The Accelerator
Strategic Insights at the Intersection of AI & Community Growth
Bridging the AI Skills Gap:
The Human Challenge in the Age of Artificial Intelligence
by James White II
As artificial intelligence rapidly transforms industries worldwide, organizations face a critical bottleneck that threatens to slow their AI ambitions: a severe shortage of skilled professionals who can bridge the gap between technology and business value. The AI skills gap represents one of the most significant challenges facing enterprises today, requiring not just technical expertise but a diverse ecosystem of roles, capabilities, and strategic thinking. Understanding and addressing this gap is essential for organizations seeking to harness AI’s transformative potential.
Understanding the Multifaceted AI Skills Challenge
The AI skills gap extends far beyond the commonly cited shortage of data scientists and machine learning engineers. Organizations need people who understand AI capabilities deeply enough to identify where and how AI can create value. They require professionals who can implement and manage complex AI systems in production environments, ensuring reliability, security, and performance. Perhaps most critically, they need leaders and strategists who can think holistically about AI applications, balancing technological possibilities with business objectives, ethical considerations, and organizational readiness.
This challenge manifests differently across organizations. Enterprises struggle to attract top AI talent in a fiercely competitive market where tech giants and well-funded startups offer premium compensation packages. Mid-sized companies often lack the resources to compete for specialized expertise. Meanwhile, even organizations that successfully recruit AI professionals frequently discover that technical skills alone are insufficient—they need individuals who understand both the technology and the specific domain in which they operate.
The rapid pace of AI advancement compounds the problem. Skills that were cutting-edge two years ago may now be foundational, while new capabilities and approaches emerge continuously. This constant evolution means that even experienced AI professionals must engage in perpetual learning to remain effective, placing additional pressure on organizations to support ongoing skill development.
Emerging Roles Across the AI Lifecycle
The maturing AI industry has revealed that successful AI implementation requires a diverse team with specialized roles spanning the entire AI lifecycle. Contrary to popular perception, not everyone needs to become a data scientist. Instead, a rich ecosystem of complementary positions has emerged, each critical to AI success.
Data Annotation Specialists form the foundation of effective AI systems. These professionals label and categorize data that trains machine learning models. While the work may seem straightforward, high-quality annotation requires domain expertise, attention to detail, and understanding of how labeling decisions impact model performance. Organizations are discovering that investing in skilled annotators—rather than treating annotation as low-skill work—significantly improves AI outcomes.
MLOps Engineers bridge the gap between model development and production deployment. They build and maintain the infrastructure that allows AI systems to run reliably at scale, implementing monitoring systems, managing model versions, automating retraining pipelines, and ensuring that AI applications meet performance and reliability standards. As organizations move from AI experimentation to production deployment, MLOps expertise has become indispensable.
AI Ethicists and Governance Specialists address the growing need for responsible AI development. These professionals develop frameworks to identify and mitigate bias, establish transparency and accountability mechanisms, ensure regulatory compliance, and help organizations navigate the ethical dimensions of AI deployment. As AI’s societal impact grows and regulations evolve, this role has transitioned from nice-to-have to essential.
Domain Experts with AI Literacy may represent the most valuable and overlooked category. These individuals possess deep expertise in a specific industry or function—healthcare, finance, manufacturing, marketing—combined with sufficient AI knowledge to identify high-value applications. They understand business processes well enough to recognize where AI can create meaningful impact and communicate effectively with both technical teams and business stakeholders. Organizations that successfully develop this capability gain a significant competitive advantage.
AI Product Managers translate between technical capabilities and business needs. They define AI product requirements, prioritize features, manage stakeholder expectations, and ensure that AI initiatives align with organizational objectives. This role requires a unique combination of technical understanding, business acumen, and communication skills.
Data Engineers build and maintain the data infrastructure that feeds AI systems. They design data pipelines, ensure data quality and accessibility, manage data storage and processing systems, and enable the smooth flow of information that AI models require. Without strong data engineering, even the most sophisticated AI models cannot deliver value.
This diverse skill ecosystem demonstrates that addressing the AI talent gap requires thinking beyond traditional technical roles and recognizing the full spectrum of capabilities needed for AI success.
Forward-Thinking Organizations Investing in Upskilling
Rather than relying solely on external hiring, leading organizations have recognized that building internal AI capabilities through workforce upskilling offers a more sustainable and often more effective approach. These companies are making substantial investments in developing their existing employees’ AI skills, creating pathways for people to transition into AI-related roles, and fostering a culture of continuous learning.
This strategic approach offers multiple advantages. Internal employees already understand the organization’s culture, processes, and challenges. They possess domain expertise that external hires would need time to acquire. Upskilling initiatives also boost employee engagement and retention, as workers appreciate investments in their professional development. Moreover, building internal capabilities creates a sustainable talent pipeline less vulnerable to external market dynamics.
Forward-thinking organizations approach upskilling systematically. They assess current skill levels and identify gaps, define clear learning pathways for different roles and career stages, provide time and resources for learning, create opportunities to apply new skills to real projects, and measure and celebrate progress. This structured approach ensures that upskilling efforts translate into genuine capability building rather than remaining theoretical exercises.
Importantly, successful upskilling programs recognize that different roles require different levels and types of AI knowledge. Not everyone needs to code neural networks. Some employees benefit from AI literacy programs that help them understand possibilities and limitations. Others pursue deeper technical training in specific areas. The most effective programs offer personalized learning paths that align with individual roles and career aspirations.
Case Studies: Successful Approaches to Skills Development
Corporate Training Programs: Building AI Capabilities from Within
A global financial services firm launched a comprehensive AI academy accessible to all 50,000 employees. The program offered tiered learning paths: basic AI literacy for all employees, intermediate courses for those working alongside AI systems, and advanced technical training for those transitioning into AI development roles. Within two years, over 15,000 employees completed at least foundational training, and 500 employees successfully transitioned into AI-related positions. The company reported that internally developed talent often outperformed external hires because they combined technical skills with deep institutional knowledge.
A manufacturing company took a different approach, creating intensive boot camps that trained production floor workers to become AI quality control specialists. These employees used their expertise in identifying defects to train computer vision models and validate automated inspection systems. The program transformed the role of quality inspectors while addressing the shortage of AI specialists who understood manufacturing processes. Employee satisfaction increased significantly as workers saw their expertise valued in new, technologically advanced ways.
Educational Partnerships: Bridging Academia and Industry
A technology company partnered with a university to create a custom master’s degree program in applied AI. The company provided real-world projects, guest lecturers from its AI teams, and internship opportunities. Employees could pursue the degree part-time with tuition support and dedicated study time. The university gained practical insights that improved curriculum relevance, while the company developed a pipeline of talent with both theoretical knowledge and hands-on experience specific to its needs. Over 200 employees completed the program, with 85% remaining with the company afterward.
A healthcare organization collaborated with multiple community colleges to develop AI training programs for medical records specialists and administrative staff. The program taught participants to work with natural language processing systems, understand AI-assisted diagnostic tools, and manage AI implementations in clinical settings. This partnership addressed the healthcare industry’s unique need for AI professionals who understand medical workflows and regulatory requirements while providing community colleges with industry-relevant curriculum that attracted students.
Knowledge-Sharing Initiatives: Creating Learning Cultures
A retail company established an internal AI community of practice that met weekly to share projects, challenges, and learning. Senior AI practitioners mentored junior colleagues through formal mentorship programs. The company created an internal knowledge repository where teams documented their AI projects, including both successes and failures. This transparency accelerated learning across the organization and prevented teams from repeating mistakes. The initiative cost relatively little but generated substantial value by facilitating knowledge transfer and building collective expertise.
An energy company implemented a “teach to learn” program where employees who completed AI training were required to teach what they learned to colleagues. This approach reinforced learning for those who taught while scaling knowledge dissemination efficiently. The program also identified natural teachers and communicators who became internal AI advocates and trainers. Within eighteen months, AI literacy spread throughout the organization, creating a shared language and understanding that improved cross-functional collaboration on AI projects.
Hybrid Approaches: Combining Multiple Strategies
The most successful organizations often combine multiple approaches. A telecommunications company created an AI skills development ecosystem that included partnerships with online learning platforms for foundational training, collaborations with universities for advanced degrees, internal mentorship programs, dedicated time for experimentation and learning, regular internal conferences where teams showcased AI projects, and rotation programs that allowed employees to work on AI teams temporarily. This comprehensive approach recognized that different people learn differently and that building AI capabilities requires sustained, multifaceted effort.
Why AI Success Depends on People and Processes,
Not Just Technology
The persistent belief that AI adoption is primarily a technology challenge has led many organizations to invest heavily in tools, platforms, and algorithms while underinvesting in the human and organizational elements essential for success. Experience demonstrates clearly that technology alone never delivers value—value emerges from how people use technology within effective processes and supportive organizational contexts.
Consider the reality of AI implementation. The most sophisticated machine learning model provides no value if no one knows how to identify appropriate use cases. A powerful AI platform sits idle without people who understand how to operate it. Even successfully deployed AI systems fail when organizational processes do not adapt to incorporate AI-generated insights into decision-making. Technology creates potential; people and processes actualize it.
The human element proves crucial at every stage of the AI lifecycle. During the ideation phase, domain experts identify problems worth solving and assess whether AI represents the right solution. During development, data scientists and engineers build models, but their success depends on collaboration with business stakeholders who provide context and feedback. During deployment, change management specialists help organizations adapt workflows and overcome resistance. During operations, diverse teams monitor performance, address issues, and continuously improve systems.
Processes matter equally. Clear governance frameworks ensure that AI initiatives align with strategic priorities and that resources flow to high-value projects. Robust data management processes provide the foundation that AI systems require. Change management processes help organizations navigate the human dimensions of AI adoption, addressing concerns, building trust, and ensuring that people understand how to work effectively with AI tools. Quality assurance processes catch errors before they impact customers or operations. Without these supporting processes, even talented teams struggle to deliver results.
The interdependence of people, processes, and technology becomes obvious when examining AI failures. Projects fail not because the technology was inadequate but because organizations lacked people with the skills to use it properly. Initiatives stall not from technical limitations but from process gaps that prevent data access or slow deployment. AI systems underperform not due to algorithmic deficiencies but because organizational culture resists the changes required to leverage AI effectively.
This reality has profound implications for AI strategy. Organizations must balance investments across all three dimensions: acquiring or developing technology capabilities, building human capital through hiring and upskilling, and establishing processes that enable effective AI deployment and operation. Overemphasizing any single element creates bottlenecks that limit overall progress.
Moreover, the people and process dimensions often deliver competitive advantage more sustainably than technology alone. Competitors can purchase the same tools and platforms. They can access the same algorithms and frameworks. Replicating an organization’s AI talent, culture, and operating processes proves far more difficult. Companies that excel at AI do so not because they possess superior technology but because they have built superior capabilities for identifying opportunities, executing projects, and integrating AI into operations.
The skills gap, then, represents more than a shortage of technical expertise. It reflects the broader challenge of developing organizational AI capabilities—the combination of skilled people, effective processes, and appropriate technology working in concert. Closing this gap requires comprehensive strategies that address all three elements simultaneously.
Conclusion: The Path Forward
Bridging the AI skills gap stands as one of the defining challenges for organizations pursuing AI transformation. The good news is that solutions exist and forward-thinking companies are demonstrating what works. Success requires recognizing that AI skills span a diverse ecosystem of roles, investing systematically in workforce development, leveraging partnerships with educational institutions, fostering knowledge-sharing cultures, and understanding that AI adoption depends fundamentally on people and processes as much as technology.
Organizations that address the skills gap comprehensively—by developing internal talent, creating clear career pathways in AI-related roles, establishing robust processes, and building cultures that embrace learning and change—position themselves to capture AI’s full potential. Those that view the skills gap narrowly as a hiring challenge or treat AI adoption as purely a technology project will likely struggle regardless of their financial resources or technological investments.
The journey to AI maturity is ultimately a journey of organizational learning and capability building. Technology enables this journey, but people drive it, and processes sustain it. Organizations that embrace this reality and invest accordingly will not only close their skills gaps but will build lasting competitive advantages in an AI-driven future.
Key Takeaways from Day 1 AI Expo: Trends Shaping the AI Landscape
The Day 1 AI Expo offered more than just impressive technology demonstrations—it provided a window into the trends, challenges, and opportunities defining the current AI landscape. For business leaders, technologists, and anyone interested in where artificial intelligence is headed, several key insights emerged that warrant attention.
Trend 1: AI is Moving from Experimentation to Production
One of the clearest messages from the expo was that organizations have moved past the “pilot project” phase. Companies are deploying AI systems at scale, integrating them into core business processes, and measuring real ROI. The conversation has shifted from “should we explore AI?” to “how do we optimize our AI operations?”
This maturation brings new challenges. Speakers emphasized the importance of MLOps (Machine Learning Operations) and robust infrastructure for managing AI systems in production. Reliability, monitoring, and continuous improvement emerged as critical concerns for organizations running AI at scale. The days of treating AI as a side experiment are over—it now requires the same operational rigor as any other mission-critical system.
Trend 2: Data Quality Trumps Data Quantity
While the importance of data for AI has long been understood, expo discussions revealed a refined perspective. Multiple presentations highlighted that having massive datasets matters less than having high-quality, well-labeled, and representative data. Companies that have focused on data governance, curation, and quality control are seeing better AI outcomes than those simply hoarding information.
This insight has practical implications for organizations building AI capabilities. Investing in data infrastructure, establishing clear data governance policies, and ensuring data diversity and accuracy emerged as foundational steps. Several case studies demonstrated how companies achieved breakthrough results not by collecting more data, but by improving the data they already had.
Trend 3: Generative AI is Transforming Knowledge Work
The impact of generative AI tools on knowledge work was a dominant theme throughout the expo. Demonstrations showed how AI assists with content creation, code generation, data analysis, and decision support. However, the most compelling presentations focused not on replacing humans, but on augmenting human capabilities and removing tedious tasks.
Organizations are discovering that generative AI’s value lies in productivity gains and creative enhancement. Marketing teams use AI to generate content variations, allowing human experts to focus on strategy and refinement. Developers leverage AI coding assistants to handle boilerplate code while concentrating on architecture and complex problem-solving. The pattern is consistent: AI handles routine work, freeing humans for higher-value contributions.
Trend 4: AI Governance and Ethics Are Business Imperatives
No longer confined to academic discussions, AI ethics and governance took center stage at the expo. Organizations shared frameworks for responsible AI development, highlighting considerations around bias mitigation, transparency, accountability, and fairness. Regulatory compliance and risk management emerged as significant drivers for these efforts.
Companies are establishing AI ethics boards, conducting algorithmic impact assessments, and implementing monitoring systems to detect and correct biases. This focus reflects both ethical commitments and practical recognition that AI systems making unfair or unexplained decisions create legal, reputational, and operational risks. The message was clear: responsible AI is not optional—it’s essential for sustainable success.
Trend 5: Skills Development is the Next Frontier
Perhaps the most discussed challenge at the expo was the AI skills gap. Organizations need people who understand AI capabilities, can implement and manage AI systems, and can think strategically about AI applications. This doesn’t mean everyone needs to become a data scientist—roles are emerging across the AI lifecycle, from data annotation specialists to AI ethicists to domain experts who can identify high-value AI applications.
Forward-thinking organizations are investing heavily in upskilling their workforce. Training programs, partnerships with educational institutions, and knowledge-sharing initiatives were common themes. The consensus: AI adoption success depends as much on people and processes as on technology.
Applying These Insights
The Day 1 AI Expo made it clear that AI has reached an inflection point. Organizations that understand these trends and act on them will be better positioned to capture AI’s benefits while navigating its challenges. The path forward requires balancing innovation with responsibility, investing in both technology and people, and maintaining focus on solving real business problems rather than chasing technological novelty.
For those looking to advance their AI initiatives, the expo offered a roadmap: prioritize data quality, establish governance frameworks, invest in skills development, move thoughtfully from experimentation to production, and remember that AI’s ultimate value lies in augmenting human capabilities to achieve outcomes that neither humans nor machines could accomplish alone.
How AI is Reshaping Community Economic Development: Opportunities and Challenges
by James White II
The intersection of artificial intelligence and community economic development represents one of the most promising—and complex—frontiers for local economies. As AI tools become more accessible, community development practitioners face a critical question: How can we harness this technology to build more equitable, resilient local economies without exacerbating existing disparities?
The Promise: AI as an Equalizer
AI is democratizing capabilities that were once available only to well-resourced organizations. Small community development corporations can now leverage AI-powered tools for market analysis, grant writing, and project planning—tasks that previously required expensive consultants or specialized staff. Natural language processing tools can help CDFIs streamline loan applications and risk assessments, potentially expanding access to capital in underserved communities.
Consider the potential for AI in workforce development. Personalized learning platforms powered by AI can help community colleges and training programs tailor education to individual learners' needs, adapting in real-time to help residents build skills for emerging local industries. This adaptive approach could be transformative for communities working to retrain workers displaced by economic transitions.
The Reality Check: New Risks for Vulnerable Communities
However, the AI revolution also carries significant risks for community economic development. Automation threatens to disproportionately impact the very communities that CED organizations serve. Entry-level and routine jobs—often the first rung on the economic ladder for residents of low-income communities—are among the most vulnerable to AI-driven automation.
There's also the troubling reality of algorithmic bias. AI systems trained on historical data can perpetuate and even amplify existing inequities in lending, hiring, and resource allocation. A community development organization using AI tools for decision-making must be vigilant about these embedded biases, which can undermine equity goals even when adopted with the best intentions.
A Path Forward: Strategic Adoption with Community Voice
The key for CED practitioners is strategic, values-driven adoption of AI. This means several things:
First, invest in AI literacy within your organization and community. Residents and stakeholders need to understand both the capabilities and limitations of AI to participate meaningfully in decisions about its use in community development.
Second, prioritize transparency and accountability when implementing AI tools. If you're using AI for loan decisions, tenant screening, or resource allocation, ensure there are clear mechanisms for explaining decisions and providing human oversight.
Third, engage communities in shaping how AI is deployed locally. This might mean participatory design processes for AI-powered services or community input on which applications of AI align with local development goals.
Finally, advocate for policy frameworks that ensure AI development and deployment serve community economic development goals. This includes pushing for inclusive data practices, algorithmic accountability, and workforce transition support.
The Bottom Line
AI is neither savior nor villain for community economic development—it's a powerful tool whose impact will be determined by how we choose to use it. The communities and organizations that thrive will be those that approach AI with eyes wide open, leveraging its capabilities while building safeguards against its risks. The accelerator effect we should aim for isn't just faster growth, but more inclusive and sustainable development that genuinely serves the communities we work with.
The question isn't whether AI will reshape CED work—it already is. The question is whether we'll shape that transformation to advance equity and community self-determination, or allow it to deepen existing divides.
Five AI Applications Every CED Organization Should Consider in 2026
by James White II
Community economic development organizations operate with tight budgets and ambitious missions. While AI might seem like a luxury reserved for tech companies and large corporations, several practical applications can help CED organizations work more effectively without breaking the bank or compromising their values.
1. Grant Writing and Fundraising Support
Grant writing consumes enormous staff time at most CED organizations, yet it's a task where AI can provide substantial assistance. AI tools can help draft initial grant narratives based on your organization's track record and the funder's priorities, research foundation guidelines, and even analyze which opportunities best match your programs.
The key is using AI as a collaborator, not a replacement. Have AI generate first drafts, then infuse them with the authentic community stories and local knowledge that funders actually want to see. Some organizations report cutting grant writing time by 30-40% while maintaining or improving success rates.
2. Community Needs Assessment and Data Analysis
Understanding community needs is fundamental to effective CED work, but analyzing demographic data, survey responses, and community input can be overwhelming. AI-powered analysis tools can help identify patterns in community feedback, map asset and opportunity zones, and surface insights that might be missed in manual review.
For example, natural language processing can analyze hundreds of community survey responses to identify common themes and priorities, while machine learning can help predict which neighborhoods might benefit most from specific interventions based on multiple data sources. This allows smaller organizations to conduct sophisticated analyses without hiring dedicated data scientists.
3. Small Business Technical Assistance at Scale
Many CED organizations provide technical assistance to local entrepreneurs but struggle to serve everyone who needs help. AI chatbots and advisory tools can provide 24/7 basic business guidance, answering common questions about business planning, licensing, and financing.
This doesn't replace the human touch for complex situations, but it can handle routine inquiries and free up staff to focus on high-touch, relationship-based support where they add the most value. Some organizations are creating AI tools trained in local business resources and regulations, making them more relevant than generic business advice platforms.
4. Workforce Development and Skills Matching
AI can enhance workforce development programs by better matching residents with training opportunities and job openings. Skills assessment tools can identify both obvious and hidden capabilities, while AI-powered platforms can suggest career pathways based on a resident's current skills, interests, and local labor market demand.
Some innovative programs are using AI to help job seekers practice interviews, receive feedback on resumes, and even build portfolios that showcase their skills in formats employers prefer. The personalization AI enables can be particularly powerful for residents facing multiple barriers to employment.
5. Property and Asset Management Optimization
For CDCs managing affordable housing or commercial properties, AI can optimize maintenance schedules, predict equipment failures before they happen, and even improve energy efficiency through smart building systems. These applications can reduce operating costs and free up resources for mission-critical work.
Predictive maintenance powered by AI can be especially valuable, preventing small issues from becoming expensive emergencies and helping preserve affordable housing stock more cost-effectively.
Making It Work: Implementation Principles
As you consider these applications, keep several principles in mind:
Start small and specific. Don't try to AI-if your entire operation at once. Pick one pain point and test an AI solution thoroughly before expanding.
Maintain human oversight. AI should augment human judgment, not replace it. This is especially critical in decisions affecting people's housing, employment, or access to capital.
Choose tools aligned with your values. Not all AI platforms are created equally. Look for providers committed to fairness, transparency, and data privacy. Be willing to ask hard questions about how their algorithms work and what biases they might contain.
Build AI literacy in your organization. Invest in helping staff understand what AI can and can't do. The goal isn't to turn everyone into data scientists, but to create informed users who can spot both opportunities and potential problems.
Include community voice. When implementing AI tools that affect community members, involve them in decisions about what tools to use and how. Their lived experience provides crucial context that AI alone cannot capture.
The Investment Perspective
Many AI tools now offer nonprofit pricing or free tiers that make experimentation affordable. The real investment is staff time to learn, implement, and oversee these tools. For most CED organizations, starting with one or two applications and measuring their impact before expanding is the wisest approach.
The organizations seeing the best results are those that view AI as a capacity-building investment, not a cost-cutting measure. The time saved through AI-powered efficiency goes back into mission delivery, community engagement, and the relationship-building that remains at the heart of effective community economic development.
AI won't solve the fundamental challenges CED organizations face—inadequate funding, systemic inequity, or political barriers. But thoughtfully deployed, it can help us work smarter, serve more people, and ultimately accelerate the inclusive economic development our communities deserve.
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Day 1 AI Expo: A Comprehensive Look at the Future of Artificial Intelligence
The Day 1 AI Expo brought together industry leaders, innovators, and technology enthusiasts to explore the rapidly evolving landscape of artificial intelligence. Held at a time when AI is transforming every sector imaginable, the expo served as a crucial gathering point for those shaping and being shaped by this technological revolution.
Innovation on Display
The expo floor buzzed with demonstrations of cutting-edge AI applications spanning multiple industries. From healthcare diagnostics powered by machine learning to autonomous systems revolutionizing logistics, attendees witnessed firsthand how AI is moving from theoretical promise to practical implementation. Exhibitors showcased solutions addressing real-world challenges, demonstrating that AI has matured beyond the experimental phase into a driving force for business transformation.
What stood out most was the diversity of applications. Manufacturing companies displayed predictive maintenance systems that reduce downtime and costs. Retail innovators presented personalized shopping experiences powered by sophisticated recommendation engines. Financial institutions demonstrated fraud detection systems that protect consumers while streamlining operations. The breadth of use cases underscored AI’s universal applicability across sectors.
Thought Leadership and Insights
The conference programming featured keynote speakers who provided valuable perspectives on AI’s trajectory. Discussions ranged from technical deep dives into neural network architectures to strategic conversations about organizational AI adoption. Panel discussions tackled pressing questions about implementation challenges, data governance, and the skills gap facing companies eager to leverage AI capabilities.
Several recurring themes emerged throughout the presentations. Leaders emphasized the importance of responsible AI development, stressing that ethical considerations must be embedded into design processes from the start. The need for transparency in AI decision-making systems garnered significant attention, particularly for applications affecting human lives and livelihoods. Speakers also highlighted the democratization of AI tools, noting that sophisticated capabilities once available only to tech giants are now accessible to startups and established enterprises alike.
Networking and Collaboration
Beyond the formal programming, the Day 1 AI Expo facilitated valuable connections between practitioners, researchers, and business leaders. The event created opportunities for partnerships, knowledge exchange, and collaborative problem-solving. Startups connected with potential investors and enterprise clients, while established companies explored integration partnerships and vendor relationships.
The networking sessions revealed a community eager to learn from each other’s experiences. Attendees shared implementation stories—both successes and failures—creating a culture of openness that benefits the entire ecosystem. These informal conversations often proved as valuable as the scheduled presentations, offering practical insights that can only come from those working directly with AI technologies.
Looking Ahead
The Day 1 AI Expo reinforced that artificial intelligence is not a distant future technology but a present-day reality reshaping how we work, live, and solve problems. The innovations on display and conversations taking place signal that we’re still in the early stages of AI’s transformative potential. As these technologies continue to mature and new applications emerge, events like this expo will remain essential for staying informed, connected, and prepared for what’s next.
For those who attended, the expo provided clarity, inspiration, and actionable insights. For those who missed it, the momentum and innovations showcased serve as a reminder that AI adoption is accelerating, and staying engaged with this evolving landscape is becoming increasingly critical for organizations across all sectors.
AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise
by Ryan Daws
Posted February 4, 2026 Reposted February 4, 2026
While the prospect of AI acting as a digital co-worker dominated the day one agenda at the co-located AI & Big Data Expo and Intelligent Automation Conference, the technical sessions focused on the infrastructure to make it work.
A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These tools reason, plan, and execute tasks rather than following rigid scripts. Amal Makwana from Citi detailed how these systems act across enterprise workflows. This capability separates them from earlier robotic process automation (RPA).
Scott Ivell and Ire Adewolu of DeepL described this development as closing the “automation gap”. They argued that agentic AI functions as a digital co-worker rather than a simple tool. Real value is unlocked by reducing the distance between intent and execution. Brian Halpin from SS&C Blue Prism noted that organisations typically must master standard automation before they can deploy agentic AI.
This change requires governance frameworks capable of handling non-deterministic outcomes. Steve Holyer of Informatica, alongside speakers from MuleSoft and Salesforce, argued that architecting these systems requires strict oversight. A governance layer must control how agents access and utilise data to prevent operational failure.Global AI Trends: What's Happening Now
Nonprofit Tech for Good
A Digital Marketing & Fundraising Resource for Nonprofits
Welcome to the Artificial Intelligence (AI) Hub for Nonprofits!
The AI Hub is an actively curated list of the most useful AI tools and resources for nonprofits found on the internet. To request an addition to the list, please post it to our Artificial Intelligence (AI) for Nonprofit Organizations LinkedIn Group.
Artificial Intelligence, Fundraising
New Data Reveals Most Nonprofits Aren’t Using AI for Fundraising Nonprofit Tech for Good social media data.
by Heather Mansfield, Founder and Editor-in-chief of Nonprofit Tech for Good
Posted January 9, 2026 Reposted February 3, 2026
The most recent Nonprofit Tech for Good Survey reveals that the vast majority of nonprofits are not using AI for fundraising. In fact, the nonprofit sector has barely entered the early adoption phase, as visualized by the Rogers’ Technology Adoption Curve. If you are a nonprofit professional who has worried that your nonprofit is moving too slowly to adopt AI, worry not! There is still ample opportunity to reap the benefits of early adoption, provided your nonprofit has a budget to experiment with new AI fundraising tools and staff who are enthusiastic and empowered to deploy these new tools.
In 2026, the adoption rate of AI in fundraising will grow as nonprofit professionals increase their AI literacy and fundraising platforms expand their AI toolsets. However, it’s worth noting that currently, 60% of nonprofits report a lack of in-house expertise to assess AI tools, and only 4% have AI-specific training budgets, according to the must-read 2025 AI Equity Report. At that level of investment in AI, the nonprofit sector is at risk of remaining stuck in curiosity mode, or worse, willfully illiterate in AI despite “AI Literacy” being named the fastest-growing skill of 2025 by LinkedIn.
All that said, here are five key findings about AI for fundraising from the most recent Nonprofit Tech for Good Survey completed by 826 nonprofits:
1) 4.5% of nonprofits utilize smart donation forms.
Smart fundraising software uses predictive AI to personalize donation form amounts by processing the donor data (PII) in your CRM, or for anonymous website visitors, non-PII data, such as web browser, zip code, etc. Slowly, more fundraising platforms are offering smart fundraising, also known as intelligent fundraising, and in the years to come, all fundraising platforms will have to roll out smart fundraising software or risk becoming obsolete.
2) 4.1% of nonprofits utilize smart email sending.
Smart email sending uses predictive AI to analyze each subscriber’s past open and click activity to determine the best time to send them emails. It’s an affordable paid upgrade offered by most email marketing platforms. Email is a powerhouse for online fundraising and growing monthly giving programs, so experimenting with smart email sending is a must for nonprofits in 2026.
CONTINUE READING AT NONPROFIT TECH FOR GOOD
CONTINUE READING AT NONPROFIT TECH FOR GOOD
2026 Social Media Statistics for Nonprofits
A supplemental post to 101 Digital Marketing & Fundraising Best Practices for Nonprofits, the statistics listed below can guide your nonprofit in creating and maintaining a successful social media strategy.
Facebook has 3.05 billion monthly active users of which 65% access the site daily and spend an average of 40 minutes per day on the platform. [HootSuite]
By 2027, Facebook will reach 75% of the world’s population and is currently the world’s third most trafficked website after Google and YouTube. [Similarweb]
62% of its users worldwide are millennials and Gen Z. [Statista]
93% of nonprofits use Facebook Pages. [2025 Nonprofit Tech for Good Survey]
On average, organic posts reach 2.2% of their followers on Facebook. [Social Status]
Nonprofits post an average of 5.5 times per week to their Facebook Page. [Rival IQ Social Media Industry Benchmark Report]
Nonprofits have an average engagement rate of 0.046% on Facebook. [Rival IQ Social Media Industry Benchmark Report]
53% of nonprofits spend on social media advertising. Of those that spend on social media ads, 98% spend on Facebook. [Nonprofit Tech for Good Report]
Facebook/Meta has an average cost per lead of $3.20 compared to $17.40 on TikTok. [M+R Benchmarks Report]
Facebook/Meta results in an average return on ad spend (ROAS) of $0.48 for fundraising ads compared to $0.03 on TikTok. [M+R Benchmarks Report]
The average cost per donation via Facebook Ads/Meta is $106 compared to $1,040 on TikTok. [M+R Benchmarks Report]
For every 1,000 email addresses, nonprofits have an average of 1,041 Facebook fans. [M+R Benchmarks Report]
37% of nonprofits use Facebook Fundraising Tools. 35% of nonprofits raised more money than they expected while 23% raised less. 32% raised what they expected. [Nonprofit Tech for Good Report]
97% of all Facebook fundraising revenue is donated through Facebook Fundraisers. [M+R Benchmarks Report]
The average Fundraiser has three gifts that average $36 USD — $108 USD in total. [M+R Benchmarks]
Giving through Facebook Fundraising Tools resulted in 0.2% of all online revenue for nonprofits in 2024 – down from 1.1% in 2023. [M+R Benchmarks]
Facebook fundraisers who are thanked by nonprofits during the fundraising campaign raise 35% more than those who are not. [Give Panel]
22% of nonprofits utilize UTM codes to monitor website traffic from Facebook/social media. [Nonprofit Tech for Good Report]
LinkedIn has 1 billion registered users of which 49% are monthly active users and 16% are daily active users. Monthly users spend an average of 17 minutes per month on LinkedIn. [The Social Shepherd]
44% of U.S. adults who use LinkedIn earn over $75,000 a year and 51% have a college degree. [Sprinklr]
36% of LinkedIn users are millennials, 27% are Gen X, 26% are Gen Z, and 9% are baby boomers. [HootSuite]
There are 2.2 million nonprofits on LinkedIn and 22 million nonprofit professionals. [LinkedIn]
27% of nonprofits worldwide have an official policy to allow staff to work on their LinkedIn Profiles during work hours. [Global NGO Technology Report]
81% of nonprofits use LinkedIn Pages. [2025 Nonprofit Tech for Good Survey]
68% of nonprofits post less than once weekly, 15% post once weekly, 8% post once every other day, 6% post once daily, and 3% post twice or more daily. [Global NGO Technology Report]
On average, posts reach 2.9% of their followers on LinkedIn. [Social Status]
Nonprofits have an average engagement rate of 1.91% on LinkedIn. [HootSuite Social Media Trends Report]
53% of nonprofits spend on social media advertising. Of those that spend on social media ads, 17% spend on LinkedIn. [Nonprofit Tech for Good Report]
For every 1,000 email addresses, nonprofits have an average of 58 LinkedIn followers. [M+R Benchmarks Report]
42% of US donors use LinkedIn to research nonprofits to support and 26% discover donation opportunities on LinkedIn. [Classy]
Instagram has 3 billion monthly active users of which 500 million access the site daily and spend an average of 24 minutes per day on the platform. [The Verge]
62% of Instagram users are between the ages of 18 to 34. [Backlinko]
85% of nonprofits worldwide use Instagram. [2025 Nonprofit Tech for Good Survey]
On average, posts reach 15.3% of their followers on Instagram. [Social Status]
Nonprofits post an average of 4.9 times per week to their Instagram account. [Rival IQ Social Media Industry Benchmark Report]
Nonprofits have an average engagement rate of 0.623% on Instagram. [Rival IQ Social Media Industry Benchmark Report]
53% of nonprofits spend on social media advertising. Of those that spend on social media ads, 47% spend on Instagram. [Nonprofit Tech for Good Report]
For every 1,000 email addresses, nonprofits have an average of 251 Instagram followers. [M+R Benchmarks Report]
13% of nonprofits use Instagram Fundraising Tools. 10% of nonprofits raised more money than they expected from Instagram Fundraising Tools while 57% raised less. 33% raised what they expected. [Nonprofit Tech for Good Report]
Instagram is the most commonly used platform for nonprofit influencer campaigns. Of those nonprofits that engage in influencer campaigns, 94% work with influencers on Instagram. [M+R Benchmarks Report]
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The State of Artificial Intelligence Adoption in Canadian Nonprofits
Key findings
This report offers one of the first snapshots of how Canada’s nonprofit sector is using Artificial Intelligence (AI) or Generative Artificial Intelligence (GAI). AI refers to computer systems that analyze data to recognize patterns, make predictions or support decisions.
GAI is a type of AI that creates new content such as text, images or code in response to prompts. Although AIis now part of many workplace tools, there has been little concrete information about how nonprofits are engaging with it. The findings from this report offer a baseline view of current adoption, highlight key challenges and illustrate the range of experiences across the sector.AI use is common but often limited in scope.
Eighty percent of survey respondents say their organization is using AI in some way. For many, this use is modest: half use AI for three or fewer organizational activities and only a fifth use it for seven or more. On average, organizations apply AI to 4.5 activities. Smaller organizations, those in arts, culture and recreation and those based in Alberta, the Prairies and Atlantic Canada are less likely to report using AI.
Most nonprofits begin with accessible, outward-facing uses of AI and expand into more complex areas. Two thirds of organizations (67%) use AI for communications and fundraising, and about half (50%) use it for data and information tasks. Far fewer apply it to internal functions such as strategy, human resources or programming.
The most common tasks are language- and information-focused, such as editing, summarizing and creating text, brainstorming, searching for information, and translating or transcribing speech. This pattern reflects a continuum of adoption: organizations typically start where tools are easier to use, and then build toward more complex, internally focused applications as their experience grows.
Experience strongly shapes perspectives about AI. The more extensively an organization uses.
AI, the more confident it tends to feel about its potential. Organizations using AI extensively are more likely to believe they can apply it across their operations and less likely to view it as over-hyped. Those making light use of AI, or not using it at all, are far more likely to be uncertain or unable to offer an opinion Skills, time and knowledge are stronger enablers of AI adoption than financial resources .Across the sector, the main barriers to using AI are uncertainty and limited hands-on experience, not resistance or lack of interest.
Staff capacity, training and access to relevant resources have a stronger influence on whether organizations use AI than funding. For example, organizations identifying staff time as an enabler are 6.4% more likely to use AI, and access to relevant knowledge increases the likelihood by 5.8%. Organizations that are unsure whether staff time, skills or IT capacity are enablers or barriers are less likely to be using AI at all.
While financial resources matter less for getting started, they do shape how extensively AI is applied: sufficient funding increases the number of AI-supported activities by 1.3 on average. This suggests that building skills and shared knowledge may have greater impact on adoption than financial resources alone. Risk awareness varies and policy development is lagging.
Majorities of organizations say they are aware of reputational risks (62%), legal, ethical and environmental issues (60%), and the potential to reinforce inequities (54%). However, sizeable minorities remain unsure. Only one tenth of organizations have formal AI-related policies (10%) and another fifth are developing them (21%). Nearly two thirds of organizations using AI (64%) have no policies and are not currently working to develop them.
Use of external supports is limited. Just 16% of organizations have drawn on a formal or informal group or network for AI-related support, and only 9% have engaged an external consultant. Larger organizations and those using AI more extensively are more likely to access these supports. Among those that do, training is the most common type of assistance (reported by 67%).
Overall, these findings paint a picture of a sector that is curious and experimenting with AI, but still developing the confidence, skills and governance needed for deeper and more responsible use. The results offer an early benchmark for understanding where nonprofits stand today anda foundation for tracking how AI adoption evolves in the years ahead.
101 Digital Marketing & Fundraising Best Practices for Nonprofits
February 3, 2026
101 Digital Marketing & Fundraising Best Practices for Nonprofits is a blog and webinar series that covers the fundamentals of website and email marketing, online fundraising, and social media for nonprofits. Originally published in 2024, the series has been updated for 2026!
The 101st best practice is a checklist of 200+ digital marketing and fundraising tasks meant to help your nonprofit get organized and implement the best practices featured in the series. We hope you find the best practices, checklist, stats, and related webinars and certificate programs useful!
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10 Online Fundraising Best Practices for Nonprofits
To be alerted of updates to the 101 Digital Marketing & Fundraising Best Practices for Nonprofits series, please sign up for Nonprofit Tech for Good’s newsletter.
The first “Donate Now” button was released in 1999 by a project of the Tides Foundation called Groundspring (acquired by Network for Good in 2005, Network for Good acquired by Bonterra in 2023), and for the next two decades, nonprofit fundraisers have embraced the study of inspiring people to give online to good causes.
Innovation in online fundraising was driven by the release of new technology, such as email marketing services like Constant Contact, which launched in 1998, the release of WordPress as a blogging tool in 2003 (now a website content management system used by 58% of nonprofits worldwide), and social networking websites beginning with Myspace in 2005.
Today, nonprofits worldwide have access to online fundraising tools that could not have been imagined at the turn of the millennium, and current best practices are shaped by 20+ years of innovation and experimentation.
1) Implement donation page best practices.
Even though billions of dollars have been raised online since 1999, it’s surprising how many nonprofits make the online giving process more complicated than it should be. As a general rule, donation pages should be simple, optimized for mobile giving, and ask for the minimal amount of information required to make a donation and capture a donor’s contact information.
The fourth post in this series, 10 Donation Page Best Practices for Nonprofits, elaborates on how the first step in being successful in online fundraising is having well-designed donation pages that accept multiple types of payments, enable monthly giving, and provide the option for donors to make a tribute gift. A good example of a one-time donation page is the American Cancer Society.
The State of AI Adoption in Canadian Nonprofits
Nonprofit Tech for Good social media data.
Posted on LinkedIn January 27, 2026 Reposted February 3, 2026
You know how much I love digging into the Nonprofit Tech for Good social media data. 📊
There’s so much here that’s genuinely useful, not just interesting.
If your nonprofit is low on time, staff, or capacity (and honestly… who isn’t?), 𝗱𝗮𝘁𝗮 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗳𝗼𝗰𝘂𝘀 𝘆𝗼𝘂𝗿 𝗲𝗻𝗲𝗿𝗴𝘆, 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝗯𝗲 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲 𝗮𝘁 𝗼𝗻𝗰𝗲.
This chart highlights just a few key stats, but one thing really jumped out at me:
𝟵𝟯% 𝗼𝗳 𝗻𝗼𝗻𝗽𝗿𝗼𝗳𝗶𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸, 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮𝗻𝘆 𝗼𝘁𝗵𝗲𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺, 𝘆𝗲𝘁 𝗶𝘁 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗹𝗼𝘄𝗲𝘀𝘁 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗿𝗮𝘁𝗲 𝗮𝗻𝗱, 𝗹𝗶𝗸𝗲𝗹𝘆, 𝘁𝗵𝗲 𝗹𝗼𝘄𝗲𝘀𝘁 𝗥𝗢𝗜.
It’s a good reminder that popularity doesn’t always equal effectiveness.
What stands out to you when you look at this? 👀
The Brookings Institution
Research
Why community benefit agreements are necessary for data centers
by Nicol Turner Lee and Darrell M. West
Posted January 29, 2026 Reposted January 30, 2026
Data centers are crucial for AI, but protests have arisen throughout the U.S. over financial, energy, and environmental concerns as companies try to build more facilities.
AI companies can work closely with local leaders to establish community benefit agreements (CBAs), which can help address public concerns and provide greater benefits to communities impacted by data center construction.
Transparency and cooperation between firms, local institutions, and residents are essential to facilitate community input into CBAs, and for proposals to support residents’ digital access, well-being, employment, and more.
Commentary
What the research shows about generative AI in tutoring
by Mary Burns
Posted January 27, 2026 Reposted January 30, 2026
While tutoring platforms enhanced by generative AI introduce new concerns around accuracy, pedagogical judgment, and possible dependence, the evidence shows that these platforms can hold numerous benefits for students—if designed responsibly.
It’s not just students who benefit. Tutoring that incorporates generative AI promises substantial benefits for teachers and education systems as a whole.
But in order to work as intended, safeguards must be built into the AI platforms. Areas for improvement remain, including enhancing personalization, fine-tuning effective feedback, and prioritizing a hybrid approach where human teachers and AI work together.
Research
Pro-productivity policies for the US economy
by Martin Neil Baily, David M. Byrne, Aidan T. Kane, and Paul E. Soto
Editor's note: This paper was published by the Productivity Institute on December 2, 2025. Reposted January 30, 2026
Abstract
Against the backdrop of slower growth globally, U.S. productivity growth has been faster than many peer economies. We argue that the reasons for U.S. productivity leadership go to both the remarkable engine of innovation represented by public and private sector research and development and to the economic dynamism that promotes the adoption of new technologies, the introduction of new business models, the entry of innovative firms, and reallocation of labor and capital to their best uses. This paper uses the policy scheme proposed by Dirk Pilat and Bart Van Ark to look at the source of growth: factor accumulation, competition policy, support of technology, and policies to support internationalization. We also explore the emerging technology of AI and its likely impact on future growth, the labor market, and the distribution of earnings. There is a brief history of U.S. productivity and policy, including the surge of growth in the late 1990s and early 2000s.
Social Media
Commentary
The politics of free speech in US schools today
by Michael Hansen and Annabelle Kim
Posted January 7, 2026 Reposted January 30, 2026
Claims that schools engage in systematic liberal indoctrination rely on cherry-picked evidence and overlook the complexity of how schools actually function.
While educators lean Democratic on average, their views are diverse, and research shows little evidence that classrooms are used to impose political beliefs on students.
Debates over free speech in schools are shaped less by ideology than by competing obligations to student learning, safety and constitutional limits, pressures intensified by social media and political polarization.
The Hutchins Center on Fiscal and Monetary Policy
About the Hutchins Center on Fiscal and Monetary Policy at Brookings
The mission of the Hutchins Center on Fiscal and Monetary Policy is to improve the quality and efficacy of fiscal and monetary policies and public understanding of them.
Research
Hutchins Center Fiscal Impact Measure
The Hutchins Center Fiscal Impact Measure shows how much local, state, and federal tax and spending policy adds to or subtracts from overall economic growth, and provides a near-term forecast of fiscal policies’ effects on economic activity.
FEDERAL, STATE AND LOCAL FISCAL POLICY AND THE ECONOMY
By Sarah Ahmad, Chase Parry, and Louise Sheiner
Posted January 23, 2026 Reposted January 30, 2026
The Hutchins Center Fiscal Impact Measure shows how much local, state, and federal tax and spending policy adds to or subtracts from overall economic growth, and provides a near-term forecast of fiscal policies’ effects on economic activity.
FEDERAL, STATE AND LOCAL FISCAL POLICY AND THE ECONOMY
By Sarah Ahmad, Chase Parry, and Louise Sheiner
We now release more detail about the FIM. In particular, we are making available a breakdown of the FIM into the effects of One Big Beautiful Bill Act (OBBBA), tariffs, the government shutdown, and more. See the Fiscal Impact Breakdown spreadsheet in the Downloads section.
Fiscal policy increased U.S. GDP growth by 0.3 percentage point in the third quarter of 2025, the Hutchins Center Fiscal Impact Measure (FIM) shows. The FIM illustrates the effect of fiscal policy on real GDP growth. It translates changes in taxes and spending at federal, state, and local levels into changes in aggregate demand. It also includes the supply side effects of fiscal policy and the effects of fiscal policy uncertainty on GDP growth.
The positive forecast for the third quarter reflects boosts from the One Big Beautiful Bill Act (OBBBA) and from the effects of the Inflation Reduction Act on equipment spending, partially offset by the effects of tariffs and weak underlying (non-OBBBA) federal spending.
Quote of the week
“If you look at the incoming data since the last meeting, [there is] clear improvement in the outlook for growth. The data have come in and sentiment, the beige book, everything [coming] in suggesting that this year starts off on...a solid footing for growth. Inflation performed about as expected. And...some of the labor market data came in suggesting evidence of stabilization. So it’s overall a stronger forecast,” says Jerome Powell, Chair of the Federal Reserve.
“After the three recent rate cuts, we’re well-positioned to address the risks that we face on both sides of our dual mandate, and we’ll continue to make our decisions meeting by meeting based on the incoming data [and] the implications...for the outlook and the balance of risks. The economy’s growing at a solid pace, the unemployment rate has been broadly stable, and inflation remains somewhat elevated.
“I think the upside risks to inflation and the downside risks have probably both diminished a bit. So we’ll be looking at that. It’s about how you weigh the risks to the two goals and...quantify them. And so there are different views on the committee and we'll find our way forward as the data evolve.”
Consumer confidence plunges to 12-year low
Chart courtesy of Financial Times
courteous OF the financial times
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