Blair AI

Jeffery Chan, Co-Founder and Chief Revenue Officer

Converting AI Adoption into Lasting Revenue

Jeffery Chan

Jeffery Chan is co-founder and chief revenue officer of Blair AI, which helps clinics automate processes including referral management, patient calls, appointment scheduling and routine inquiries through AI. Drawing on his experience in private equity, software investment and financial services, Chan assesses commercial potential and buyer priorities to identify organizations ready to adopt AI. He then determines whether Blair AI can meet their needs and turns successful implementations into long-term customer partnerships.

Translating Problems into Priorities

Chan evaluates business growth through three interconnected outcomes—acquiring new customers, expanding existing accounts and sustaining the revenue generated through both. New contracts have limited value if customers eventually leave, while account expansion becomes unsustainable if employees are overextended or the technology cannot deliver what was promised. Balancing these outcomes, he has helped Blair AI achieve hypergrowth, with the business more than tripling annually.

Determining whether an opportunity can generate sustainable revenue requires Chan to assess the customer’s problem before pursuing the sale. His investment experience taught him to examine the severity of the pain point, the feasibility of the proposed solution and its likely return. His financial services background helped him evaluate the opportunity from a decision-maker’s perspective and explain the technology in terms that connect with business priorities.

Chan applies this combined perspective at Blair AI by examining how each customer operates, where AI can improve existing workflows and what organizational changes adoption will require. This allows the team to determine whether the technology can solve the problem and what support the customer will need to integrate it successfully and produce measurable results. The value of this approach is reflected in customer demand, with clients often requesting an expanded product set within two to three months of launch.

"New Contracts Have Limited Value If Customers Eventually Leave, While Account Expansion Becomes Unsustainable If Employees are Overextended or The Technology Cannot Deliver What Was Promised."

Blair AI then decides whether to pursue the opportunity immediately or revisit it later. If waiting would cause the customer to select another provider, the company assesses whether the potential revenue justifies the development and delivery resources required. If the customer can wait, its requirement can instead inform future product development, fundraising and pipeline planning.

Identifying Buyers Prepared to Proceed

Blair AI’s willingness to pursue an opportunity is only one part of the decision. Organizations that remain curious but uncommitted can consume substantial resources without moving towards implementation.

Chan assesses buyer readiness by looking for a defined problem, an identifiable decision-maker and clear evaluation criteria. After the first meeting, he proposes a concrete next action. The organization’s response reveals whether it is willing and able to take the opportunity forward.

When readiness is evident, attention shifts from the organization as a whole to the individuals influencing the decision. In complex healthcare and financial services purchases, different stakeholders evaluate different concerns, including clinical outcomes, data privacy, regulatory compliance and financial ROI. Chan addresses each stakeholder’s specific evaluation criteria, helping them understand the solution’s relevance and support it internally.

"Understanding How Customers Operate And How Their Needs Evolve Allows A CRO To Remain Involved Beyond The Sale and Contribute To Their Long-Term Success."

Their support also depends on credible expectations. Chan explains what Blair AI can accomplish and what remains outside its capabilities. Such candor may end an unsuitable opportunity, but it prevents customers from forming expectations the company cannot fulfill. Chan has found that such honesty can strengthen trust, with prospects later expressing greater confidence in entering a partnership with Blair AI.

Once expectations are aligned, Blair AI examines the specific risk preventing the purchase. If uncertain ROI is the concern, the commercial arrangement can connect payment to predetermined results. Structuring the engagement around the downside the buyer wants to avoid reduces a defined barrier rather than addressing risk in general terms.

Aligning Commitments with Capacity

Making adoption less risky for the customer only works if Blair AI can deliver what it has promised. Chan, therefore, aligns product commitments with internal capacity

The revenue team presents evidence of customer demand, while the product team determines whether meeting it supports the company’s objectives and justifies the resources required. Those objectives are not limited to generating immediate sales. Blair AI has also invested in infrastructure that reduces the computing cost of delivering its services.

Market feedback helps the teams decide when product development is warranted. Losing one opportunity because of a missing capability may not justify changing the roadmap.

Repeated losses for the same reason, however, can indicate that the capability addresses a wider market need.

Internal capacity is only one consideration in deciding which customers Blair AI should pursue. The organization’s ability to move through evaluation and procurement also matters. Although healthcare and financial services are heavily regulated, not every organization follows the same purchasing timeline. Smaller organizations may be able to evaluate and adopt AI faster than large hospitals or banks. Blair AI adjusts its ideal customer profile as its capacity and market position develop, pursuing buyers whose needs and decision-making timelines suit its stage of growth.

The company balances these targets across its pipeline. Faster-closing opportunities generate near-term revenue and maintain delivery momentum, while larger organizations progress through longer procurement processes. Managing this mix according to contract value, sales cycle and cash burn allows Blair AI to pursue major accounts without depending on them for immediate revenue. It also reduces the risk of relying too heavily on one type of customer, such as only large hospitals or banks.

Turning Pilots into Operational Results

Careful customer selection and realistic commitments create the conditions for implementation, but they do not guarantee its success. Chan identifies momentum with the right partners, excellence in execution and implementation and clearly demonstrated ROI as the three factors that distinguish AI providers in a crowded market.

Credible partners matter because regulated organizations often wait for early adopters to demonstrate that a solution works before proceeding themselves. Blair AI further strengthens its market position by engaging with key opinion leaders and contributing to healthcare discussions on AI policy, advocacy and governance.

Market credibility must then be supported by operational performance. More than 80 percent of AI pilots fail to reach full implementation, while Blair AI has converted almost every pilot into a full launch. Some customers have requested additional products or use cases before their initial pilots concluded.

The outcome must also suit the customer’s operating environment. Reducing healthcare headcount may appear to demonstrate financial savings, but unionized workplaces can make that result unrealistic. More relevant measures may include relieving administrative pressure and reducing burnout or turnover among front-office employees.

Employee satisfaction, therefore, becomes an important measure of lasting adoption. Technical performance alone cannot show whether a solution will remain in use. When employees recognize its value, the technology becomes part of their workflows, strengthening customer retention.

Sustained adoption also creates opportunities for account expansion. Instead of treating implementation as the end of the revenue process, Chan maintains a solutions-engineering mindset after launch. Positioning Blair AI as a partner encourages customers to share operational information and discuss emerging challenges, allowing the team to identify related problems that were not visible during the initial sales process.

Blair AI’s customer contracts typically quadruple in value during their first 12 months. With a focused ideal customer profile, a capability developed for one organization can often address a similar requirement elsewhere, strengthening the product roadmap and extending its value across the customer portfolio.

Chan encourages future revenue leaders to combine strategy, operations and execution with genuine curiosity. Understanding how customers operate and how their needs evolve allows a CRO to remain involved beyond the sale and contribute to their long-term success.