Stronger Together: AI Consortiums for Shared Nonprofit Resources and Learning
Collaborative AI consortiums are transforming how nonprofits access technology, share knowledge, and amplify impact. By pooling resources and expertise across organizations, these partnerships reduce costs, accelerate learning, and enable smaller nonprofits to access enterprise-level AI capabilities they could never afford alone.
Artificial intelligence holds tremendous promise for nonprofits, but the barriers to adoption—high costs, technical complexity, and knowledge gaps—can feel insurmountable, especially for small and mid-sized organizations. While large nonprofits may have the resources to invest in AI infrastructure and expertise, smaller organizations often find themselves priced out of transformative technology.
Enter AI consortiums: collaborative partnerships where multiple nonprofits pool resources, share infrastructure, and learn together. These alliances are reshaping what's possible in the social sector, democratizing access to powerful AI tools and accelerating the pace of innovation. From shared technology platforms to collective learning communities, consortiums offer a practical pathway for nonprofits to overcome resource constraints while maintaining their independence and mission focus.
The concept isn't entirely new—shared services models have existed in the nonprofit sector for decades, covering everything from office space to back-office functions. What's different now is the sophistication of AI technology and the collaborative infrastructure emerging to support its adoption. Organizations like NetHope, which unites over 60 global nonprofits with technology partners, demonstrate how collective action can multiply individual impact. When nonprofits band together, they gain negotiating power with vendors, access to expertise, and a community of peers facing similar challenges.
This article explores how AI consortiums work, the tangible benefits they deliver, models for collaboration, and practical steps your organization can take to participate in or create these powerful partnerships. Whether you're a small nonprofit looking to access AI capabilities for the first time or a larger organization seeking to maximize your technology investments through collaboration, there's a consortium model that can help you achieve your goals.
Understanding AI Consortiums: What They Are and How They Work
An AI consortium is a formal or informal partnership where multiple nonprofit organizations collaborate to share AI-related resources, infrastructure, knowledge, and expertise. Unlike traditional vendor relationships where each organization contracts separately for technology services, consortiums leverage collective purchasing power and shared learning to reduce costs and accelerate adoption.
These partnerships vary widely in structure and scope. Some focus primarily on cost savings through group purchasing agreements, negotiating discounted rates for AI tools and platforms that members access individually. Others go much deeper, implementing shared technology infrastructure where multiple organizations use the same systems, databases, and AI models. The most comprehensive consortiums combine both approaches while adding robust training programs, peer learning networks, and collaborative research initiatives.
What makes AI consortiums particularly powerful is their ability to address multiple barriers simultaneously. Cost is an obvious benefit—when ten organizations split the implementation cost of an enterprise AI platform, each pays a fraction of what they'd pay alone. But beyond cost savings, consortiums tackle the knowledge gap through structured learning communities where members share successes, failures, and practical insights. They provide access to technical expertise that individual organizations couldn't afford to hire, often through shared consultants, data scientists, or AI specialists.
Core Components of Effective AI Consortiums
Essential elements that make collaborative AI initiatives successful
- Shared governance structure: Clear decision-making processes, leadership roles, and participation agreements that ensure all members have voice and accountability
- Common technology infrastructure: Shared platforms, data storage, AI models, and integration tools that members can access according to their needs
- Knowledge-sharing mechanisms: Regular meetings, shared documentation, training programs, and communication channels for ongoing learning
- Coordinating entity or backbone organization: A dedicated coordinator (often funded by a lead funder) who facilitates collaboration, manages logistics, and ensures the consortium achieves its goals
- Data governance and privacy frameworks: Agreements on how data will be shared, protected, and used, especially critical when AI systems process sensitive beneficiary information
The backbone organization plays a particularly crucial role in consortium success. Research on collective impact initiatives shows that having a neutral third party to facilitate collaboration significantly increases the likelihood of achieving shared goals. This entity handles the administrative burden, coordinates meetings and learning opportunities, manages relationships with technology vendors, and ensures that smaller organizations aren't overshadowed by larger, better-resourced members.
Types of AI Consortium Models
Not all consortiums look the same. The structure you choose should align with your organization's needs, resources, and strategic goals. Here are the most common models emerging in the nonprofit sector.
Group Purchasing Consortiums
Collective buying power for discounted AI tools and services
The simplest consortium model focuses on negotiating group discounts with AI vendors. Members maintain their own separate implementations but benefit from reduced pricing achieved through volume commitments. This approach requires minimal coordination and allows organizations to maintain complete independence while still realizing cost savings.
Group purchasing works particularly well for software-as-a-service (SaaS) AI tools where vendors offer tiered pricing based on number of users or organizations. A coordinator negotiates the agreement, and members contract individually at the consortium rate. This model is ideal for organizations that want cost savings but aren't ready for deeper integration or shared infrastructure.
Best for: Organizations seeking immediate cost reduction with minimal commitment or those testing AI adoption before deeper collaboration.
Shared Services Consortiums
Pooled infrastructure and technical resources across organizations
Shared services consortiums go beyond purchasing to implement common technology platforms that multiple organizations use simultaneously. This might include shared data warehouses, AI model deployment infrastructure, or integrated systems for donor management, case work, or program evaluation.
A powerful example comes from Community Service Partners, which integrated the same information technology platform across six nonprofits, saving $100,000 initially plus an additional $96,000 from other efficiencies, with projections of $3 million in savings over five years. By sharing infrastructure costs—servers, databases, integration work, and maintenance—each organization pays far less than they would independently while accessing enterprise-grade systems.
This model requires more coordination and trust, since organizations must agree on system configurations, data standards, and operational procedures. However, it delivers significantly greater cost savings and can enable sophisticated AI capabilities—like predictive analytics or natural language processing—that would be prohibitively expensive for individual organizations.
Best for: Organizations willing to standardize some processes and systems in exchange for access to enterprise-level AI capabilities at a fraction of the cost.
Learning and Capacity-Building Consortiums
Collaborative communities focused on knowledge sharing and skill development
These consortiums prioritize education, training, and peer learning over shared infrastructure. Members participate in cohort-based learning programs, attend workshops, share case studies, and collaborate on pilot projects. Organizations like Team4Tech exemplify this model, supporting annual cohorts of 10 nonprofits as they develop and pilot AI use cases aligned with their missions, with emphasis on long-term sustainability through peer learning spaces.
Learning consortiums address one of the most significant barriers to AI adoption: the knowledge gap. By creating structured opportunities to learn from peers facing similar challenges, these networks accelerate the learning curve and reduce the costly trial-and-error that individual organizations might experience. Members benefit from shared playbooks, lessons learned, and practical guidance on everything from vendor selection to change management.
Many learning consortiums also provide access to expert facilitators, consultants, or technical advisors who would be unaffordable for individual members. The collective pays for this expertise, but each organization receives personalized guidance adapted to their specific context.
Best for: Organizations early in their AI journey seeking guidance, training, and a supportive peer community to accelerate learning and avoid common pitfalls.
Sector-Specific or Mission-Aligned Consortiums
Partnerships among organizations serving similar populations or causes
Some of the most effective consortiums bring together organizations working in the same sector or on related issues. NetHope, for instance, unites over 60 global nonprofits working on development, humanitarian, and conservation challenges with technology companies and funding partners. This sector focus allows for deeper collaboration because members face similar operational challenges, serve similar populations, and can benefit from specialized AI applications tailored to their work.
Sector-specific consortiums can develop and share AI models trained on relevant data. For example, organizations serving refugees might collaborate on natural language processing models for intake interviews in multiple languages, or youth development nonprofits might share predictive models for identifying students at risk of disengagement. Because they're not direct competitors (often serving different geographies), these organizations can share insights and resources more freely than would be possible across sectors.
Best for: Organizations seeking deep collaboration with peers who understand their specific programmatic challenges and can co-develop specialized AI solutions.
Geographic or Regional Consortiums
Local partnerships leveraging proximity and shared community context
Geography-based consortiums bring together nonprofits operating in the same city, region, or state. These partnerships leverage the advantages of proximity—easier in-person meetings, shared understanding of local context, and opportunities for cross-referrals and coordinated services. Geographic consortiums often emerge from existing collaborative networks like United Ways, community foundations, or nonprofit associations.
A regional consortium might pool resources to hire a shared data scientist who works with multiple organizations, or develop community-wide AI infrastructure for client intake and referrals. Because members serve the same geographic area, they can implement AI systems that track clients across organizations, reducing duplication and ensuring that individuals receive coordinated support.
Best for: Organizations serving the same community who want to coordinate services, reduce duplication, and maximize collective impact through shared AI systems.
The Compelling Benefits: Why Consortiums Work
AI consortiums deliver value that extends far beyond simple cost savings. While reduced expenses are certainly appealing, the strategic advantages often prove even more transformative. Let's examine the multifaceted benefits these partnerships provide.
Dramatic Cost Reduction and Resource Efficiency
The financial case for consortiums is compelling. By sharing infrastructure costs—servers, software licenses, implementation fees, and ongoing maintenance—organizations can access AI capabilities at a fraction of individual implementation costs. Cloud platforms make this increasingly feasible, allowing flexible scaling as consortium membership grows or shrinks without major upfront capital investments.
Consider the economics of hiring technical expertise. A skilled data scientist or AI engineer might command a $150,000+ salary that few small nonprofits can afford. But ten organizations sharing that cost each pay $15,000—suddenly affordable for many. The same logic applies to consultants, system administrators, and specialized training programs. Shared services extend these savings to almost any resource that doesn't directly fulfill an individual organization's unique mission.
Beyond direct cost sharing, consortiums gain negotiating leverage with vendors. Technology companies often provide substantial discounts to groups that commit to multi-organization contracts, recognizing both the volume opportunity and the marketing value of serving a consortium of recognized nonprofits. Organizations like TechSoup and NTEN have built their entire value proposition around facilitating these relationships, securing nonprofit-friendly pricing that individual organizations could never obtain alone.
Accelerated Learning and Reduced Risk
AI adoption is challenging, and mistakes are expensive. Consortiums dramatically reduce the learning curve by enabling members to learn from each other's experiences—both successes and failures. When one organization discovers that a particular AI tool doesn't work well for their use case, all members benefit from that knowledge. When another finds an effective implementation strategy, everyone can adapt that approach to their context.
This peer learning addresses a critical challenge identified by research: 69% of nonprofit AI users have no formal training. Learning consortiums provide structured educational opportunities that build capability across organizations simultaneously. Cohort-based programs, where organizations progress through implementation milestones together, create accountability and shared momentum that isolated organizations often lack.
The risk reduction extends to technology decisions themselves. Consortiums can pilot new AI tools or approaches collectively, spreading the financial and operational risk across multiple organizations. If a pilot fails, no single organization bears the full cost. If it succeeds, all members can scale the solution with confidence based on proven results.
Access to Enterprise-Grade Capabilities
Many powerful AI capabilities require sophisticated infrastructure, large datasets, and specialized expertise that only large organizations can typically afford. Consortiums democratize access to these enterprise-level tools by pooling resources to implement systems that would be unattainable individually.
This includes advanced analytics capabilities like predictive modeling, natural language processing, computer vision, and machine learning systems that require substantial computing power and data science expertise. A small nonprofit serving a few hundred clients annually cannot justify building these capabilities alone. But a consortium of ten similar organizations collectively serving thousands of clients suddenly has the scale, data, and resources to implement sophisticated AI that benefits everyone.
Cloud-based AI platforms make this particularly feasible. Organizations like AWS, Microsoft Azure, and Google Cloud offer nonprofit-specific programs and pricing, and their infrastructure scales efficiently to accommodate consortiums of various sizes. The technology architecture that once required millions in capital investment now becomes accessible through shared operating expenses spread across multiple organizations.
Stronger Data Governance and Privacy Protections
Paradoxically, sharing resources through a consortium can actually enhance data security and privacy protections. By pooling resources to hire information security expertise and implement robust data governance frameworks, consortiums can afford security measures that individual organizations—especially smaller ones—could never justify alone.
A consortium can employ a Chief Information Security Officer or contract with specialized cybersecurity firms to conduct regular audits, penetration testing, and compliance reviews. They can implement enterprise-grade encryption, multi-factor authentication, and intrusion detection systems across all member organizations. These protections become especially critical when working with sensitive beneficiary data that nonprofits are ethically and often legally obligated to protect.
The collaborative process of developing shared data governance policies also elevates the conversation within each organization. Rather than individual staff making ad hoc decisions about data use, consortiums formalize policies around consent, data minimization, retention schedules, and ethical AI use. This structured approach to data governance reduces risk and builds public trust.
Network Effects and Cross-Organization Innovation
Perhaps the most exciting benefit of AI consortiums is the innovation that emerges from cross-pollination of ideas. When diverse organizations bring different perspectives, experiences, and use cases together, they often identify novel applications of AI that no single organization would have conceived independently.
A consortium creates a safe space for experimentation and creative problem-solving. Members can test unconventional ideas with peer support, brainstorm solutions to shared challenges, and adapt innovations from other sectors to nonprofit contexts. This collaborative innovation accelerates the pace of progress across the entire sector.
Network effects also compound over time. As consortiums grow and mature, they attract additional members, technology partners, and funders interested in supporting collective action. Successful consortiums often become magnets for resources and opportunities that benefit all participants, creating a virtuous cycle of growth and impact.
Getting Started: Joining or Creating an AI Consortium
Whether you're considering joining an existing consortium or exploring the creation of a new collaborative partnership, the path forward requires careful planning and strategic thinking. Here's how to approach both scenarios.
Joining an Existing Consortium
Research Available Options
Identify consortiums that align with your mission and needs
Start by investigating existing AI consortiums in your sector, region, or issue area. NetHope, AI for Nonprofits (AI4NP), and similar initiatives maintain public-facing information about their programs and membership criteria. Reach out to peer organizations to learn about their consortium participation and experiences.
Don't limit your search to formally branded "AI consortiums." Look for shared services partnerships, technology collaboratives, or collective impact initiatives that are incorporating AI into their work. Many existing nonprofit networks are evolving their offerings to include AI-related resources and learning opportunities.
Consider both the structure and focus of available consortiums. Does their model emphasize cost savings, shared infrastructure, learning, or all three? Does their focus align with your organization's strategic priorities? What is the time commitment expected of members, and do you have capacity to participate meaningfully?
Evaluate Fit and Value Proposition
Assess whether consortium membership aligns with your needs and capacity
- Mission alignment: Do consortium members serve similar populations or work on related issues? Will collaboration enhance or distract from your core mission?
- Technical readiness: What is the expected baseline of technical capacity? Will your organization need significant foundational work before you can benefit from advanced offerings?
- Financial commitment: What are the membership fees or participation costs? Do they include access to technology, training, consulting, or all three? How does this compare to what you'd pay independently?
- Governance structure: How are decisions made? Will your organization have meaningful input, or is the consortium dominated by larger, better-resourced members?
- Data sharing expectations: What data will you need to share? Are there robust privacy protections and clear data governance policies?
Prepare Your Organization for Participation
Build internal readiness before committing to consortium membership
Successful consortium participation requires internal preparation. Start by educating your leadership about what consortium membership entails—the benefits, costs, time commitments, and expectations. Secure board-level support for participation, especially if membership requires data sharing or system integration that affects organizational operations.
Identify a consortium liaison—someone on your staff who will serve as the primary point of contact, attend meetings, and coordinate your organization's engagement. This person needs sufficient authority to make decisions and access to leadership when major strategic questions arise. Ensure they have protected time in their workload for consortium activities; treating this as "extra" work on top of a full job description leads to disengagement and poor outcomes.
Assess your technical infrastructure before joining. If the consortium offers shared systems or platforms, ensure your existing technology can integrate with those tools. Address any foundational data management issues—data quality problems, scattered information systems, or lack of documentation—that might prevent you from fully leveraging consortium resources.
Creating a New Consortium
Identify Potential Partners and Shared Needs
Build the case for collaboration with peer organizations
Creating a new consortium starts with relationship-building and needs assessment. Reach out to peer organizations facing similar AI challenges—perhaps organizations you already collaborate with through collective impact initiatives, shared funding relationships, or sector networks. Host informal conversations to explore whether there's appetite for formal collaboration.
Conduct a shared needs assessment across potential partner organizations. What AI capabilities would benefit multiple organizations? Where are current costs prohibitively high for individual adoption? What learning gaps exist across organizations? The stronger the overlap in needs and challenges, the more compelling the case for consortium formation.
Start small and focused rather than attempting to build a comprehensive consortium immediately. Perhaps begin with a group purchasing agreement for a specific AI tool that multiple organizations want to adopt. Or organize a learning cohort around a particular AI application relevant to your sector. Early wins build trust and momentum for expanded collaboration.
Secure Backbone Organization and Funding
Establish infrastructure and resources to support collaboration
Research on collective impact shows that successful collaborations nearly always have a backbone organization or dedicated coordinator to facilitate the work. This cannot be an afterthought or something handled "on the side" by busy staff from member organizations. Identify or create an entity to serve this coordinating function.
This might be an existing organization like a community foundation, nonprofit association, or capacity-building intermediary that adds consortium coordination to its portfolio. Or it might be a new entity created specifically for this purpose. The coordinator needs legitimacy, trust from all members, and neutrality—they cannot be perceived as favoring certain organizations over others.
Secure dedicated funding for consortium operations. The vast majority of successful shared service collaborations have a lead funder that subsidizes startup costs. Approach foundations, corporate funders, or technology companies with AI philanthropy programs. Frame the funding request around the collective impact potential—how the consortium will multiply the effectiveness of their investment across multiple organizations and beneficiaries.
Develop Governance and Operating Agreements
Create clear structures for decision-making and collaboration
Establish a governance structure that ensures all members have voice while enabling efficient decision-making. This typically includes a steering committee with representatives from each member organization, regular all-member meetings, and clear protocols for how decisions get made (consensus, voting, executive authority for certain types of decisions).
Draft operating agreements that address potential points of friction before they become problems. How will costs be shared—equally, proportionally based on organization size, based on usage? How can members exit the consortium if it's not working for them? What happens to shared infrastructure and data if the consortium dissolves? How will intellectual property created through the collaboration be owned and shared?
Pay special attention to data governance agreements. If the consortium will involve shared data systems or AI models trained on member data, establish clear policies around data privacy, security, usage rights, and compliance with regulations. Consult legal expertise to ensure agreements protect all parties and meet ethical standards for beneficiary data protection.
Start with Pilot Projects and Build Incrementally
Prove value through focused initiatives before scaling
Resist the temptation to design the perfect comprehensive consortium from day one. Start with a focused pilot project that delivers tangible value quickly. This might be a shared procurement for a specific AI tool, a cohort learning program on a particular application, or a pilot implementation of shared infrastructure for one specific function.
Early successes build trust and enthusiasm for expanded collaboration. They also provide practical learning about what works in your specific context—what governance structures are effective, how to manage communication, what technical infrastructure is needed, and where friction points emerge. Use these insights to refine your approach before scaling.
Document and share results from pilot projects, both successes and challenges. Transparency builds trust among members and provides evidence to attract additional funding or recruit new members. Celebrate wins publicly to demonstrate the value of collaborative approaches and encourage broader sector adoption of consortium models.
Navigating Common Challenges
While AI consortiums offer tremendous benefits, they also introduce complexities that don't exist when organizations work independently. Understanding and preparing for these challenges increases your likelihood of success.
Balancing Autonomy with Collaboration
Nonprofits fiercely protect their independence and mission autonomy—for good reason. Consortium participation inevitably involves some compromise as organizations align on shared standards, systems, or approaches. The key is finding the right balance where collaboration enhances rather than constrains individual organizational effectiveness.
Address this by clearly distinguishing between what must be standardized for collaboration to work (data formats for shared systems, for example) and what can remain flexible (how each organization uses those systems for their specific mission). Design governance structures that require consensus only for core decisions, giving members autonomy for implementation details.
Managing Power Dynamics and Resource Imbalances
Consortiums often include organizations of vastly different sizes, resources, and technical sophistication. Without careful management, larger organizations can dominate decision-making, leading to solutions that work well for them but don't meet the needs of smaller members. This creates resentment and eventually leads to disengagement.
Combat this through intentional governance design. Consider governance structures where each member organization gets one vote regardless of size, or where decisions require support from both large and small organizations. The backbone organization should actively amplify voices from smaller members and ensure all perspectives inform decisions. Some consortiums use facilitation techniques like round-robin speaking or written input collection to prevent dominant voices from overshadowing quieter members.
Sustaining Engagement Over Time
Initial enthusiasm for consortium participation often wanes as the reality of ongoing meetings, coordination requirements, and competing organizational priorities sets in. Maintaining active engagement across all member organizations requires continuous effort and attention to value delivery.
Counter this challenge by ensuring the consortium consistently delivers tangible value, not just meetings and discussion. Create quick wins and regular success stories that remind members why participation matters. Use diverse engagement mechanisms—not everyone needs to attend every meeting, but everyone should have opportunities to participate meaningfully. Conduct regular surveys or check-ins to understand member satisfaction and address concerns before they lead to disengagement.
Addressing Data Privacy and Security Concerns
When consortiums involve shared data systems or AI models, member organizations legitimately worry about data security, privacy, and their legal obligations to protect beneficiary information. These concerns can prevent organizations from fully participating in shared infrastructure even when the benefits are clear.
Address data concerns proactively through comprehensive data governance frameworks developed with legal and ethical expertise. Implement technical safeguards like data encryption, access controls, and audit trails that provide confidence in security. Consider data segmentation approaches where organizations can participate in shared infrastructure while maintaining complete control over sensitive beneficiary data—perhaps sharing aggregated or anonymized data for analytics while keeping individual-level data within their own systems.
Funding Sustainability Beyond Initial Grants
Many consortiums launch with generous startup funding from foundations or corporate sponsors, but struggle when that initial support ends. Building financially sustainable models that don't rely on perpetual external funding requires careful planning and sometimes difficult conversations about cost sharing among members.
Design sustainability into the consortium from the beginning. Develop clear cost-sharing models where members understand their ongoing financial obligations beyond startup phases. Explore revenue-generating services the consortium might offer to a broader market—consulting, training, or technology solutions that generate income to support operations. Some consortiums find sustainability through a mix of member dues, fee-for-service offerings to non-members, and ongoing (but reduced) philanthropic support.
The Future of Nonprofit AI Consortiums
As AI technology continues to advance rapidly, consortiums will become increasingly important for ensuring that the nonprofit sector can keep pace. The organizations that thrive in the coming decade will be those that recognize the power of collaboration and actively participate in building collective AI capacity.
We're likely to see consortiums become more sophisticated and specialized. Early consortiums focused primarily on cost reduction and basic learning, but emerging partnerships are developing sector-specific AI models, creating shared data infrastructure for collective impact measurement, and coordinating advocacy around AI policy and regulation. Some consortiums are even partnering with academic institutions to conduct research that advances the state of knowledge about effective AI use in the social sector.
The rise of agentic AI and autonomous systems will make consortiums even more valuable. These advanced AI capabilities require substantial infrastructure and expertise to implement safely and effectively. Consortiums will enable smaller organizations to access these powerful tools through shared infrastructure that would be impossible to build independently.
Cross-sector partnerships will also expand, with nonprofits, government agencies, academic institutions, and private sector partners collaborating through consortium structures. These partnerships can accelerate innovation by bringing together diverse expertise, datasets, and perspectives. Public-private partnerships focused on social good applications of AI represent a particularly promising frontier.
Perhaps most importantly, consortiums will play a crucial role in ensuring equitable access to AI capabilities across the nonprofit sector. Without collaborative models, a dangerous divide could emerge between well-resourced organizations that can afford cutting-edge AI and smaller organizations left behind with outdated tools and approaches. Consortiums can bridge this gap, democratizing access to technology that amplifies mission impact regardless of organizational size or budget.
Conclusion: Collective Action for Greater Impact
The challenges nonprofits face—resource constraints, technical complexity, knowledge gaps—can feel overwhelming when confronted alone. But they become surmountable when organizations join together through AI consortiums that pool resources, share expertise, and accelerate learning.
Consortiums represent more than just cost-saving mechanisms; they embody a fundamentally collaborative approach to building technological capacity across the sector. By working together, nonprofits can access enterprise-grade AI capabilities, reduce implementation risks, and move faster than would ever be possible independently. The organizations pioneering these partnerships today are not just benefiting themselves—they're building infrastructure and models that will serve the entire sector for years to come.
Whether you join an existing consortium or create a new collaborative partnership, the key is taking action. Start conversations with peer organizations about shared challenges and opportunities. Investigate existing consortiums in your sector or region. Reach out to backbone organizations that could facilitate collaboration. The perfect consortium may not exist yet—but with leadership, vision, and commitment, you can help build it.
The nonprofit sector has always succeeded through collaboration and mutual support. AI consortiums simply extend that tradition into the technological realm, ensuring that the transformative potential of artificial intelligence benefits all organizations working to create positive change—not just those with the largest budgets. Stronger together isn't just a slogan; it's a practical strategy for thriving in an AI-enabled future.
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