Your TL;DR: EBHC has been accepted into the Verizon Digital Ready AI Accelerator, an opportunity to deepen how we use artificial intelligence across our business. For us, the value of AI is not replacing expertise or judgment. It is finding responsible ways to make research, analysis, internal operations, and client work more efficient while preserving the rigor that government funding demands.
We are excited to share that EBHC has been accepted into the Verizon Digital Ready AI Accelerator.
The program comes at an interesting point in our own use of artificial intelligence. AI is already changing how businesses research, analyze information, manage knowledge, develop processes, and make decisions. Those changes matter to EBHC because our work sits in environments where information is dense, deadlines are real, and accuracy matters.
Funding research requires teams to interpret opportunity requirements rather than simply locate opportunities. Grant development requires strategy, evidence, compliance, and a clear understanding of what an agency is actually asking an applicant to accomplish. Evaluation requires careful analysis of what happened, what changed, and what the available evidence can reasonably support. Those are all areas where AI can create efficiency, and they are also areas where unchecked automation can create very expensive mistakes.
That tension is exactly why we are interested in this work.
Why an AI Accelerator Makes Sense for EBHC
Our interest in AI has never been about using technology simply because it is available. The more useful question is whether a particular application improves the quality, consistency, speed, or accessibility of the work without weakening the judgment behind it.
That distinction matters in non-dilutive funding.
A system can find a solicitation and summarize it. Someone still needs to determine whether the opportunity is genuinely aligned with the organization, whether the applicant can meet the eligibility and technical requirements, whether the timeline is realistic, and whether pursuing the award makes strategic sense.
AI can accelerate portions of proposal development. It cannot make weak evidence strong, manufacture organizational readiness, or substitute for understanding how reviewers will evaluate the proposed work.
The same applies to evaluation. AI can support data organization, analysis, synthesis, and workflow efficiency, yet evaluators remain responsible for deciding what the evidence means and whether a conclusion is defensible.
The gap appears when organizations confuse faster information processing with better decision-making. AI can dramatically increase the amount of material a team can process, while poor implementation can simply allow that team to reach the wrong conclusion faster.
Organizations experimenting with AI may want to examine where the technology is improving a decision versus where it is merely accelerating a task.
Learning Where AI Belongs, and Where It Does Not
Participation in the Verizon Digital Ready AI Accelerator gives EBHC an opportunity to continue developing that distinction in a structured setting.
As part of the program, participants are completing AI-focused professional development, including the Grow with Google AI Professional Certificate curriculum. The broader accelerator experience will provide an opportunity to examine AI applications through the lens of actual business operations rather than treating AI as a standalone technology exercise.
That is particularly valuable to us.
EBHC works with innovators, startups, universities, nonprofits, ecosystem builders, and organizations pursuing or managing government-funded projects. Our clients frequently operate with limited capacity while navigating substantial requirements. A founder preparing an SBIR/STTR proposal may also be managing product development, customer discovery, partnerships, and fundraising. A project director overseeing a government award may be responsible for implementation, data collection, stakeholder coordination, reporting, and compliance simultaneously.
Efficiency matters in those environments.
So does getting the answer right.
AI Does Not Eliminate the Need for Expertise
One of the most consequential misunderstandings about AI is the assumption that access to information and the ability to generate an answer are equivalent to expertise.
They are not.
Government funding illustrates the difference particularly well. A funding opportunity can contain dozens or hundreds of pages of instructions, attachments, evaluation criteria, eligibility provisions, technical requirements, and agency-specific expectations. Extracting those requirements is useful. Understanding which requirements materially affect competitiveness requires another level of judgment.
The same issue emerges during proposal development. AI can generate polished language remarkably quickly. Polished language, however, does not rescue a proposal with weak objectives, unsupported market assumptions, an implausible work plan, or a commercialization strategy that fails to distinguish the end user from the purchasing decision-maker.
This is where EBHC sees the greatest opportunity for thoughtful AI adoption. The technology can reduce friction around portions of the work so experienced people have more capacity for the reasoning that deserves their attention.
That is a considerably different objective from automating everything that can technically be automated.
What We Want to Learn Through the Accelerator
Our participation is also an opportunity to pressure-test how we think about AI inside EBHC.
Where can it shorten repetitive internal processes? Where can it improve knowledge management? Which research activities can become faster without reducing the quality of the underlying analysis? How can AI support consistency across complex workflows? Where does human review need to remain deliberately intensive?
Those questions are operational, not theoretical.
A useful AI implementation should eventually be visible in the quality of the work around it. Teams should spend less time searching for information they already have. Processes should become easier to repeat. Research should become more manageable. Staff should have more capacity for analysis, client communication, and strategic decision-making.
If an AI tool adds another platform, another review burden, or another source of information that someone must verify without producing meaningful value, adoption alone is not progress.
What This Means for Our Clients
Clients should not expect EBHC to suddenly replace thoughtful consulting with automated output. Quite the opposite.
We are interested in using AI to reinforce the parts of our work that clients rely on: disciplined research, stronger analysis, clear communication, careful review, and informed strategic guidance.
That may mean improving how we organize and assess large volumes of funding information. It may mean creating better internal systems for managing institutional knowledge. It may mean identifying responsible ways to streamline portions of research or administrative work so that more attention can be directed toward strategy and interpretation.
The standard remains the same. The work needs to withstand scrutiny.
Teams considering their own AI adoption may find it useful to ask a similar question: if this technology saves time, where will that newly available time create the most meaningful value?
We Are Excited to Get Started
Being accepted into the Verizon Digital Ready AI Accelerator gives EBHC another opportunity to learn, test, refine, and challenge our assumptions about where artificial intelligence belongs in a professional services firm.
We expect some applications to prove genuinely valuable. Others may turn out to be more impressive in a demonstration than useful in day-to-day operations. Knowing the difference is part of the work.
That is what makes this opportunity exciting for us. We are not interested in adopting AI for the sake of saying that we use AI. We are interested in understanding where it can make EBHC better at what we already do and where human expertise should remain firmly in charge.
We look forward to sharing what we learn along the way.