation support should be treated as core mission support.
  3. Leverage your area of corporate expertise. Rather than creating a generic grant program, Salesforce designed the Accelerator around its expertise in AI and technical product infrastructure, and also leveraged its deeply rooted culture of employee volunteerism — all assets where it can provide unique value. Relying on institutional superpowers and values embedded deeply in the DNA of the company has allowed for more hands-on guidance and proficiency than capital alone could ever provide.
  4. Create mechanisms for organizations to share lessons learned and observed successes. Cohort models, peer exchange, and open sharing of implementation lessons can help organizations avoid duplicative experimentation and accelerate adoption of proven practices.
  5. Build systems that can adapt to a rapidly changing world. AI evolution shows no sign of slowing down, and Salesforce’s program has had to adapt in kind. What began as “AI for Impact” quickly evolved into “Agents for Impact” as the technology landscape shifted towards prioritizing agentic AI, and continued evolution will almost surely be necessary down the line. The ability to remain nimble and continually adapt support structures is key to longevity and sustained impact, particularly when it comes to something as volatile and fast-evolving as AI.

Reimagining Workforce Readiness: Why Mental Health and Human Skills Will Define Success in the AI Economy

Posted on by Brianna Provenzano

What if the greatest barrier to workforce readiness wasn’t a lack of technical skills, but the absence of systems that help young people adapt in an ever-changing world? As AI continues to reshape industries and traditional career pathways, the future of workforce readiness may depend less on what young people know and more on how effectively they collaborate, communicate, regulate stress, and navigate uncertainty. Emotional intelligence, mental health, and relationship-building are increasingly emerging not only as “soft skills,” but as essential ones.

On June 10, NationSwell convened a group of cross-sector leaders for a virtual roundtable that moved beyond compliance-driven approaches to career readiness, instead examining how relationship-centered environments, identity safety, and well-being practices hold the potential to strengthen resilience, deepen engagement, and improve long-term workforce outcomes. Some of the most salient takeaways from the discussion appear below:

Key takeaways

Invest in early career opportunities as a mental health and workforce strategy. Employment can serve as one of the most effective antidotes for mental health challenges, addressing immediate needs, like a paycheck and a support network, and unlocking long-term potential. Organizations should invest in internships, cross-departmental learning, and other early career opportunities as a means to support young people’s wellbeing and build a stronger workforce pipeline.

Reframe “soft skills” – especially relationship-building – as critical and durable work competencies. As AI takes on more knowledge-based tasks, human connection becomes an increasingly valuable differentiator. Mentorship, peer coaching, cross-functional apprenticeships, and volunteer opportunities can strengthen relationships, improve mental wellbeing, and support workforce development. Participants also emphasized the unique “win-win-win” of employee volunteering and mentoring through nonprofits, which can generate benefits for individuals, communities, and employers altogether.

Embed emotional intelligence across workflows, trainings, and culture. As young people increasingly turn to AI for mental health and career support, they miss opportunities to build socio-emotional skills, like self awareness, social awareness, and relationship management. Organizations can integrate emotional intelligence into everyday work and interactions (especially feedback processes) to strengthen human collaboration, trust, and help-seeking. Dedicated culture roles – such as culture coaches, trauma-informed specialists, and wellness buddies – can then amplify this programming.

Design employee engagement programs that help young people navigate stress, uncertainty, and change. Young people face a number of stressors – from climate anxiety and loneliness to social expectations, relationship violence, and rapid technological change – leaving them feeling overwhelmed and powerless. Leaders emphasized that civic engagement, community participation, and knowledge can help transform this anxiety into agency by giving young people a greater sense of purpose or efficacy over their environment. 

Tailor workforce training to meet educators and managers where they are. Teachers and managers, especially those operating in under-resourced systems, face their own bandwidth constraints and mental health challenges related to AI use and integration. Effective training requires understanding the different realities, priorities, and constraints across sectors, and co-developing tools and resources that range from low-lift, integrable touchpoints to deeper, long-term engagements.

Establish AI guardrails that help young people use AI safely and effectively. With AI regulation, ethical standards, and safeguards still evolving, many young people feel a general anxiety over the technology and many organizations lack clear guardrails for how to best support youth mental health and workforce development. As guardrails standardize, organizations can continue to promote basic AI literacy and responsible, ongoing learning about AI’s impacts and risks. 

Leveraging AI & Technology to Connect More Communities to Quality Healthcare

Posted on by Brianna Provenzano

Technology is reshaping healthcare access, but progress is uneven. AI, digital tools, and data platforms have the potential to extend care to underserved communities, address workforce shortages, and improve outcomes. At the same time, gaps in infrastructure, trust, and governance risk widening disparities rather than closing them.

On May 5, NationSwell convened a group of leaders from the healthcare, technology, philanthropy, and the social sectors to unpack how AI and technology can be used to connect more communities to quality care. Together, the group focused on practical strategies for deploying technology responsibly, building partnerships that center community needs, and ensuring that innovation strengthens equity, affordability, and trust in healthcare systems. Some of the most salient takeaways from the discussion appear below:

Key Takeaways:

Ensure that technology empowers community health workers as relationship builders. AI and tech are most valuable when they augment the work of community health workers rather than substitute it. The trusted, relational role that CHWs play in their communities is the irreplaceable foundation of effective care connection. All technology deployed should be designed to protect and extend that capacity.

Design AI tools with CHWs and communities. The most responsible AI adoption in healthcare requires community health workers and the communities they serve to be active participants in tool design. Without mechanisms for feedback, bias mitigation, and accountability, technology risks widening the very health inequities it aims to address.

Prioritize data security and trust as foundational. Organizations working at the intersection of technology and community health must treat data stewardship with the same rigor as the healthcare system itself. Achieving certifications, committing to governance structures, and designing platforms that bring AI into the human loop are essential to maintaining the trust that makes community engagement possible.

Address the full picture of need, not just point-of-care data. Existing data systems often capture only what brings someone into the healthcare system, missing the co-occurring social determinants of health that shape outcomes. Continuous, relationship-based data collection with the support of technology can surface a more complete and actionable picture that enables both better resource connection and effective advocacy.

Invest in AI literacy and critical capacity for the CHW workforce. Community health workers need both the practical skills to use AI tools effectively and the critical frameworks to evaluate how those tools are designed and deployed. Approaches that build competency while also developing CHW voice in governance and advocacy are critical to ensuring that the workforce shaping communities is not left behind as technology advances.

Build toward interoperability and sustainable models. For community-based organizations to achieve lasting impact through technology, they must be able to integrate securely with healthcare payer systems. Achieving interoperability opens pathways to revenue that sustains mission-driven work in ways that philanthropic funding alone cannot.

Shift the question from “can we?” to “should we?” Across sectors, the most important orientation toward AI adoption is not simply capability, but intentionality. Keeping the focus on how technology can better serve CHWs, and continuously asking whether each application advances their interventions, is the compass that keeps this work on the right path.

NationSwell op-ed: Predicting the Future of Work

Posted on Jacob Lepiarz and Allie Mahler

We are currently living through one of the most profound shifts in the history of work. As AI, automation, and other emerging technologies redefine jobs, skills, and career pathways wholesale, leaders across sectors are being called to meet these industry-wide undulations head-on and help shape what comes next.

That imperative is at the heart of NationSwell’s new Workforce Innovation Collaborative — a cross-sector effort designed to help leaders explore emerging workforce trends and co-design scalable solutions for a more future-ready and inclusive economy. Through shared learning, strategic dialogue, and collective action, the Collaborative aims to create the kind of trusted space leaders need to navigate uncertainty and create a future-ready workforce where every person has the skills, opportunities, and support to succeed.

To mark the launch of that work, NationSwell invited leaders from the Collaborative to respond to a shared prompt:

Which emerging signals are giving you the most optimism about the future of work right now? And where do you currently see the greatest opportunity to build a system that is more responsive to where work is headed next?

Although their responses reflect different vantage points, they converge around the common belief that the future of work will be shaped by how well leaders connect learning to real opportunity, pair innovation with inclusion, and design workforce systems that can adapt as quickly as the world around them changes.

Prompt: Which emerging signals are giving you the most optimism about the future of work right now? Where do you currently see the greatest opportunity to build a workforce system that is more responsive to where work is headed next?

“We are at an inflection point in the future of work, and I believe the greatest source of optimism and opportunity is in mastering the art and science of building truly responsive workforce systems.

The science is the strategic leveraging of predictive labor market intelligence. By shifting away from reactive measures, we can now leverage data and insights to anticipate skill demands driven by global trends. Our data provides the scientific rigor needed to pinpoint future talent shortages, standardize risk indicators, and replace guesswork with reliable, real-time insights, allowing us to accelerate our workforce investments across the globe.

However, the true opportunity — the art — lies in translating those insights and data into hyper-local execution that allows us to co-create with the communities we work in. This essential human-centered approach ensures our work doesn’t just fill a business gap, but actively builds equitable, transparent systems that deliver a net-positive impact in local communities. We achieve this by cultivating bespoke, long-term partnerships with community leaders, educational institutions, and nonprofits. 

Linking our global data-driven approach to local trust and co-creation is the systemic approach necessary to ensure our interventions foster equity and accessibility, building the sustainable, resilient workforce the future demands.”

— Courtney Williams, Global Workforce Development & Labor Market Intelligence, Google

“Across the Design and Make industries, I’m seeing promising workforce solutions that connect access, applied skills, and real hiring pathways. It’s no longer enough to train people on tools in isolation — what’s emerging now are integrated models that build capability in real workflows, validate those skills through industry recognized credentials, and link learners directly to opportunity. That’s how we ensure both students and experienced professionals can adapt and thrive as technology reshapes the future of work.”

— Kate Buchanan, Workforce Innovation & Investment Lead, Autodesk Foundation

“Right now, what gives me the most optimism about the future of work is the growing consensus that, as AI reshapes roles, human-centric skills — critical thinking, communication, and creativity — matter more, not less. It’s really important that optimism is matched with action in this moment, and through Barclays LifeSkills, our programs are helping people to develop these skills in order to differentiate themselves for current and future roles.

As we look at the workforce development sector, the greatest opportunity is to build a system that keeps pace with change by connecting learning to work earlier and more often, and by updating training as employer needs evolve faster. That means scaling employer-aligned earn-and-learn pathways — apprenticeships, fellowships, internships and project-based work — so learners graduate with an increased level of experience. It also means widening access to growth sectors, including AI-enabled roles and the skilled trades, where we continue to see strong demand. Through Barclays LifeSkills, we’re working across our partnerships to turn demand into clear routes to good jobs.”

— Deborah Goldfarb, Global Head of Citizenship, Barclays

“What gives me optimism is how clearly manufacturing and industrial skills are being redefined as both high-tech and people-driven. Advances in automation, digital tools and connected systems are changing work on the factory floor and at job sites. Realizing the full value of those advances depends on sustained investment in our people through skills-building, learning and clear career pathways. I’m also encouraged by how employers are engaging more intentionally with collaborators beyond their organizations. We’re witnessing stronger coordination among educators, workforce systems and local communities to ensure training keeps pace with technological advancement. This alignment — of innovation, skills and purpose — is a compelling signal that manufacturing can provide meaningful, fulfilling careers in a dynamic industry.

One of the greatest opportunities lies in modernizing workforce systems to evolve alongside the technologies shaping manufacturing. High schools, community colleges and regional training providers are critical anchors in this system, and we need to align more closely and dynamically with them, given that roles and skill requirements are changing faster than traditional training cycles can keep pace.

That means co‑designing training pathways that blend hands‑on experience with digital and technology‑enabled learning. It also means creating opportunities for continuous upskilling throughout a career. When workforce systems are built to adapt — rather than react — they not only prepare people for today’s manufacturing roles, but also for the future. They also help ensure the industry can remain innovative, competitive, and resilient over the long term.”

— Asha Varghese, Head of Corporate Social Responsibility, Caterpillar Inc. and President of the Caterpillar Foundation

“We are seeing a historic surge in systems readiness work at the local, state, and national levels. Stakeholders in the workforce ecosystem sometimes work in silos, but I’m seeing sustained interest in collaboration, especially across sectors. We are collectively examining what worked in the past to determine what must evolve for the future. 

There’s also growing consensus that career journeys of the future will be less linear. We know upskilling isn’t one-dimensional. It might mean deepening expertise to grow within an existing career trajectory, diversifying skills to transition into an adjacent role, or pivoting into an entirely new profession. A big opportunity right now is to reimagine our support systems to recognize this full spectrum of movement, ensuring that our infrastructure is as flexible as the workers it serves.”

— Diana Fischer, Senior Director, Workday Foundation

“One of the greatest opportunities is in building accelerated, more flexible pathways into the skilled trades that are tightly connected with employer needs. A more responsive workforce system should focus on expanding apprenticeships, investing in short-term training, and exposing students earlier to these fulfilling and well-paying careers.”

— Betsy Conway, Executive Director, Lowe’s Foundation

When and How AI Can Improve Grantmaking

Posted on by Brianna Provenzano

AI is moving fast, but grantmakers are rightly cautious. Funders are under pressure to move money more efficiently, learn faster, and support grantees better, all without adding risk, burden, or opacity to an already complex system. The question is no longer whether AI will touch grantmaking, but where it can actually add value—and where it shouldn’t.

On April 16, NationSwell invited philanthropic and impact leaders to take part in a conversation on the practical use of AI in grantmaking. The conversation featured ideas about when AI can meaningfully improve decisions and workflows and how to adopt it in ways that strengthen, rather than undermine, equity, accountability, and relationships with grantees. Some of the most salient takeaways from the discussion appear below:

Key takeaways:

Assess where AI meaningfully adds value across the grantmaking process. Rather than applying AI indiscriminately, organizations should take a step back and evaluate workflows end-to-end to determine where these tools can be most effective. A thoughtful, system-level approach can promote AI application in ways that enhance, rather than complicate, existing processes.

Use AI to streamline manual and error-prone grantmaking workflows. Financial due diligence can be a highly manual, time-intensive, and error-prone process, often involving spreadsheet-based analysis or visual review of financial statements. AI tools like Grant Guardian were developed to improve accuracy and efficiency in this specific workflow. 

Reinvest time savings from AI into deeper grantee engagement. Small grantmaking teams often face hundreds of applications, creating capacity constraints. AI can be used to support summarization, rubric-based pre-review, and prioritization to help manage this volume. The reduction in processing time, from hours to minutes, can allow staff to spend more time having meaningful conversations with grantees and improving the quality of their work. 

Recognize and normalize AI use among applicants and grantees. There is growing recognition that applicants and grantees are using AI to improve efficiency, particularly in drafting and responding to applications. When used thoughtfully, this can help reduce administrative burden, though differentiation still relies on the substance of proposals and outcomes.

Consider supporting grantees’ capacity to adopt AI tools and infrastructure. As AI becomes more embedded in workflows, there is an opportunity for funders to think about how grantees can access and use these tools effectively. Supporting this capacity, particularly through flexible, operational funding, can help organizations integrate AI in ways that enhance their work, rather than treating it as a one-off programmatic expense.

Develop and deploy AI systems with responsible AI principles. Specific principles should guide all AI adoption work in grantmaking, including safety and transparency, community-centered design, bias mitigation, human-in-the-loop validation, enterprise-grade security, and sustainability considerations. Start AI adoption through structured experimentation with clear guardrails, and consider empowering early adopters to test tools within defined parameters (e.g., “stoplight” approaches to acceptable use). These frameworks can also support clearer communication and transparency about how AI is being used.

Consider AI disclosure as contextual and relational: Whether and how to disclose AI use in grantmaking processes depends on organizational policies and levels of AI involvement. While practices may vary between organizations, especially as technology grows and with wider experimentation, keep a relational and trust-based mindset.

Maintain human oversight as a core requirement in AI-assisted workflows. AI is never a substitute for human judgment, and validation and verification by users must be built into the process. Being explicit about this, both internally and externally, can help reinforce trust, particularly in a field like philanthropy that is deeply relationship-driven and values human expertise.

Design for customization of AI tools to reflect different evaluation contexts. Grantmaking organizations assess financial health and programmatic fit differently, and AI tools can be configured with varying metrics, thresholds, and profiles to match those needs. This flexibility can also support more context-sensitive and equitable evaluation approaches; for example, assessing early-stage organizations differently than more established ones. 

Predicting the Future of Work: Using Data to Build More Inclusive Workforce Systems

Posted on by Brianna Provenzano

As AI and automation accelerate change across the labor market, predictive analytics offer powerful tools to anticipate which jobs, skills, and communities face the greatest risk – and where new opportunities are emerging.

On April 14, NationSwell convened a group of cross-sector leaders for a conversation on how data-driven insights can inform equitable training pathways, smarter investments, and workforce systems that are more responsive, inclusive, and resilient – ensuring workers are prepared for what’s next. Some of the most salient takeaways from the discussion appear below:

Key takeaways

Build workforce systems around capabilities, not credentials. A skills-first labor market only works if the underlying data infrastructure can recognize how people actually build skills through work, not just through degrees. When systems continue to privilege credential proxies over demonstrated capability, they miss large pools of qualified talent and reinforce inequities that workforce initiatives are meant to solve.

Pair predictive tools with better upstream data. Forecasting tools are only as strong as the signals they rely on. If workforce data continues to over-index on traditional credentials or lagging indicators, even sophisticated models will reproduce old blind spots; the real opportunity is to feed these systems richer, skills-based, real-world signals that surface emerging pathways earlier.

Invest in verified outcomes data, not just self-reported program metrics. Too much workforce decision-making still depends on incomplete or anecdotal outcome data. Expanding access to administrative wage data and other verified sources can help providers understand which programs are actually driving employment and earnings gains, and make more strategic decisions about what to scale, refine, or retire.

Use labor market data to map mobility, not just demand. It is not enough to know which jobs are growing. More useful systems help workers and practitioners understand how people can move from one role to the next based on shared skills, adjacent occupations, and realistic transition pathways, especially in a labor market where workers will increasingly need to pivot across sectors over time.

Treat durable human skills as core infrastructure. As AI and automation continue to reshape tasks, foundational capabilities like problem-solving, judgment, adaptability, collaboration, and communication are becoming more valuable. Technical requirements will keep evolving, but these underlying skills are what allow workers to remain resilient and mobile across changing tools, roles, and industries.

Redesign learning environments for experiential learning, not just memorization. Traditional teaching methodologies are increasingly challenged in a labor market where workers are expected to interpret information, make decisions, and adapt in real time. Experiential learning where people must apply knowledge, navigate ambiguity, and solve real problems better prepares learners for a world in which execution is increasingly automated and judgment is the differentiator.

Center the learner’s lived experience when designing workforce pathways. Workforce systems often default to employer demand signals and institutional priorities, but durable pathways require equal attention to how individuals actually make decisions. People choose careers based on identity, values, belonging, perceived risk, and developmental stage so the strongest systems help learners navigate options rather than simply presenting them.

Avoid replacing one rigid pathway with another. As enthusiasm grows around alternatives to four-year degrees, there is a risk of steering lower-income learners into workforce tracks while more privileged peers retain access to broader optionality. The goal is not to substitute “college for some, training for others,” but to build multiple high-quality pathways that preserve dignity, mobility, and long-term choice across backgrounds.Reframe middle-skill and nontraditional career paths as real engines of mobility. Many high-demand roles outside the traditional college track now offer strong wages, lower debt burden, and meaningful advancement potential, yet outdated perceptions still diminish their value. Shifting both rhetoric and practice to put career and college readiness on more equal footing is essential if workforce systems are going to reflect today’s economic realities.

Cisco | Skills-to-jobs at scale

Cisco | Skills-to-jobs at scale

How Cisco Networking Academy is transforming the lives of learners

The idea for Cisco Networking Academy was born in 1997. Cisco employees returned to an under-resourced school where they had donated state-of-the-art networking equipment. They were excited to see how students and educators were being empowered by the technology. Instead, they found the equipment sitting unused. The lesson learned that day was that technology alone is not enough; without the knowledge and skills to use it, even the best equipment’s potential will go untapped. 

Cisco recognized that for networking technology to truly expand and thrive, there needed to be a workforce capable of installing, configuring, and maintaining those networks. There was a critical skills gap: educators and students lacked the training to leverage the new technology, and there was no established pathway to build that expertise at scale.

Beyond just technical skills, Cisco also saw an opportunity to transform lives by providing inclusive access to technology education. Cisco sought to use its own technology and vast networking expertise to create clear pathways for both new learners and those reskilling or upskilling, ensuring they become prepared for the jobs of today and tomorrow. Thus, Networking Academy was launched.

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Responsible Use of AI for Social Impact

Responsible Use of AI for Social Impact

AI is rapidly reshaping how the social impact sector delivers on its mission. Yet as adoption accelerates, many organizations lack the governance needed to manage risk and fully realize AI’s potential.

Developed by NationSwell in collaboration with IBM, Responsible Use of AI for Social Impact is a practical playbook designed to help organizations of all sizes adopt AI ethically, safely, and effectively. Drawing on insights from leaders across the field, the guide offers real-world frameworks, case studies, and actionable steps to move from experimentation to responsible application.

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