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<p>The Wharton AI Study Every Educator Should Read — and What It Actually Means</p><p>Category: AI &amp; Learning Outcomes &nbsp; | &nbsp; Reading time: 6 min<br>The Number That Should Be Keeping Provosts Up at Night</p><p>Imagine you ran a study where students using AI scored 48% better on practice problems — and then you took the AI away and watched their test scores drop by 17% compared to the control group. That is not a hypothetical. That is what researchers found when they studied the effect of an AI tutor on student learning at a business school — and the education world has spent far too little time sitting with what it actually means.</p><p>As Faculty&nbsp; at the University of Nevada, Reno. I also spend my doctoral research hours studying exactly this question: what happens to learning when AI enters the room? That Wharton number — the 48% gain that became a 17% loss — is the number I keep returning to, because it tells you everything about where the conversation is going wrong.</p><p>What the Study Actually Found</p><p>The research, conducted at Wharton, introduced an AI tutor into a business problem-solving curriculum. Students with access to the AI performed dramatically better during the assisted practice phase. On the surface, this looks like a win. AI advocates point to that 48% and call it transformation. And they are not wrong that something significant happened.</p><p>But when those same students sat down for an unassisted test — no AI, just the knowledge they had actually retained — they performed worse than the students who had never used the AI tutor at all. The gap was not marginal. Seventeen percent worse is a meaningful, measurable erosion of learning.</p><p>A complementary study out of Budapest deepened the finding. Students who used AI chatbots to study for exams performed significantly worse on follow-up assessments than students who had studied without AI assistance. The mechanism appears to be what cognitive scientists call offloading — when the brain consistently delegates a task to an external tool, it stops building the neural infrastructure to perform that task independently.</p><p>The Misread That Is Shaping Institutional Policy</p><p>Here is where I want to push back on the dominant interpretation, because I think institutions are about to make a category error that will cost students real learning.</p><p>The common read of studies like this is that AI is a crutch, therefore dangerous, therefore restrict it. That is the wrong conclusion. The problem is not AI. The problem is that we inserted AI into a learning environment that was never designed to account for it — and then we measured performance using assessments designed for a world before it existed.</p><p>Think about it this way. When calculators became available, the education response was not to ban them. It was to redesign what mathematics education was actually for. We stopped testing whether students could perform long division by hand and started asking whether they could reason about numerical relationships, interpret results, and know when an answer was nonsense. The assessment changed because what we valued changed.</p><p>AI demands the same recalibration — at a scale and speed that no institution is currently moving fast enough to meet.</p><p>What This Means for How We Design Learning</p><p>The Wharton finding is not an argument against AI in education. It is an argument that AI-assisted practice and AI-free assessment, sitting side by side in an unredesigned curriculum, will produce exactly this result: students who can perform brilliantly with the tool and struggle without it.</p><p>Three implications follow directly from this, and they are aimed at administrators and curriculum designers more than individual teachers.</p><p>First, assessment design has to change before AI deployment, not after. If an institution is piloting AI tutoring tools while still measuring learning through closed-book, AI-free exams, it has created a structural contradiction. Students are being trained in one mode and tested in another. That is not a student failure — it is a design failure.</p><p>Second, the practice-to-retention pipeline needs deliberate interruption. The research on cognitive offloading suggests that learners need regular, structured moments of unassisted retrieval — not as punishment, but as the actual mechanism by which knowledge transfers from assisted performance into durable understanding. AI-enhanced curricula that skip this step are not accelerating learning; they are accelerating forgetting.</p><p>Third, the 48% number is not the story; the 17% is. Institutions under pressure to demonstrate AI ROI will be tempted to lead with the practice gains and bury the retention loss. Researchers, educators, and accreditation bodies need to insist on the full picture. Assisted performance and genuine learning are not the same thing, and conflating them is how an edtech investment becomes a credential inflation machine.</p><p>The Question Underneath the Question</p><p>I want to end with something that the Wharton study surfaces but does not resolve, because I think it is the real animating question for everyone working in education right now.</p><p>What is education actually for?</p><p>If the answer is performance on proximate tasks — getting students through the next exam, the next module, the next course — then AI tutors are a reasonable tool and the 48% gain is the headline. If the answer is durable human capacity — the ability to reason, to retrieve, to think under pressure without a scaffold — then the 17% loss is the most important number in the room.</p><p>Those two answers demand entirely different institutional architectures. The tragedy is that most institutions have not yet decided which answer they are building toward. They are deploying AI into a purpose vacuum, and hoping the outcomes will sort themselves out.</p><p>They will not.</p><p>What would it take for your institution to answer that question before the next AI pilot goes live — and who in your organization has both the authority and the evidence to lead that conversation?</p><p>&nbsp;<br>Priscillar McMillan is a doctoral research assistant in Information Technology in Education at the University of Nevada, Reno, and founder of Kowa Agency. She writes weekly on AI, learning systems, and the institutional decisions that will shape education for the next generation.</p><p><br data-mce-bogus="1"></p>

The Wharton AI Study Every Educator Should Read — and What It Actually Means

<p><strong>Category: AI &amp; Learning Outcomes &nbsp; | &nbsp; Reading time: 7 min</strong><br><strong>A Problem That Hides in Aggregate Dat</strong>a</p><p>When an AI feedback system reports that it improved average learning outcomes by 15%, that number is doing significant work to obscure something important. Averages aggregate across students whose experiences with the system may have been dramatically different. They obscure whether the improvement was distributed evenly across student populations or concentrated among students who were already better positioned to succeed. And they say nothing about whether the feedback some students received was accurate, appropriate, and fair — or whether it was systematically biased in ways that disadvantaged students who needed the most support.</p><p>Algorithmic bias in AI educational systems is not a hypothetical future risk. It is a documented present reality that the research has identified clearly and the field has addressed inadequately. In my systematic literature review on AI feedback and learning retention, algorithmic fairness emerged as one of the most critical gaps: the evidence of the problem is substantial, the evidence of evaluated solutions is sparse, and the institutional governance mechanisms that would catch and correct bias before it causes harm are largely absent.</p><p>As a doctoral researcher in IT in Education at UNR and a former UNICEF consultant who has worked in contexts where the consequences of flawed educational technology fall hardest on the most vulnerable learners, I want to be direct about what this means. Algorithmic bias in AI feedback is not a technical edge case. It is a justice issue with real consequences for real students, and it is being systematically underprioritized in how institutions select, deploy, and evaluate AI tools.</p><p>Where Bias Enters the System</p><p>AI educational systems learn their behavior from historical data — patterns in how past students answered questions, wrote essays, engaged with material, and were evaluated by human instructors. This learning process is powerful, but it inherits whatever patterns were present in the historical data, including patterns that reflect past inequities rather than genuine differences in student capacity or preparation.</p><p>Automated essay scoring systems, among the most widely deployed AI feedback tools in higher education, provide a clear example. These systems are trained on essay datasets that are typically scored by human raters working within particular cultural and linguistic norms. When those training datasets underrepresent essays written by multilingual students, students from non-standard dialect backgrounds, or students whose rhetorical traditions differ from the dominant academic register, the resulting scoring algorithms perform with lower accuracy for those populations. A multilingual student whose writing reflects sophisticated code-switching or whose argumentative structure draws on a non-Western rhetorical tradition may receive lower automated scores not because their thinking is weaker but because the system was not trained to recognize its value.</p><p>Natural language processing tools used to detect AI-generated text introduce a related problem. Research has shown that these tools produce substantially higher false positive rates for text written by non-native English speakers — flagging human-written work as AI-generated at disproportionately higher rates for students whose English reflects multilingual backgrounds. In institutions using AI detection tools to enforce academic integrity policies, this means multilingual students face unfair disciplinary risk from a system that presents its outputs as objective and reliable.</p><p>The Feedback Quality Gap</p><p>Beyond scoring accuracy, algorithmic bias affects the quality and relevance of feedback itself. Adaptive learning systems recommend next steps, additional resources, and remedial support based on patterns learned from historical student data. When those patterns reflect historical inequities in which students received strong academic preparation, which students had access to enrichment resources, and which students were tracked into advanced coursework, the adaptive recommendations can encode and amplify those inequities into the present.</p><p>A first-generation college student whose preparation differs from the students on whose data the system was trained may receive feedback recommendations calibrated to a learning trajectory that does not match their actual needs. An adaptive system that routes students toward remedial content based on early performance patterns can lock students into lower-level pathways in ways that limit rather than expand their learning opportunities — reproducing in algorithmic form the tracking and sorting functions that have always disadvantaged particular student populations in education.</p><p>The research on AI inclusivity in higher education is consistent on this point: the analysis reveals that although AI technologies are increasingly adopted, their primary aim remains institutional efficiency rather than fostering equity. Initiatives explicitly designed to support underrepresented students are rare, and equity assessments before deployment are rarer still. The default assumption appears to be that AI is neutral unless proven otherwise, when the evidence suggests the opposite assumption would be more appropriate.</p><p>What Accountability Would Actually Require</p><p>The absence of evaluated solutions to algorithmic bias in AI educational systems is not because solutions do not exist in principle. It is because the institutional conditions that would generate and implement those solutions are not yet in place at most institutions. Three conditions are necessary and largely absent.</p><p>First, pre-deployment equity auditing. Before an AI feedback system is deployed at an institution, it should be evaluated for differential accuracy and differential impact across the student populations it will serve. This means disaggregating performance data by race, first-generation status, language background, disability status, and socioeconomic background — not as an afterthought but as a procurement requirement. Vendors whose systems cannot provide this data should not clear procurement review.</p><p>Second, ongoing monitoring with disaggregated outcomes. Equity auditing is not a one-time event at deployment. Student populations change, system behavior drifts as training data accumulates, and bias patterns that were not present at launch can emerge over time. Institutions need ongoing monitoring frameworks that track whether AI feedback systems are serving all students equitably throughout their deployment — and governance structures with authority to suspend or reconfigure systems where equity problems are identified.</p><p>Third, transparency and student recourse. Students who receive AI-generated feedback and assessment deserve to know that they are interacting with an AI system, to understand what data is being used to generate feedback about them, and to have a clear process for challenging AI assessments they believe are inaccurate or unfair. Most current AI deployment at institutions offers none of these things. Students often do not know which elements of their educational experience involve AI systems, and those who suspect bias have no formal mechanism for raising it.</p><p>The Governance Gap</p><p>The deeper problem is that most institutions lack the governance infrastructure to catch and correct algorithmic bias even if they wanted to. AI procurement decisions are often made in IT or finance offices without meaningful input from faculty, students, or equity officers. The people with the technical expertise to identify algorithmic bias are rarely in conversation with the people who have the institutional authority to act on it. And the people most affected by biased AI feedback — underrepresented students — are almost never in the room when systems are selected or evaluated.</p><p>Building adequate governance requires institutions to establish clear ownership of AI equity accountability, create channels for students to report suspected bias without professional or academic risk, mandate equity impact assessments as a component of AI procurement, and invest in the technical expertise needed to audit AI systems rather than relying on vendor-provided evaluations. None of this is technically difficult. All of it requires institutional will that the efficiency narrative of AI adoption tends to crowd out.</p><p>The students who are most harmed by algorithmic bias in educational AI are the students for whom educational equity already requires the most active institutional commitment. They are the students who have historically been underserved by educational systems, and who are now being underserved by AI systems trained on the data those historical failures produced. That is not an accident of technology. It is a choice — one that institutions can and should make differently.</p><p>What would it require for your institution to treat algorithmic equity auditing as a non-negotiable standard for AI procurement, equivalent to data security and accessibility compliance — and who has the standing to make that standard binding?</p><p>&nbsp;<br>Priscillar McMillan is a doctoral research assistant in Information Technology in Education at the University of Nevada, Reno, and founder of Kowa Agency. She writes weekly on AI, learning systems, and the institutional decisions that will shape education for the next generation.</p>

The AI Giving Your Students Feedback May Be Grading Them Unfairly. Here Is What the Evidence Shows.

<h2>Understanding Digital Transformation</h2>
<p>Digital transformation is a pivotal process that involves integrating digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It’s a journey of continuous adaptation to a rapidly evolving digital landscape. The role of a change manager in this context is crucial as they guide organizations through the hurdles and challenges of this transformation.</p>

<p>As businesses strive to remain competitive, embracing digital solutions becomes imperative. However, the journey towards digital transformation is fraught with challenges such as resistance to change, lack of expertise, and the need for significant cultural shifts. This is where a change manager becomes an indispensable asset.</p>

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<h2>The Essential Role of a Change Manager</h2>
<p>A change manager acts as the bridge between the technological possibilities and the human elements of change. Their primary focus is on the people side of change, helping employees understand, accept, and embrace new technologies and processes. They facilitate smooth transitions by addressing concerns, communicating effectively, and ensuring that everyone is on board.</p>

<p>Change managers employ various strategies to ensure successful digital transformation. These include stakeholder engagement, effective communication plans, and comprehensive training programs. By doing so, they help mitigate resistance and foster an environment of cooperation and innovation.</p>

<h3>Strategies for Successful Change Management</h3>
<p><strong>Stakeholder Engagement:</strong> Engaging key stakeholders early in the process is crucial. This involves identifying those who will be affected by the change and actively involving them in the planning and implementation phases. Their buy-in is essential for overcoming resistance and ensuring a smoother transition.</p>

<p><strong>Effective Communication:</strong> Clear and consistent communication is key to alleviating concerns and keeping everyone informed about the changes being implemented. A change manager ensures that communication is not only top-down but also encourages feedback and dialogue, making employees feel valued and heard.</p>

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<h3>The Importance of Training and Development</h3>
<p>Training programs are an integral part of digital transformation. A change manager coordinates these initiatives, ensuring that employees have the necessary skills and knowledge to work with new systems and processes. This reduces anxiety and builds confidence among staff, contributing to a more successful transition.</p>

<p>Moreover, ongoing development opportunities ensure that employees continue to grow alongside the technology, fostering a culture of continuous improvement and innovation. This proactive approach helps maintain the momentum of digital transformation and ensures that the organization remains agile and adaptable.</p>

<h2>Overcoming Resistance to Change</h2>
<p>Resistance to change is a natural human response. A change manager anticipates this and works proactively to address concerns, alleviate fears, and highlight the benefits of the transformation. By fostering a supportive environment, they help employees move from resistance to acceptance, ensuring that the transformation is embraced at all levels.</p>

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<p>Through empathy, communication, and strategic planning, change managers play a pivotal role in smoothing the path of digital transformation. Their ability to balance the technical and human aspects of change is what ultimately leads to successful implementation and long-term sustainability.</p>

<p>In conclusion, the role of a change manager in navigating digital transformation cannot be overstated. By guiding organizations through the complex landscape of change, they ensure that businesses not only survive but thrive in a digital-first world. Their work lays the foundation for a future where innovation and adaptability are at the forefront of business success.</p>

Navigating Digital Transformation: The Role of a Change Manager

<h2>Understanding the Need for Alignment</h2>
<p>In today's rapidly evolving technological landscape, aligning tech execution with leadership strategy is crucial, especially for regulated industries. These sectors face unique challenges, such as compliance and security, which demand a synchronized approach to ensure both innovation and adherence to regulatory standards. When technology is not aligned with strategic objectives, businesses risk inefficiencies and potential compliance breaches.</p>

<p>Effective alignment ensures that technological advancements not only meet current industry standards but also support the long-term goals of the organization. By integrating tech initiatives with leadership strategies, companies can enhance operational efficiency, improve customer satisfaction, and maintain a competitive edge.</p>

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<h2>Challenges in Regulated Industries</h2>
<p>Regulated industries, such as finance, healthcare, and energy, operate under stringent rules and regulations. These sectors must navigate complex legal landscapes while striving to innovate and improve service delivery. The challenge lies in implementing technology that complies with these regulations without stifling creativity and growth.</p>

<p>One of the primary hurdles is ensuring data security and privacy. With increasing cyber threats, companies must adopt robust security measures while remaining compliant with regulatory requirements. This requires a delicate balance between leveraging new technologies and maintaining strict security protocols.</p>

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<h3>Strategies for Successful Alignment</h3>
<p>To achieve successful alignment between tech execution and leadership strategy, organizations should consider several key strategies:</p>

<ul>
    <li><b>Engage Cross-Functional Teams:</b> Encouraging collaboration between IT, compliance, and leadership teams can foster a culture of shared responsibility and understanding.</li>
    <li><b>Continuous Training:</b> Regular training sessions can equip employees with the knowledge to implement technology effectively while adhering to regulations.</li>
    <li><b>Invest in Scalable Technologies:</b> Opt for technologies that can adapt to evolving regulatory landscapes and business needs.</li>
</ul>

<h2>The Role of Leadership in Tech Alignment</h2>
<p>Leadership plays a crucial role in aligning technology initiatives with strategic goals. Leaders must communicate a clear vision that integrates technology as a core component of business strategy. By doing so, they can inspire teams to innovate within the boundaries of regulatory compliance.</p>

<p>Moreover, leaders should prioritize transparency and accountability. Establishing clear metrics for success and regularly reviewing tech initiatives can ensure that projects remain aligned with strategic objectives.</p>

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<h3>Leveraging Technology for Competitive Advantage</h3>
<p>When executed correctly, technology can provide regulated industries with a significant competitive advantage. Innovative solutions such as artificial intelligence, blockchain, and cloud computing offer unprecedented opportunities for efficiency and growth. However, these technologies must be implemented thoughtfully, with a clear understanding of regulatory implications.</p>

<p>By staying ahead of technological trends and integrating them into strategic planning, companies can not only meet compliance requirements but also enhance their market position.</p>

<h2>Conclusion: A Unified Approach</h2>
<p>Aligning tech execution with leadership strategy is not just a necessity but a strategic advantage for regulated industries. By fostering communication between tech teams and leadership, investing in adaptable technologies, and prioritizing compliance, businesses can thrive in a regulated environment.</p>

<p>Ultimately, the goal is to create a unified approach where technology serves as a driving force for innovation while supporting the strategic vision of the organization. This alignment ensures that companies can navigate the complexities of regulation while achieving sustainable growth.</p>

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Aligning Tech Execution with Leadership Strategy: Insights for Regulated Industries

<h2>Understanding Vendor Risk Assessments</h2>
<p>In today's interconnected business environment, vendors play a crucial role in the operations of many companies. Particularly in a dynamic market like Reno, businesses rely on various external partners for services and products. However, this dependency also introduces potential risks. A vendor risk assessment is a strategic tool that evaluates the risks associated with working with these external entities. By identifying and mitigating these risks, businesses can ensure smoother operations and protect their interests.</p>

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<h3>Why Vendor Risk Assessments Are Crucial</h3>
<p>Vendor risk assessments help businesses identify vulnerabilities that could arise from their relationships with third parties. By conducting these assessments, companies in Reno can pinpoint potential issues before they become significant problems. This proactive approach not only protects the business but also enhances overall operational efficiency. <b>Understanding the risk profile of each vendor</b> enables businesses to make informed decisions about who they choose to partner with.</p>

<h3>Key Components of a Vendor Risk Assessment</h3>
<p>A comprehensive vendor risk assessment includes several critical components:</p>
<ul>
  <li><strong>Risk Identification:</strong> Determining potential risks associated with each vendor.</li>
  <li><strong>Risk Analysis:</strong> Evaluating the likelihood and impact of identified risks.</li>
  <li><strong>Risk Mitigation:</strong> Developing strategies to minimize or eliminate risks.</li>
</ul>
<p>By addressing these components, businesses can develop a robust framework for managing vendor relationships effectively.</p>

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<h2>Benefits of Vendor Risk Assessments in Reno</h2>
<p>Conducting vendor risk assessments offers several advantages for Reno businesses. Firstly, it enhances compliance with industry standards and regulations, which is crucial for maintaining a reputable standing in the community. Secondly, it fosters better communication and transparency between businesses and their vendors. This improved relationship can lead to more efficient collaborations and innovative solutions.</p>

<h3>Protecting Your Business from Potential Threats</h3>
<p>One of the primary goals of a vendor risk assessment is to protect your business from potential threats. These threats may include data breaches, financial instability of vendors, or operational disruptions. By identifying these risks early on, businesses can implement safeguards to protect their assets and continue operations smoothly even when unforeseen issues arise with vendors.</p>

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<h2>Implementing Vendor Risk Assessments</h2>
<p>To effectively implement vendor risk assessments, Reno businesses should consider the following steps:</p>
<ol>
  <li><strong>Establish Clear Criteria:</strong> Define what constitutes an acceptable level of risk for your business.</li>
  <li><strong>Select the Right Tools:</strong> Utilize software and tools that streamline the assessment process.</li>
  <li><strong>Regularly Review and Update:</strong> Conduct assessments periodically to account for changes in vendor performance or business needs.</li>
</ol>

<h3>Working with Experts</h3>
<p>For businesses new to vendor risk assessments, partnering with experts can be highly beneficial. Experienced consultants can provide guidance and support throughout the assessment process, ensuring that all potential risks are thoroughly evaluated. This collaboration can help businesses in Reno gain confidence in their vendor relationships and focus on growth and innovation.</p>

<p>In conclusion, vendor risk assessments are an essential part of business operations in Reno. By proactively managing vendor-related risks, companies can enhance their operational resilience and ensure long-term success.</p>

How Vendor Risk Assessments Can Enhance Your Business Operations in Reno

Enterprise resource planning concept. Businessman using tablet and laptop for business management planning and company improvement. Technology, Network, Enterprise resource planning.

<h2>Understanding the Unique Challenges of ERP in Regulated Industries</h2>
<p>Implementing an Enterprise Resource Planning (ERP) system is a complex endeavor, made even more challenging when dealing with regulated industries. These sectors, such as healthcare, finance, and pharmaceuticals, must comply with stringent regulations and standards. Understanding these unique challenges is crucial for success. Compliance requirements, data security, and change management are just a few of the critical aspects to consider.</p>

<p>Before diving into the implementation process, it's essential to conduct a thorough assessment of the regulatory landscape affecting your industry. This step helps in identifying specific compliance requirements that your ERP solution must meet. Failure to understand these requirements can lead to costly penalties and operational disruptions.</p>

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<h2>Assembling the Right Project Team</h2>
<p>The success of an ERP implementation heavily relies on assembling the right project team. This team should include individuals with diverse expertise, ranging from IT specialists to compliance officers. It's crucial to have representation from all departments that will interact with the ERP system.</p>

<p>Ensure that your team includes a project manager with strong leadership skills and experience in handling complex projects. This leader will oversee the project's progress, manage resources effectively, and ensure all team members are aligned with the project goals. Regular communication and collaboration within the team are vital for addressing any issues that arise during the implementation process.</p>

<h2>Choosing the Right ERP Solution</h2>
<p>Selecting the right ERP solution is pivotal, especially in regulated industries. The chosen software must not only cater to your business needs but also comply with industry regulations. Conduct a comprehensive evaluation of potential ERP vendors, focusing on their experience in your industry and their ability to provide compliance-ready solutions.</p>

<p>Consider factors such as scalability, integration capabilities, and user-friendliness. A solution that easily integrates with existing systems can streamline operations and enhance efficiency. Additionally, ensure that the ERP vendor offers robust support and training services to facilitate a smooth transition.</p>

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<h2>Focusing on Data Security and Privacy</h2>
<p>Data security and privacy are paramount in regulated industries. An ERP system will centralize sensitive information, making it imperative to implement robust security measures. Ensure that the chosen ERP solution offers advanced security features, such as encryption, access controls, and regular security updates.</p>

<p>Develop a comprehensive data security strategy that aligns with industry regulations such as GDPR or HIPAA. This strategy should include regular audits, employee training, and incident response plans to mitigate potential risks. By prioritizing data security, you can safeguard your organization's reputation and maintain compliance.</p>

<h2>Managing Change Effectively</h2>
<p>Change management is often overlooked in ERP implementations but is crucial for success. Employees may resist changes to their workflows or feel overwhelmed by new technology. Implement a structured change management plan that includes training sessions, clear communication, and stakeholder engagement.</p>

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<p>Engage employees early in the process by involving them in testing phases and gathering their feedback. This approach not only increases buy-in but also helps identify potential issues before full-scale deployment. By fostering a culture of adaptability and support, organizations can ease the transition and maximize the benefits of the new ERP system.</p>

<h2>Continuous Monitoring and Optimization</h2>
<p>The implementation of an ERP system is not a one-time project but an ongoing process. Continuous monitoring is necessary to ensure the system's performance aligns with regulatory requirements and business objectives. Regularly review key performance indicators (KPIs) to assess the system's impact on productivity, efficiency, and compliance.</p>

<p>Be prepared to make adjustments as needed and stay informed about changes in regulations that may affect your ERP system. By maintaining a proactive approach to monitoring and optimization, organizations can achieve long-term success and remain competitive in their respective industries.</p>

How to Lead Successful ERP Implementations in Regulated Industries

Priscillar Simon Banda

Priscillar McMillan is a high-impact Change Manager, ERP Implementation Lead, and Technical Program Manager with deep expertise in modernizing systems, aligning cross-functional teams, and driving transformation across regulated industries.

She has led full lifecycle implementations of enterprise systems — from client onboarding platforms and claims operations to medical compliance trackers and government reporting modules. Whether deploying ERP solutions or leading multi-department rollouts, Priscillar specializes in guiding organizations through the technical, operational, and human side of change.

Her track record includes:
	•	Leading cross-functional teams across IT, compliance, medical, and client services
	•	Managing system integrations tied to HIPAA, ORR-5, and federal grant compliance
	•	Driving complex ERP rollouts with minimal disruption and high adoption
	•	Building reporting and automation tools to enhance program visibility and executive decision-making
	•	Training and transitioning 100+ users across multiple departments through system migrations

Priscillar’s work spans FinTech, HealthTech, public sector, and nonprofit environments. She is fluent in leading enterprise-wide change, designing scalable operations, and aligning tech execution with leadership strategy. Known for her clear communication and structured thinking, she bridges the gap between executive vision and system delivery.

If your organization is navigating digital transformation, system migration, or program expansion — she is the technical operator and change architect you want in the room.


Priscillar Simon Banda

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