Verify, https://credsverse.com/credentials/5c9a43a5-c17e-4698-aae0-ab83ccf5edf3

Board Role

Founder and CEO, MBA Standards Board™, since December 2019
Executive Director, MBA Leaders Forum, since March 2026

Professional Biography

Cheryl Nunn is CEO of the MBA Standards Board™, where she leads a modern standard for business credentials in an AI-driven hiring market. Her organization turns real work experience into verified credentials that employers, recruiting software and AI search can confirm in seconds. She also founded the MBA Leaders Forum, a professional association for MBA-level leaders, and serves as its Executive Director.

Before MBA Standards Board™, Cheryl spent fifteen years as CFO and CEO of ALG Group, a book publishing company focused on business and executive education titles. She brought early digital workflows, print-on-demand and e-books to a slow, manual industry. In 1998 she built the company with a 99 percent remote workforce, decades before remote work became the norm. Earlier in her career she held a NASD Series 24 principal license, managing a brokerage office, and was recognized as a top national producer in financial services. She also served in advisory and public service roles in Utah focused on workforce development and higher education policy.

Cheryl is the author of GET FOUND. GET HIRED. How to Turn Verified Credentials Into Job Offers, which shows job seekers, career changers and rising professionals how to compete on proof instead of claims. She writes the weekly Credential Edge: AI Advantage newsletter on credentialing, AI hiring and the future of work.

She advocates for credentials that are affordable, competency-based, renewable and machine-readable. Every credential MBA Standards Board™ issues is registered in the Credential Engine Registry, because she believes trust in the AI era has to be verifiable and human judgment has to stay in the loop.

One Question, Answered

Question. If AI screens my profile before a person ever reads it, what can I do this week to get found?

Make your proof readable by machines, not just people. AI screening tools trust what they can verify, so a credential with a live verification link, registered in a public registry like the Credential Engine Registry, carries more weight than a degree or skill you simply typed in. Start by adding the verification URL to every credential on your LinkedIn profile that has one. Then use the exact skill terms employers search for, because the software can only find what you name.

Areas of Expertise

1. Skills-Based Hiring

2. Verified and Digital Credentials

3. AI Strategy for Executives

4. Career Development and Career Transitions

5. Credentialing Standards and Competency Frameworks (CTDL)

6. Board Governance

7. Future of Work and Workforce Transformation

Education and Credentials

1. MBAe™, MBA Standards Board™ (2025)

2. Bachelor of Science (BS), Business Finance, Brigham Young University, Marriott School of Business

3. Executive MBA Certification, with AI and Organizational Change and Agile Leadership specializations, SectionAI (2023 to 2025), including the AI for Business Mini-MBA, the Marketing Mini-MBA, 8 certifications and 50+ courses

4. 2026 State of Credentialing, Accredible (2026)

5. NASD Series 24 General Securities Principal License, FINRA (1987 to 1998)

Verify My Credentials

MBAe™, https://credsverse.com/credentials/5c9a43a5-c17e-4698-aae0-ab83ccf5edf3

2026 State of Credentialing, https://accredible.credential.net/d2c2b72e-90ba-4a4f-9554-0f44846aab6a

Publications and Thought Leadership

GET FOUND. GET HIRED. How to Turn Verified Credentials Into Job Offers. A practical guide to competing on proof in an AI-screened job market. Advance Reading Copy, 2026.

Credential Edge: AI Advantage, weekly newsletter on credentialing, AI hiring and the future of work. https://www.linkedin.com/newsletters/credential-edge-ai-advantage-7379626048780472320/

Employers Are Looking, But Not Finding. Why 91 percent of employers want verified credentials but few see them. https://www.linkedin.com/pulse/employers-looking-finding-cheryl-nunn-mbae--rdxtc/

The College System Is Fragmenting. What is filling the gaps as the traditional university model breaks apart. https://www.linkedin.com/pulse/college-system-fragmenting-read-learn-what-filling-gaps-nunn-mbae--h8ivc/

The Governance Gap Has a Classroom Problem. Why we teach AI tools but not AI judgment. https://www.linkedin.com/pulse/governance-gap-has-classroom-problem-cheryl-nunn-mbae--18rmc/

Professional Philosophy

Experience Counts and Credentials Matter.

Real experience deserves real recognition. A credential should prove what someone can do today, earned through demonstrated competency, kept current through continuing education and backed by an ethics standard.

Ask Me About

1. How to make your credentials visible to AI screening tools and recruiters

2. Turning work experience into a verified credential

3. Building a career after a layoff or career change

Connect

Website, https://applymba.org

MBA Leaders Forum, https://leadersforum.co

LinkedIn, https://www.linkedin.com/in/cherylnunn

Substack, https://www.thecredentialedge.com

Professional Interests

Future of Work | AI in Hiring | Higher Education Reform | Book Publishing | Workforce Development

What recruiters, HR teams, and hiring software can actually confirm, and what a profile can’t tell you at a glance.

The Button Everyone Trusts

Open any LinkedIn profile and scroll to Licenses and Certifications. You’ll see a logo, a credential name, dates and a button that says “Show credential.” It looks official. It looks like proof.

It isn’t always.

That button is only a link. When someone adds a credential, they type in the name, the issuer and a web address. LinkedIn turns that address into the button. It doesn’t check where the link goes. The logo appears because LinkedIn matched the issuer’s name to that organization’s company page, not because the organization confirmed anything.

I found this on my own profile. A securities principal license from early in my career shows the FINRA logo, a credential ID, and a “Show credential” button. The button opens a general FINRA page about its registration system. My name isn’t on it. The license isn’t on it. To anyone scanning my profile, it looks exactly like my MBAe™, which opens a page confirming my name, my credential, and its issue and expiration date and current status. It also verifies the skills I learned for that credential.

Two buttons, one design, two very different levels of proof.

What a Human Sees

A recruiter looking at a profile can’t tell the difference without clicking. And most don’t click. They’re reviewing hundreds of candidates, and the button itself sends the message “this has been checked.”

That’s the problem. When every credential gets the same button, the button stops meaning anything. It dilutes the value of the credentials that really are verifiable.

Experienced recruiters and HR teams don’t trust the button. They trust the issuer.

What Recruiters and Hiring Software Look For

The Credential Engine Registry publishes credentials in a common language called CTDL, short for Credential Transparency Description Language. It describes what a credential is, who issues it, what it requires and what skills it proves, in a format software can read. Anyone can search the registry for free at Credential Finder.

The gap is wide. Credential Engine counts more than 1.85 million credentials in the United States, and only about 7 percent are published in the registry. The rest exist as text on a website, a certificate on a wall or a line on a profile.

Hiring software works the same way. Software can’t judge a link. It can read structured data.

A credential registered in CTDL, or issued as a digitally signed badge under the Open Badges 3.0 standard, gives a system something it can check automatically. A typed-in name and a web address give it nothing but words.

That isn’t just a theory. HR leaders are asking for it. Accredible’s 2026 research found that 60 percent of employers want credentials connected to hiring platforms, yet only 34 percent of issuers provide structured data. Meanwhile, Equifax reports that 71 percent of HR professionals have run into misleading candidate information, and only 20 percent feel confident they can catch it.

How to Tell the Difference in 30 Seconds

1. Click the button. Does the page show the person’s name? If not, it isn’t verification.

2. Check the web address. Does it belong to the issuer or a known credential platform such as Credsverse, Credly or Accredible?

3. Look for a status. A real verification page shows whether the credential is active, expired or revoked.

4. Search the issuer on CredentialFinder.org. Registered credentials show exactly what was required to earn them.

What You Should Do With Your Own Profile

Click every “Show credential” button on your own profile. If one opens a general page, remove the link and keep the credential as plain text. An honest listing builds more trust than a button that can’t deliver.

Then look for the credentials that can deliver. Verified, registered credentials are the ones recruiters, HR teams and their software can confirm in seconds. That’s the difference between being listed and being found.

About Credential Watch

Credential Watch tracks the standards, data and tools shaping how credentials are verified in an AI-driven hiring market. Every MBA Standards Board™ credential is registered in the Credential Engine Registry.

Suggested tags Credential Watch, Verified Credentials, LinkedIn, Skills-Based Hiring, AI Hiring

Sources:

• How to Add a Certificate to LinkedIn: Credential ID and URL (TRUE)

• Credential fraud in 2026 (Sertifier, citing Equifax)

• Accredible Open Badges 3.0 and W3C Verifiable Credentials

• LinkedIn Certifications guide 2026 (ConnectSafely)

Dr. Beth Fries-Packard is a learning and development professional, educator, and founder of BFP Studio, where she focuses on workplace readiness, competency development, and helping individuals connect their professional growth to organizational success.

With experience spanning corporate learning and development, workforce training, leadership development, and higher education, Beth brings a practical, people-centered perspective to building organizational capability.

She holds a Doctor of Business Administration in Organizational Leadership and Development, with doctoral research examining core competency development through the perspectives of U.S. staffing industry leaders. Her work explores the connection between the skills organizations need, the capabilities individuals develop, and the opportunities that allow people to demonstrate their potential.

Through BFP Studio, Beth created the Know. Do. Become. framework, emphasizing the progression from understanding and knowledge acquisition to practical application and continued professional growth.

She is also the author of Know the Business: A Guided Workbook for Seeing Beyond Your Department, a resource designed to help individuals better understand how organizations operate, connect their contributions to broader business objectives, and take ownership of their professional development.

Beth advocates for learning that extends beyond completing training to building meaningful, demonstrable workplace capabilities.

Areas of Expertise

• Workforce Readiness and Competency Development

• Learning and Development Strategy

• Leadership and Organizational Development

• Employee Development and Career Readiness

• Skills-Based Learning and Capability Validation

• Organizational Learning and Business Acumen

• Training Design and Facilitation

Education and Credentials

• Doctor of Business Administration (DBA), Organizational Leadership and Development

• Certified MBA™ (CMBA™)

• Master of Business Administration (MBA), Project Management

• Bachelor of Arts (BA), Business Management and Communication

• Lean Six Sigma Green Belt

• Everything DiSC® Certifications

Publications and Thought Leadership

Know the Business: A Guided Workbook for Seeing Beyond Your Department
Understand the Organization. Connect Your Contribution. Grow Your Impact.

As Your Organization Plans for 2027, Are You Planning for You?
An article addressing the importance of intentional individual professional development alongside organizational planning.

Professional Philosophy

Know. Do. Become.

Knowledge into action. Action into growth.

Beth believes meaningful professional development requires more than acquiring information. Individuals need opportunities to apply what they learn, demonstrate their capabilities, and recognize how their contributions connect to organizational success.

Connect with Beth

BFP Studio: https://www.bfp-studio.com/
LinkedIn: https://www.linkedin.com/in/bethfries/

Publications:

Know the Business Workbook
We Train People to Do the Job. Who Teaches Them How to Work? – Credential Edge: AI Advantage article

Professional Interests:

Skills-Based Hiring | Career Development | Future of Work | Workforce Readiness | Leadership Development

Areas of Expertise

* Workforce Readiness and Competency Development
* Learning and Development Strategy
* Leadership and Organizational Development
* Employee Development and Career Readiness
* Skills-Based Learning and Capability Validation
* Organizational Learning and Business Acumen
* Training Design and Facilitation

Education and Credentials

Doctor of Business Administration (DBA), Organizational Leadership and Development
Board Certified Director (BCD™) — Click to Verify Credential Here
Chartered MBA™ (CMBA™) — Click to Verify Credential Here
Certified MBA™ — Click to Verify Credential Here
Master of Business Administration (MBA), Project Management
Bachelor of Arts (BA), Business Management, and Communication
Lean Six Sigma Green Belt
Everything DiSC® Certifications

Publications and Thought Leadership

-Know the Business: A Guided Workbook for Seeing Beyond Your Department
-Understand the Organization. Connect Your Contribution. Grow Your Impact.
-As Your Organization Plans for 2027, Are You Planning for You?
-An article addressing the importance of intentional individual professional development alongside organizational planning.

Professional Philosophy

Know. Do. Become.

Knowledge into action. Action into growth.

Beth believes meaningful professional development requires more than acquiring information. Individuals need opportunities to apply what they learn, demonstrate their capabilities, and recognize how their contributions connect to organizational success.

Connect with Beth

BFP Studio: https://www.bfp-studio.com/
LinkedIn: https://www.linkedin.com/in/bethfries/

Publications:

Know the Business Workbook

We Train People to Do the Job. Who Teaches Them How to Work? (Credential Edge: AI Advantage article)

Professional Interests:

Skills-Based Hiring | Career Development | Future of Work | Workforce Readiness | Leadership Development

Editor Note

Every professional now uses AI. Very few have been taught when to trust it.

This week’s guest edition comes from Dr. David Z. Jacome, an educator, TEDx speaker and valued member of the MBA Leaders Forum board who consults with accounting firms on AI governance. Building on Priya Udeshi’s recent edition, David takes the governance conversation into the classroom, where tomorrow’s leaders are learning to use AI long before anyone teaches them to judge it. Whether you lead a team, hire graduates, or are building your own skills for the next stage of your career, his argument is one you’ll want to read closely. Thank you, David, for this thoughtful and timely contribution.

Executive Summary

Priya Udeshi made the case in a recent edition of this newsletter that the future of work is not about which jobs AI will replace. It is about whether organizations can govern what comes next. She is right, and her argument deserves to be extended into the two domains where I spend my professional life: the accounting profession and the business school classroom.

The accounting and finance function has adopted AI faster than almost any corner of the enterprise. The Consero Global 2026 CFO Report found that 97 percent of finance departments have now adopted AI in some form, up from 76 percent just a year earlier [1]. Yet Thomson Reuters’ 2026 survey of more than 1,500 professionals found that only 18 percent of organizations track AI return on investment at all [2]. Adoption without measurement. Experimentation without evaluation. That is the same governance gap Priya described, playing out in the profession whose entire identity is built on measurement, controls, and assurance.

The deeper problem is upstream. The professionals who will be expected to close this gap are sitting in business school classrooms right now, and we are largely not teaching them how. Surveys consistently show that between 80 and 92 percent of students already use generative AI in their studies [3, 4]. Meanwhile, an AACSB survey of deans and faculty found that nearly 45 percent of business schools lack the coordinated governance needed to translate their own AI policies into practice [5]. We are producing graduates who are fluent in using AI tools and unprepared to govern them. That is not a skills gap. It is a judgment gap, and it is the one that will define the next decade of the profession.

The Profession That Measures Everything Is Not Measuring This

Accounting is the discipline of verification. We built our professional identity on internal controls, audit trails, materiality thresholds, and the principle that a number you cannot support is a number you cannot report. So it should trouble all of us that the profession’s own AI adoption looks, from a governance standpoint, remarkably undisciplined.

Consider the pattern in the current data:

Adoption is nearly universal, and it happened fast. Beyond the 97 percent finance department figure, Thomson Reuters found that organizational use of generative AI among tax and accounting professionals jumped from 22 percent to 40 percent in a single year, and that 86 percent of tax professionals using generative AI do so at least weekly [2, 6]. Intuit’s 2026 survey of 725 U.S. accounting professionals found 88 percent using AI for client services and 86 percent for firm operations [7].

Measurement has not kept pace. Only 18 percent of organizations in the Thomson Reuters survey track AI return on investment [2]. A CFO.com survey of 321 U.S. finance and accounting decision-makers found that only 28 percent of finance teams using AI are seeing measurable financial impact [1]. The firms that do see returns look structurally different: Grant Thornton’s 2026 CFO research found that fully integrated firms are four times more likely to report revenue growth than pilot-stage organizations [1]. Integration, not experimentation, is what pays. And integration is a governance outcome.

Risk controls lag adoption. KPMG research found that 46 percent of U.S. accounting firms have inadvertently input confidential client information into public AI services [6]. Read that again. Nearly half of the firms in a profession bound by confidentiality obligations have already had a data governance failure with AI, most without a policy framework in place to prevent the next one. Thomson Reuters also found that 61 percent of firms have stalled or halted AI projects due to skill shortages [6].

None of this is a technology failure. The tools work. What is missing is exactly what Priya identified at the enterprise level: the decision infrastructure. Which AI use cases align with the firm’s strategy and risk appetite? What does value mean for each one? Who decides what client data can enter which system? What triggers a shutdown? How is return measured, and who is accountable for it? These are portfolio governance questions, and in most firms I work with, nobody owns them.

I see this firsthand in my consulting practice with accounting firms. The typical engagement does not begin with “help us choose an AI tool.” It begins with a firm that has already accumulated a dozen tools, three unofficial ChatGPT workflows the partners do not know about, and no shared answer to the question of what any of it is for. The first deliverable is never technology. It is an inventory, a data classification policy, an evaluation framework, and a decision-rights map. Governance first. Tools second. The firms that reverse that order become part of the statistics above.

The Second Governance Gap: The One in the Classroom

Here is where I want to push the conversation somewhere Priya’s piece pointed but did not go. Every governance framework, every portfolio process, every measurement system she called for has to be designed, operated, and defended by people. Where do those people learn to do it?

Increasingly, the honest answer is: nowhere.

The student side of the equation has moved with breathtaking speed. The UK’s Higher Education Policy Institute found that the share of students using generative AI tools jumped from 66 percent to 92 percent in a single year [3]. Stanford’s 2026 AI Index estimates that four in five university students now use generative AI, and Chegg’s survey of nearly 12,000 undergraduates across 15 countries found that 80 percent use it to support their studies, with a majority of U.S. users prompting at least once a day [4]. Our students are not waiting for permission or curriculum. They are already fluent operators.

The institutional side has moved far more slowly, and unevenly. To be fair, there is real progress. The AACSB, GMAC, and Graduate Business Curriculum Roundtable framework for AI in business education has grown from 26 participating schools in July 2025 to 84 in July 2026, and it documents a genuine shift from experimentation toward institution-wide integration [8]. AACSB’s proposed 2026 Global Standards elevate digital agility and AI literacy toward accreditation expectations rather than optional innovation [9]. These are the right moves.

But look closely at what the same body’s research reveals: nearly 45 percent of business schools lack the coordinated governance to translate their own generative AI policies into practice [5]. Business schools, in other words, have the identical failure mode as the organizations they study. Scattered faculty innovation, inconsistent policy, no shared definition of what AI competence means, and limited mechanisms for scaling what works and retiring what does not. The AACSB’s own contributors have proposed six-pillar governance frameworks for schools precisely because the ad hoc approach is producing innovator fatigue and inconsistent institutional impact [5].

And even where AI has entered the curriculum, notice what we are teaching. The dominant model is tool fluency: prompt this system, automate that workflow, build this dashboard. Tool fluency matters, and I teach it. But tool fluency is the easy half. The World Economic Forum’s Future of Jobs Report identified skills gaps as the single biggest barrier to business transformation, cited by 63 percent of employers, and estimates that 39 percent of workers’ existing skills will be transformed or become outdated by 2030 [10]. The instinctive institutional response has been to teach more tools faster. But the skills that will not become outdated are the governance skills: knowing how to evaluate an AI investment against strategic priorities, how to define value before deployment rather than after, how to design controls around a probabilistic system, and how to decide when to scale and when to stop.

Nobody’s prompt library will matter in five years. Their judgment will.

Why Accounting Education Carries a Special Burden

This argument applies across business education, but it lands hardest in accounting, for three reasons.

First, the pipeline math leaves no slack. More than 300,000 accountants and auditors left the profession or the labor force between 2019 and 2022 [11]. The AICPA’s 2025 Trends report shows schools awarded 55,152 accounting bachelor’s and master’s degrees in the 2023 to 2024 academic year, down 6.6 percent from the prior year, extending a decade-long decline even as the Bureau of Labor Statistics projects over one hundred thousand accounting and auditing openings each year [12, 13]. There are encouraging signals: accounting enrollment rose 8.9 percent in spring 2026, the third consecutive year of growth [14]. But every graduate we produce now has to carry more responsibility, earlier, than any prior generation. A shrinking profession cannot afford graduates who need five years of on-the-job seasoning before they can be trusted with judgment. The judgment training has to start in the classroom.

Second, AI is consuming exactly the work we used to train juniors on. The traditional accounting apprenticeship ran on repetition: ticking and tying, reconciliations, workpaper preparation, first-pass research. That repetition was inefficient, but it was also where professional skepticism was quietly built. As AI absorbs that layer, the profession loses its default training ground for judgment at the very moment it needs judgment most. Some observers now argue AI is absorbing entry-level accounting work faster than the pipeline initiatives can replenish it [15]. If the bottom rung of the ladder is disappearing, education has to build the rung somewhere else, deliberately.

Third, accountants are the natural owners of AI governance, if we prepare them for it. Strip away the technology and look at what enterprise AI governance actually requires: control design, evidence evaluation, risk assessment, independence of judgment, documentation discipline, and the ability to tell leadership an uncomfortable truth with support for every number. That is not a new skill set. That is the accounting skill set, pointed at a new object. The evolved CPA exam already gestures in this direction with its Information Systems and Controls discipline [12]. The firms that see the strongest AI returns are the ones that pair tools with explicit data-handling policies and mandatory training before go-live [6]. Someone has to design those policies and controls. There is no professional community better positioned than ours, and no better recruiting pitch for the pipeline than this: the profession that governs the numbers is becoming the profession that governs the machines producing them.

What Teaching AI Judgment Actually Looks Like

It is fair to ask what I would put in the curriculum instead of, or alongside, tool training. Here is what I have been building into my own courses and what I believe the next revision of accounting and business curricula requires.

Evaluation before automation. Before students automate a workflow, require them to write the business case a governance committee would demand: the value hypothesis, the measurement plan, the risk assessment, the kill criteria. In my analytics courses, the deliverable is not the working workflow. It is the workflow plus the memo that justifies its existence and defines how we will know if it failed. Students resist this at first. It feels like paperwork. Then they interview for jobs and discover it is the scarcest skill in the room.

Verification as a first-class competency. Every AI output in my classroom arrives with a standing question attached: how would you audit this? Students learn to trace an AI-generated analysis back to source data, to identify what the system cannot know, and to document the review the way they would document audit evidence. Professional skepticism does not transfer to AI automatically. It has to be practiced against AI specifically, because probabilistic systems fail differently than careless humans do. They fail confidently.

Data governance as ethics in practice. The 46 percent confidentiality failure statistic [6] belongs in every accounting ethics course in the country, not as a scare slide but as a case study. What policy would have prevented it? What classification scheme? What training? Students should graduate having actually drafted an AI acceptable-use policy for a hypothetical firm, argued about its edge cases, and defended it.

Portfolio thinking, not project thinking. Business curricula still mostly teach technology decisions as single-project capital budgeting exercises. The reality Priya described, and the one my consulting clients live in, is a portfolio of dozens of concurrent AI initiatives competing for constrained capacity. Students need practice in comparative evaluation: given ten pilots and funding for three, which do you scale, which do you stop, and how do you defend the decision? That exercise teaches more about the future of their work than any individual tool ever will.

Cross-functional translation. The governance gap persists partly because technologists and business leaders lack a shared language for value. Accountants can be that translation layer, but only if we train them to present control and measurement concepts to non-accountants. Communication is not a soft-skills garnish on this curriculum. It is the delivery mechanism for everything else.

None of this requires abandoning tool instruction. It requires reframing it. The tool is the object of study. Governance is the discipline being taught.

The Institutional Mirror

One more uncomfortable observation, offered in the spirit of the honest debate this board has become known for. Business schools cannot credibly teach AI governance while practicing AI anarchy.

When nearly half of schools lack coordinated governance for their own AI adoption [5], the hidden curriculum undermines the formal one. Students notice when one professor bans AI, the next requires it, and the institution’s policy is a paragraph nobody can locate. What they learn from that experience is that governance is theater. The schools documented in the AACSB framework as exemplars share a common trait: they treated their own institution as the first case study, pairing platforms with governance frameworks, policies, and oversight before scaling into the curriculum [8]. That sequencing is the lesson. The 4P framing some AACSB contributors propose, covering people, policy, pedagogy, and platform, is a reasonable starting scaffold for any school still improvising [16].

This is also, frankly, a leadership opportunity for those of us in accounting departments. We teach controls for a living. We should be volunteering to help design our institutions’ AI governance, not waiting for a task force memo. I have found that the credibility this builds flows directly back into the classroom and into relationships with the firms that hire our students.

Where This Leaves Us

Priya ended her piece by reframing the future of work question: not which jobs AI will take, but who will build the organizational systems that determine whether AI creates value or noise. I want to extend her reframe one step further upstream.

Who will teach the builders?

The governance infrastructure the next decade requires will be designed by people who today are undergraduates using ChatGPT to summarize their textbook. Whether they become the professionals who close the governance gap or the ones who widen it depends substantially on what happens in our classrooms over the next few years. The data says the profession is adopting faster than it is governing, the pipeline is too thin to waste a single graduate on obsolete training, and the institutions doing the training are struggling with the same governance deficit they need to teach against.

That is not a counsel of despair. It is a to-do list, and an unusually clear one. Teach evaluation before automation. Teach verification as rigorously as we teach preparation. Practice institutional governance so students see it modeled, not just described. And tell the next generation the truth about their profession: the ledger is being automated, but judgment is not, and judgment is now the product.

The future of work in accounting is not fewer accountants. It is accountants doing the one thing machines cannot: deciding what should be trusted, and taking responsibility for the answer.

What This Means for Your Career

You don’t have to be an accountant or a professor for David’s argument to apply to you. In nearly every field, knowing how to use AI tools is quickly becoming the baseline. What employers increasingly need, and rarely find, is someone who can judge what those tools produce.

That gives every professional three practical steps. First, check what AI gives you before you rely on it, and be able to explain how you checked. Second, when you propose using AI for a task, write down what success looks like and when you would stop. Third, keep a record of decisions you made well with AI’s help, because that record is proof of judgment, and judgment is what hiring managers are searching for.

At the MBA Standards Board™, this is why our Competency Conversation™ tests judgment, not memory. A credential should show that you can think through a real decision, not just that you completed a course.

Where in your work are you using AI without a clear way to check it? Share it in the comments. David and I will be reading.

Frequently Asked Questions

If AI adoption in accounting is already near universal, has the governance window closed?

No, and the data suggests the opposite. Adoption being universal is precisely why governance is now the differentiator. When 97 percent of finance departments have adopted AI [1] but only 18 percent of organizations measure its return [2], the competitive separation is no longer about access to tools. Grant Thornton’s finding that fully integrated firms are four times more likely to report revenue growth than pilot-stage peers [1] shows that the returns accrue to organizations that built the surrounding discipline. The window for adopting first has closed. The window for governing best is wide open.

Is this an argument against teaching AI tools in accounting programs?

Not at all. Tool fluency is necessary and students expect it; surveys show most of them arrive already using AI daily [3, 4]. The argument is about what surrounds the tool instruction. A student who can automate a reconciliation but cannot write the evaluation criteria, the data-handling policy, or the review procedure for that automation is half-trained, and it is the wrong half to stop at. The tools will be replaced repeatedly during their careers. The evaluation discipline will not.

Why should accounting faculty and firms treat this as a shared problem rather than blaming each other?

Because the failure modes are interlocking. Firms report that skill shortages have stalled 61 percent of AI projects [6], while schools report that governance deficits prevent their AI policies from reaching practice [5]. Firms need graduates trained in judgment; schools need firms to articulate what judgment looks like in current practice and to provide the case material. The most productive model I have seen is co-designed: firms bring real, anonymized AI governance dilemmas into the classroom, and faculty bring evaluation frameworks and assessment rigor. Both sides get a stronger pipeline, and the AICPA enrollment recovery now underway [14] gives us more students to build it with.

Does the shrinking entry-level layer mean the traditional accounting career path is finished?

The path is being compressed, not eliminated. The apprenticeship layer of repetitive work is shrinking as AI absorbs it, which means judgment formation has to be front-loaded into education and early career design rather than left to accumulate through repetition [15]. That is a solvable design problem. Professions from medicine to aviation deliberately train judgment through simulation and structured cases rather than waiting for volume experience. Accounting education can do the same, and the programs that figure it out first will own the placement pipeline.

From the Boardroom, Questions From This Discussion

Questions and answers drawn from the board and reader discussion of this edition, paraphrased and credited to the people who raised them.

What Is the Difference Between AI Tool Fluency and AI Judgment?

AI tool fluency is knowing how to operate an AI system, such as writing prompts or automating a workflow. AI judgment is knowing whether the output can be trusted, what it left out and when to stop using it. Dr. David Z. Jacome, who teaches accounting and business analytics, argues that the tools will be replaced several times over a career while judgment lasts. Jay Manouchehri, a CEO and board member, summed it up in the discussion. “Prompting is a commodity; discernment is the premium skill.”

What Evidence Convinces an Employer That a Candidate Can Be Trusted to Review AI Output?

The strongest evidence is a specific example with a paper trail. Dr. Jacome says the most convincing candidate can describe a time AI gave a wrong answer, how they caught it, what they checked and what they changed, and can then show the review notes. That one example proves three things. The candidate defined what a correct answer looked like before starting, tested the output with skepticism and documented the review the way an auditor would.

Can Candidates Fake a Story About Catching an AI Error?

Yes, once that story becomes the expected interview answer. James Lancaster, CMBA™, a founder and investor with 52 angel investments, warned that candidates will arrive with rehearsed stories and coaching services will sell the template. His alternative is to hand the candidate a one-page analysis with a planted flaw and ask what they would check first and why, because the order they check things in reveals more than whether they find the flaw. Dr. Jacome called this Goodhart’s law applied to hiring. When a measure becomes the target, it stops being a reliable measure.

What Interview Question Best Reveals Real Judgment?

Ask the candidate to describe a time they were wrong and caught it too late. James Lancaster, CMBA™, explained why it works. Anyone can narrate a catch, but very few people can walk through a miss honestly, and that kind of answer is close to impossible to rehearse into something flattering.

How Can Business Schools Evaluate a Student’s Judgment Before Graduation?

Grade the decision, not just the deliverable. In reply to a question from JoAnn Laing, CMBA™, a board director and digital modernization leader, Dr. Jacome described three methods. Give students AI output with a hidden error or missing risk and score whether they catch it. Ask for a decision memo explaining what they accepted, what they rejected and why. And require a short oral defense to confirm the reasoning is truly theirs, an approach he is using this semester in his econometrics course.

How Can a Professor Tell Whether a Student Understands AI-Assisted Work?

Ask the student to explain it out loud. Randy Priem, a finance professor at the University of Antwerp and a senior policy advisor at IOSCO, recommends following written work with a presentation or oral update, because a student who truly wrote something can explain it easily. Dr. Jacome put the principle in one line. “If you can’t explain it, you don’t own it.” Priem also noted that the business schools where he teaches give students a dedicated session on AI governance, covering plagiarism, critical review of output and what confidential information must never be shared with AI.

Why Is AI a Threat to How Junior Professionals Develop Judgment?

AI is absorbing the repetitive entry-level work where judgment used to be built. In accounting, that work included ticking and tying, reconciliations and first-pass research, tasks that quietly trained professional skepticism. Chandni Prasad, CMBA™, an AI product manager and startup mentor, warned that without a new entry point, organizations will face a shortage of capable mid-level leaders and, later, of senior leaders ready to be accountable for AI-led decisions. Her prescription is to front-load training in risk, ethics and validation instead of waiting for experience to accumulate.

If AI Removes the Apprenticeship, How Will Organizations Develop Experts?

Organizations will have to rebuild the apprenticeship on purpose. Josef Stetter, MBAe™, Certified MBA™, asked the question directly. If we automate the work that teaches judgment, who will govern AI when today’s experts retire? Dr. Jacome offered three answers. Pair juniors with seniors to review AI output together, capture expert judgment now while experienced people are still in the room, and reward the person who catches an error, not just the people who adopt the tools. Stetter’s advice for leaders was blunt. “Stop measuring who can use AI. Start measuring who can challenge it.”

How Many Accounting Firms Have Put Confidential Client Data Into Public AI Tools?

Research from KPMG found that 46 percent of U.S. accounting firms have inadvertently entered confidential client information into public AI services. Dominique Colvard, MPAS, MBAe™, a healthcare leader, compared it to entering a patient’s protected health information into a public chatbot, the exact risk HIPAA exists to prevent. She also drew a useful distinction. Many clinics now use AI for transcription, but through internal tools monitored by their own IT and cybersecurity teams, not public websites.

What Should You Ask Before Putting Work Data Into an AI Tool?

Ask the same questions an auditor would. Dr. Jacome teaches students to ask where the data goes, who can access it and whether it will be used to train the AI model before they use any tool. In his words, professional skepticism shouldn’t stop at the financial statements. It should extend to the tools used to analyze them.

Why Is AI’s Confidence a Risk for Decision-Makers?

AI often presents uncertain answers with full confidence, and people tend to trust confidence. Katherine Mathews, CMBA™, a medical director, recalled the hardest exam of her medical training, an oral examination by three Harvard faculty members. What they were really testing was whether students could admit what they didn’t know, recognize when there might be no right answer and tolerate uncertainty. Her conclusion was memorable. “AI’s certainty can be a fool’s gold.”

Does Relying on AI Keep People From Mastering Their Field?

It can, because knowledge in any field works like a language that has to be practiced. Katherine Mathews, CMBA™, compared relying on AI to listening to language tapes instead of speaking with a native speaker. A student who leans on AI may get the answers without ever becoming fluent in the discipline itself.

How Can Workplace Training Build Judgment and Not Just AI Skills?

Training should move people from knowing how AI works to being accountable for what they do with it. Beth Fries-Packard, DBA, CMBA™, a workforce readiness strategist, frames it as Know, Do, Become. Know AI’s capabilities, limits and biases. Do the work of verifying, questioning and challenging its output. Become a professional who uses AI with confidence while owning every decision. Dr. Jacome added that the most costly errors are not always wrong numbers. Sometimes they are a missing point of view, such as an overlooked stakeholder.

Why Does AI Judgment Matter in Sales and Commercial Leadership?

Faster analysis only creates value when someone can tell whether it fits the real situation. James Johnson II, CMBA™, a director of national accounts, said the differentiator will be less about who can use AI tools and more about who can question the output, recognize what may be missing and turn it into sound business decisions. Dr. Jacome agreed, noting that AI can summarize the data, but it doesn’t sit across the table from the customer.

What Should a Team Define Before Automating Any Workflow With AI?

Define the value hypothesis, the measurement plan and the kill criteria before anything is automated. Priya Udeshi, CMBA™, PMP, Chief of Staff to the CIO and head of the global IT program office at MongoDB, said she would like to see those three items on every project intake form, because they make the later decision to scale or stop far easier. She also confirmed that Dr. Jacome’s classroom exercise, choosing three of ten AI pilots to fund, closely mirrors how real portfolio decisions are made.

Is the Classroom the Only Place Where Judgment Is Built?

No. Sarah Ellison, Ed.D., an associate vice president of student affairs, called the ability to audit, evaluate risk and exercise professional judgment “the real enduring credential,” since the tools change every six months. Dr. Jacome added that judgment also grows through advising, leadership roles and every moment students must make a real decision and stand behind it. Sam Bodunrin, Ph.D., CMBA™, a professor and board director, agreed that the classroom is where judgment must be practiced, not just described.

How Can a Career Changer Prove Judgment When Tools Change So Quickly?

Focus on the skills that carry across every field and every tool. As Cheryl Nunn, MBAe™, noted in the discussion, AI tools change every quarter, but the ability to check an output, define success before you start and know when to stop can be built and proven anywhere. A rehearsed claim is easy to make. Showing that judgment live, on a problem with an embedded flaw, is far more convincing to an employer.

About the Author

Dr. David Z. Jacome, MBA, CMBA™, M.S., is an educator who teaches accounting and business analytics, a consultant to accounting firms on AI governance and adoption, a TEDx speaker and a member of the MBA Leaders Forum board. His work focuses on helping firms and business schools build the governance, measurement and judgment that responsible AI adoption requires.

References

[1] Consero Global, 2026 CFO Report; Grant Thornton 2026 CFO Survey; CFO.com survey of 321 U.S. finance and accounting decision-makers, 2025. Adoption, integration, and measurable-impact figures as compiled in “AI in Accounting and Finance Statistics 2026,” May 2026.

[2] Thomson Reuters, 2026 survey of more than 1,500 tax, accounting, and professional services respondents: generative AI organizational use rising from 22 percent to 40 percent year over year; agentic AI at 15 percent; 18 percent of organizations tracking AI ROI.

[3] Higher Education Policy Institute (HEPI) and Kortext, Student Generative AI Survey 2025 and 2026: student use of generative AI tools rising from 66 percent to 92 percent year over year among UK undergraduates.

[4] Stanford Institute for Human-Centered AI, 2026 AI Index Report; Chegg Global Student Survey of 11,706 undergraduates across 15 countries; Gallup surveys of U.S. college students, as summarized in “Student Use of AI in Higher Education,” USC Libraries, July 2026.

[5] Bhattacharyya, S., and Mehta, K., “Leading the AI Transition,” AACSB Insights, March 2026, citing a 2025 AACSB survey of deans and faculty finding nearly 45 percent of business schools lack coordinated governance to translate generative AI policies into practice.

[6] Thomson Reuters, “2026 AI in Professional Services Report” (weekly-use and stalled-project figures); KPMG, “AI Adoption Across Finance Functions,” 2025 (46 percent of U.S. accounting firms inadvertently inputting confidential client information into public AI services), as compiled in “AI Adoption in Accounting Firms: 2026 State Report.”

[7] Intuit / Firm of the Future, 2026 Accountant Technology Survey of 725 U.S. accounting and bookkeeping professionals, June 2026.

[8] Means, T., Inspire Higher Ed, in partnership with AACSB International, the Graduate Business Curriculum Roundtable, and GMAC, “A Framework for Artificial Intelligence in Business Education: Exemplars and Critical Themes for Successful Integration,” July 2026 update (84 participating institutions, up from 26 in July 2025).

[9] AACSB International, exposure drafts of the Global Standards for Business Education and 2026 Accounting Accreditation Standards, January 2026, including proposed digital agility expectations.

[10] World Economic Forum, Future of Jobs Report 2025: skills gaps cited by 63 percent of employers as the top barrier to transformation; 39 percent of workers’ core skills expected to change or become outdated by 2030.

[11] Ellis, L., “Why So Many Accountants Are Quitting,” The Wall Street Journal, December 2022, reporting more than 300,000 accountants and auditors leaving the field between 2019 and 2022, as discussed in The CPA Journal, February 2026.

[12] AICPA, “2025 Trends: A Report on Accounting Education, the CPA Exam, and Public Accounting Firms’ Hiring of Recent Graduates,” October 2025: 55,152 accounting degrees awarded in 2023 to 2024, down 6.6 percent; evolved CPA exam discipline structure.

[13] U.S. Bureau of Labor Statistics occupational projections for accountants and auditors, as cited in AICPA/NASBA pipeline analyses, 2025 to 2026.

[14] AICPA enrollment data reported in CPA Practice Advisor, August 2026: accounting enrollment at four-year institutions up 8.9 percent in spring 2026, the third consecutive year of growth.

[15] CFO.com analysis of the AICPA 2025 Trends report, October 2025, on AI absorption of entry-level accounting work relative to pipeline recovery.

[16] Zhou, X., and Ladley, D., “Integrating AI Strategically in Challenging Times,” AACSB Insights, March 2026, proposing the 4P framework of People, Policy, Pedagogy, and Platform.

The best credentials for career changers in 2027 are the ones that build on experience you already have, appear in the job postings you’re targeting, and can be verified online by an employer. Many take three to six months, cost less than $1,500 and require no college degree. Here are ten strong options across fields, plus a simple way to choose.

The Best Credentials by Career Direction

Project Management
CAPM® (Certified Associate in Project Management) from the Project Management Institute. It costs $225 for members or $300 for nonmembers, plus 23 hours of training, and takes one to three months. It’s a strong first step for anyone who already coordinates projects, events or teams without the title.

Business Analysis
ECBA™ (Entry Certificate in Business Analysis) from the International Institute of Business Analysis. It costs about $395, including first-year membership, and takes one to three months. It’s a good fit if you already translate between people and data in your current job.

Agile and Team Leadership
CSM® (Certified ScrumMaster) from Scrum Alliance. It usually costs $500 to $1,300, set by trainers, and takes a two-day course plus an online exam. It’s the fastest credential on this list and is widely requested in tech, finance, and operations.

Human Resources
aPHR® (Associate Professional in Human Resources) from HRCI. It costs $400, takes two to three months and requires no HR experience, so it’s ideal for someone moving into HR from another field.
SHRM-CP (SHRM Certified Professional) from the Society for Human Resource Management. It costs $350 to $499 and takes about three months. It’s one of the most recognized names in HR job postings.

Information Technology
CompTIA A+, about $500 for two exams, three to six months. It’s the classic entry point into IT support.
CompTIA Security+, $439, two to four months. It’s a gateway to cybersecurity roles and is often required for government and defense contractor jobs.
CCNA (Cisco Certified Network Associate), about $300, three to six months. It’s a respected foundation for networking careers.

Healthcare
CNA (Certified Nursing Assistant) through state-approved training programs. It takes four to 12 weeks, costs vary by state, and nursing homes that hire you can’t charge you for the training. It’s one of the quickest routes into healthcare.

Business Specializations for Experienced Professionals
Full disclosure, this is our own category. The MBA Standards Board™ offers 21 specialization credentials in fields such as AI strategy, cybersecurity leadership, digital health, sustainability, and supply chain. Each costs under $1,000, plus courses estimated under $300, takes three to six months, and is registered in the Credential Engine Registry. They’re designed for professionals who want to turn years of experience into a verified specialization without returning to school.

How to Choose the Right One for You
Before you spend a dollar, ask four questions.

1. Does it build on what I already know? The strongest career changes add one missing piece to experience you already have. A hospital project coordinator is halfway to a health systems credential.

2. Do employers actually ask for it? Open ten job postings for the role you want. If a credential appears in several of them, it’s worth your time.

3. Can an employer verify it? Look for a digital badge or a listing in the Credential Engine Registry. Search any credential for free at credentialfinder.org.

4. Does the time and cost fit my life? With a full-time job and a family, plan on about six months. With most evenings free, many people finish in about three.

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Frequently Asked Questions

Do I need a college degree for these credentials?
No. None of the credentials on this list require a degree.

Will a credential alone get me hired?
Rarely by itself. A credential works best when it’s added to your LinkedIn Licenses and Certifications section; its skills are listed in your Skills section, and you can talk about a real project that uses those skills in an interview.

How do I know a credential isn’t a scam?
Check who issues it, whether its requirements and costs are published openly, and whether you can verify it online. A listing at credentialfinder.org is a strong sign of transparency, legitimacy, and value.

Are costs and requirements current?
They’re current as of November 2026 and can change. Always confirm on the issuer’s website before you enroll.

Your Turn
Which credential helped you most in a career change? Share it in the comments below.

Have a question about careers, credentials, or getting hired? Reply to this email, leave it in the comments, or send it to ceo@applymba.org. We’ll answer the best questions in a future Ask the CEO. Which credential helped you most in a career change? Tell us in the comments. And send us your question. We may answer it in a future Ask the CEO.

Credential Edge: AI Advantage is published by the MBA Standards Board™, which defines competency standards for MBA-level business education and issues verified professional credentials. Cheryl Nunn is the CEO and author of the forthcoming book GET FOUND. GET HIRED.

Your career experience and skills proof already exist. The question is whether anyone, or any ATS or AI software can see it.

Welcome to the first Credential Watch, our monthly look at the credentials, certifications, and short-term programs shaping who gets hired. We’re starting with the big picture, because the numbers tell a story most job seekers never hear.

The Credential Universe Is Bigger Than You Think

Credential Engine, a national nonprofit, tracks credentials offered in the United States. Its 2025 Counting Credentials report found 1,850,034 unique credentials, including degrees, licenses, certifications, certificates, apprenticeships, and digital badges.

That’s nearly two million ways to show what you know. And that’s precisely the problem!

When there are that many credentials, an employer can’t possibly know what each one means. So how does a hiring system tell a rigorous credential from a weekend badge?

Where the Credential Engine Registry Comes In

Credential Engine also runs a free public registry where organizations publish detailed descriptions of the credentials they offer. As of October 2026, the Credential Engine Registry describes more than 134,000 credentials from more than 33,800 organizations. Those credentials are connected to more than 231,000 competencies, 4,600 occupations, and 91,000 learning and training opportunities.

Those descriptions are written in a shared format called the Credential Transparency Description Language, or CTDL. Think of CTDL as a common language for credentials. It lays out what a credential requires, who issues it and which skills it proves, in a form that both people and software can read. That’s what makes a credential searchable, comparable, and readable by the AI systems now screening applicants.

Most credentials aren’t there yet.

In an Accredible survey, 60 percent of employers said they wanted credentials connected to their hiring platforms, but only 34 percent of credential issuers provided the structured data those systems need. A credential a person can read but a machine can’t is only half visible.

The Real Gap Isn’t Missing Credentials. It’s Where You Put Them.

Here’s what we see again and again. Many professionals list certifications at the bottom of page two of their resume, use abbreviations a screening system may not recognize, or never add them to the Licenses and Certifications section of their LinkedIn profile at all. Others list a credential with no way for an employer to confirm it. In an AI-heavy hiring market, that’s starting to look the same as not having one.

So the proof exists. It just isn’t visible when someone is screening your application or deciding whether to call.

Match Your Skills to Your Credentials

Most people never take this step, and it may matter most. Every strong credential is built on specific skills. When those same skills appear in your LinkedIn Skills section, recruiters searching for them can find you.

For example, each MBA Standards Board™ credential, the MBAe™, CMBA™, Certified MBA™ and Board Certified Director™, and our 21 short term, non college required specialization credentials lists ten in-demand skills on its digital certificate.

A credential holder who adds those ten skills to their LinkedIn profile shows up in searches for them. One who doesn’t, stays hidden, even with the same credential! This works the same way for any credential you hold, from any issuer.

Your Five-Minute Credential Check

1. Open each digital credential or certificate you’ve earned and click on it. Write down the skills it lists.

2. Compare that list to the Skills section of your LinkedIn profile. Add any that are missing.

3. Make sure each credential appears in LinkedIn’s Licenses and Certifications section, with the issuing organization’s full name and a link to its verification page.

4. On your resume, spell out each credential’s full name the first time, followed by its abbreviation, and move your strongest ones higher up.

5. Check whether each credential is listed in a public registry. You can search the free Credential Finder at credentialfinder.org.

If your credential isn’t clickable and doesn’t show any skills, it may not be readable by AI hiring tools or applicant tracking systems. That’s worth knowing before you invest your valuable time and money in any credential. Also before your next application.

Look It Up on Credential Finder

Credential Engine’s free Credential Finder lets anyone search the Registry. Go to credentialfinder.org and type the name of a credential you hold, or one you’re considering, into the search box.

A registered credential’s page shows who issues it, what it requires, what it costs and which skills it proves. If you’re comparing programs, check them here before you spend a dollar. A credential that isn’t listed isn’t necessarily a bad one, but not as well accepted. You’ll have to do more work to confirm what it really offers and confirm it meets current CDTL language to be readable by AI and employment software screening protocols common today.

Your Turn

Have you listed the skills from your credentials in your LinkedIn Skills section? Tell us in the comments. And if you hold a credential and aren’t sure whether employers can verify it, ask. We’ll answer in a future edition.

^{Sources are Credential Engine, Counting Credentials 2025 (December 2025), Credential Engine Registry and Credential Finder (accessed October 2026), and Accredible’s employer and issuer survey.}

The Credential Watch is published by Credential Edge: AI Advantage from the MBA Standards Board™, which defines competency standards for MBA-level business education and issues verified professional credentials. Cheryl Nunn is the CEO and author of the forthcoming book GET FOUND. GET HIRED.

Stellar Leadership by Dr. Avis D. Dickey

Avis makes the case that leadership isn’t only for the C-suite. Drawing on research and real cases, she gives leaders at every level practical tools for building trust, resilience and inclusive cultures, including in hybrid workplaces and with AI.
Find it on Amazon

Recalculating, curated by JoAnn M. Laing

JoAnn brought together more than 97 experts to show small business owners how to get past gridlock, find a new direction and grow. It won a 2017 Independent Press Award.
Find it on Amazon

The Art and Science of Organizational Leadership by Dr. Jonathan H. Westover

Jonathan, a five-time best-selling author, shows how to build high-performing cultures through people and innovation.
Find it on Amazon

Humans Not Headcounts by Josef Stetter

Inspired by Josef’s TEDx talk, this book is about reclaiming work, worth and culture in the age of AI. It’s a call to lead people as people, not as numbers on a spreadsheet.
Find it on Amazon

Strong Mind by Dr. Toby Brooks and Wil Fleming

A guide to focus, confidence and resilience for weightlifters. Its lessons on performing under pressure apply well beyond the gym. Toby also wrote Season of Change, the story of a small coal-mining town and its baseball team beating the odds.
Find Strong Mind on Amazon | Find Season of Change on Amazon

Sprint Recruiting and The FutHRist by Trent Cotton

Sprint Recruiting applies agile methods to recruiting, helping talent teams work faster and smarter. It’s also useful for job seekers who want to understand the other side of the table. His newer book, The FutHRist, lays out five mindsets HR professionals need for the future of work.
Find Sprint Recruiting on Amazon | Find The FutHRist on Amazon