In April, in partnership with Axim Collaborative, the Christensen Institute convened a group of over 40 tool providers, funders, and researchers in the navigation and guidance space to answer a key question: How might we shape an AI-enabled navigation market that helps more students access the people, relationships, and opportunities that drive persistence and economic mobility?
We wanted to unpack this topic for a few reasons. Student navigation and support are chronically hard to scale—the ratios of advisors to students are punishing, logistical hurdles abound, and the choices and barriers students face across their college and career journeys are increasingly complex.
But those challenges can be tackled head-on. The rise of AI creates a real opportunity to deliver highly personalized, on-demand guidance at scale. At the same time, that opportunity carries risk. Specifically, organizations may be tempted to scale AI-enabled support in ways that replace rather than expand students’ networks and the relationships they need to get by and get ahead.
We wanted to explore how to build the conditions for a healthy market that offers students the best of both worlds: cutting-edge, high-tech tools that provide on-demand information and support and reliable, high-touch relationships that offer empathy, wisdom, and opportunities.
To do that, our convening brought together participants working across the student journey—from K–12 college and career readiness through postsecondary and workforce—to help students make choices about their futures and overcome the barriers that arise, particularly among students furthest from opportunity.
Throughout the day, five main themes emerged around what it will take to create a fundamentally more effective, equitable, and prosocial navigation market that leverages technology without sacrificing connections.
1. Say what goes unsaid
The landscape for navigation tools is marked by real momentum—growing funding interest in advising and coaching, promising research on high-tech, high-touch delivery models, and a wave of new providers spanning early career exploration through launching a career, and the myriad transitions along the way.
But despite excitement about the possibilities of tech-enabled navigation, the market remains highly fragmented, and several important dynamics go unsaid. Student transitions are high-stakes, yet institutions aren’t accountable for supporting those transitions. Demand for tools is also shortsighted, creating a market of point solutions rather than a coherent system that attends to students’ needs across institutions and career journeys. Siloed solutions mean siloed data, and as a result, evidence of long-term impact is limited. And while generative AI is lowering the cost of information delivery, it’s also introducing relational risks by replacing human interactions with AI that the field must acknowledge and reckon with.
The data paints a striking picture of the gap AI is being asked to fill: 65% of young people report they’re still trying to figure out what motivates them. Meanwhile, although an estimated half of jobs come through personal connections, 47% of Gen Z workers say they get better career advice from ChatGPT than from their managers, and 32% are already turning to AI for life advice. This data suggests that, whatever its limitations, young people are turning to technology when human relationships or other sources of information and support fall short.
On the institutional side, the picture is one of lagging adoption and persistent structural barriers. Tyton Partners’ “Driving Toward A Degree” study highlights that while students are leading the charge in using and paying for generative AI tools, only 3% of administrators and frontline staff are tapping into those same capabilities—and non-users are the most likely to believe AI simply isn’t suited for advising.
The barriers aren’t just technological: caseloads, coordination challenges, and access issues (such as inconvenient hours and limited awareness of available supports) all persist. Institutions with larger caseloads are more likely to lean on technology, and integration platforms are helping track students who never make it to a meeting. Still, usage and engagement remain uneven, and trust in AI remains low.
These dynamics don’t always match the rhetoric that edtech providers, funders, and advocates use when describing navigation and guidance models in the age of AI, but they’re the realities the field needs to contend with. To move toward a better market, research, advocacy, and funding strategies need to acknowledge these inconvenient truths head-on if we hope to effect real change.
2. Define quality for today and tomorrow
Although students are each on a lifelong learning and career journey, the market of tools and solutions is oriented around particular pain points and transitions that institutions manage.
With platforms tackling different parts of the student journey, it’s difficult to create a shared definition of what “good” looks like, or to collect data in rigorous ways that demonstrate impact. Without that shared definition, decision-makers default to cost or convenience, and neither institutions nor platforms are held accountable for quality.
Part of the challenge is whether to benchmark quality against the system we have or the system we want. Conversations about improving navigation often conflate those two—overstating what’s possible today while underselling what systems could become with the right alignment of incentives, tools, and human supports.
Building better systems will require more states, districts, and institutions to align around existing definitions of quality (such as CARA’s high-quality advising framework and Strada’s high-quality coaching standards) and to ensure those standards shape everything from RFPs and procurement decisions to effective implementation.
At the same time, those frameworks may not capture the full possibilities of a future where students have access to more high-tech, high-touch supports across more flexible pathways. AI-enabled tools could be a bridge to that future, but to get there, the field will likely need bolder definitions of quality that encompass purposeful work, financial security, strong relationships, health, and civic engagement— none of which are currently shaping usage and procurement decisions (the demand side of the market).
3. Break down silos
Everyone working in the navigation and guidance space knows that students need more than they’re currently getting through both tools and relationships.
That strategic and moral clarity needs to translate into more coordinated action: strategic tool integrations, communities organized around shared problems of practice, and rigorous debates about how to define quality and measure impact across the student journey. This requires the willingness to work together, not just among providers but also among the states, districts, and institutions that procure, evaluate, and implement solutions. Breakthrough results will likely require breakthrough collaborations.
The good news is there are some signs of an appetite to come together, even among competitors. Participants at the convening modeled a small version of that: 11 platforms that participated in the Christensen Institute’s Networked Guidance Pilot Fund shared publicly what they built and learned through a rapid-cycle pilot of human relationship-building features and functionalities.
Bringing providers together around a novel challenge–rather than their core business model– offered a promising entry point to generating collaboration. Providers could also collaborate to create data standards and a public-good, career-readiness framework that the field could build on, rather than each vendor recreating independently.
Additionally, philanthropy could push beyond just communities of practice to collaborative implementation, supporting deeper technology integrations among tool providers already operating in shared markets.
4. Influence the hyperscalers
Although they weren’t in attendance, the influence of frontier AI labs like OpenAI, Google, and Anthropic was palpable throughout the day. Even when purpose-built tools exist, students are going directly to Claude and ChatGPT for navigation and career support.
That raises a pressing question: how do you either redirect students to purpose-built tools or ensure the tools they’re already using provide high-quality guidance and connect them to advisors, mentors, and peers—not just chatbots?
Pushing consumer platforms to deliver higher-quality, more relationship-rich support is one lever, but making hyperscalers better at this could undercut many smaller, purpose-built platforms in the space. The field needs to navigate that tension intentionally: deciding where local context and wraparound services should drive tool development, where purpose-built education tools add irreplaceable value, and where shaping what frontier labs deliver direct-to-consumer is the best path to ensuring quality at scale.
One path forward is for a certification body to define and maintain quality standards for the field. Built on a career navigation efficacy standard, it could expand to play a systems integration and change management function—creating the pathways and credentials needed to push large language models (LLMs) toward delivering both higher-quality information and networking support to support career navigation.
5. Demonstrate what’s possible
Like any complex challenge in education, implementation is where good ideas meet reality.
Institutions are inundated with a saturated and fragmented market, which makes it hard to make good decisions and implement them well. There’s a lack of concrete implementation models, limited insight into how implementation support is actually used, and incentives that reward short-term thinking over collaboration. Decision-makers default to cost reduction, and institutions are largely solving their problems in isolation.
The best tools and models can’t overcome those very real challenges, and the field needs more examples of high-quality tools working in concert across the student journey to change that.
One way to tackle this is a vetted marketplace of quality vendors paired with institutions running concrete implementation models, organized as a community of practice, with clusters of similar institutions focused on shared problems rather than shared products.
Looking ahead
The window to shape the navigation and guidance market is open, but it won’t stay that way.
As AI tools proliferate, students increasingly turn to general-purpose chatbots to navigate some of the most consequential decisions of their lives while institutions remain slow to integrate these tools, leaving the field to face a choice: let market forces drive toward cheap, fragmented, and relationally thin solutions, or coordinate around something better.
That means aligning across product innovation and integration, shared definitions of quality, strong and diverse partnerships, and state-level coordination…not as a wish list but as a genuine fieldwide agenda. Both tool providers and funders will need to forge tighter partnerships and shared definitions of quality if we hope to overcome the market failures and risks currently hindering impact. Getting this right—for the students who need it most—is exactly the kind of challenge that collective action was made for.
