Write like you — not like a model

This capstone project aims to improve cultural and linguistic competency within the Pennsylvania Human Relations Commission (PHRC) by providing detailed demographic data on Pennsylvania’s foreign-born and Limited English Proficiency (LEP) populations. Analyzing data from the 2015 American Community Survey, the study identifies key demographic trends, such as the concentration of these populations in southeastern Pennsylvania and the prevalence of languages other than English, including Spanish, Chinese, and Vietnamese. Based on these findings, the authors recommend that the PHRC translate its materials, improve interpreter services, and develop a comprehensive language access plan to ensure equitable access to civil rights enforcement and services for all residents This paper addresses the need for the Pennsylvania Human Relations Commission (PHRC) to enhance its cultural competency to better serve the state's increasingly diverse population. As Pennsylvania's demographics shift, the study argues that governmental agencies must adapt their strategies to ensure that foreign-born and Limited English Proficiency (LEP) individuals have meaningful access to civil rights protections and services. The core objective of this project is to provide PHRC with essential demographic data identifying the composition, geographic distribution, and specific needs of these communities, thereby helping the agency bridge communication gaps and overcome systemic barriers to reporting discrimination. Based on an analysis of 2015 American Community Survey data, the researchers found that approximately 6% of Pennsylvania’s population is foreign-born. The largest foreign-born groups in the state originate from India, China, Mexico, the Dominican Republic, and Vietnam, with these populations heavily concentrated in the southeastern region of Pennsylvania, particularly Philadelphia County. Regarding language and education, the study notes that while many foreign-born residents have attained significant levels of education, language remains a critical hurdle. Spanish is identified as the most common non-English language spoken, followed by Chinese, Italian, German, and Pennsylvania Dutch, highlighting the specific linguistic needs the agency must address. To improve service delivery, the paper recommends that PHRC prioritize translating its educational and informational materials into these widely spoken languages beyond just Spanish. Furthermore, the authors advocate for the development of a comprehensive language access plan, the strengthening of interpreter services, and the implementation of more targeted, face-to-face outreach strategies in areas with high densities of foreign-born residents. The study concludes that while this demographic data serves as a vital starting point, PHRC should continue its progression toward full cultural competency by conducting internal organizational self-assessments and gathering further primary data to refine its outreach efforts.

400 words · Aug 2, 2026

Are Learned Skills and Traits Transferable? By: Brian Costanzo 07/15/2017 Based on interviews, conversations with peers, and observations of the job market, I have found that employers frequently pigeonhole candidates based solely on their degree. This rigid approach is outdated and hinders the ability to find the best candidate. The modern economy demands that individuals stand out through strong academic records, proven experience, and consistent professionalism. Furthermore, attractive candidates must demonstrate versatility and the ability to manage multiple tasks simultaneously. This raises an important question: if I am multi-talented and expand my horizons through independent study, why should I be confined to a single box? Asking this encourages candidates to avoid self-limitation, even when employers might impose it. To explore this, I will share a personal example demonstrating how learned skills and traits are transferable when one makes connections between theory and practice. I have consistently pursued my education since graduating high school in 2009, when I began working toward a Bachelor’s Degree in Public Policy at Penn State. My goal was to serve the public in a political or governmental capacity. Like many students, I worked full-time to cover tuition and living expenses. While serving tables on weekends, I consciously treated the job as a professional training ground—building customer rapport, cultivating a following, and earning the trust of colleagues. I made it a point to look, act, and speak like a professional. Even when exhausted, I recognized that a professional persists through adversity, adapting and embracing change. I also engaged customers in discussions about policy and government. By combining these experiences with my education, I developed a driven, optimistic, and analytical personality. My internships further enhanced this, providing critical field experience. I realized early on that to be exceptional, I had to apply academic concepts to the real world. I do not regret balancing work and school; the result was a highly qualified individual, as evidenced by my strong performance reviews and customer commendations. This experience proves that learned skills and traits are transferable across different domains. After graduating in 2013, I pursued a Master’s Degree in Public Administration at Penn State. This journey again involved balancing work, internships, and academic rigor. I am now focused on achieving success in my field. While definitions of success vary, I am applying it here to signify professional accomplishment and mastery. Through my Master’s program, I have gained valuable concepts, strategies, and operational knowledge. Many fail to recognize that public administration is often modeled on private sector practices. Although I lack a formal degree in sales or business administration, I have built a strong foundation in both. In my current role, I independently manage day-to-day administrative duties, serve as a brand ambassador, maintain product knowledge, ensure regulatory compliance, and handle finances. My ability to perform these tasks stems from my belief in transferable skills. My internship taught me the inner workings of state organizations, and I have successfully identified and adapted those parallels to my private sector employment. For instance, my understanding of state and local policies allows me to provide insights that profoundly benefit my customers’ businesses. As my May 2018 graduation approaches, I am reflecting on my journey. I have consistently applied academic knowledge to my work and employment experience to my studies. I have developed skills in human resources and office operations, gaining such depth in both sectors that I have occasionally been considered overqualified. The common thread—and the core message of this article—is that learned skills and traits are truly transferable when an individual maintains a thirst for knowledge and a drive to achieve their dreams.

598 words · Aug 2, 2026

Hello. My name is Brian Costanzo. I am a 35-year-old young professional who enjoys time with my family, building apps, trading on the stock market, being an uncle to my many nephews and nieces, and making memories with the people who count.

42 words · Aug 2, 2026

Value vs. Cost: Is there a real difference in a service-based industry? By: Brian Costanzo In my line of work, there is a common question that is asked by guests every day. Why can I buy a certain item at a larger hardware store at significantly lower prices than I can in your store? The answer is not as simple as some make it out to be. Business Dictionary defines cost as "An amount that has to be paid or given up in order to get something. In business, cost is usually a monetary valuation of (1) effort, (2) material, (3) resources, (4) time and utilities consumed, (5) risks incurred, and (6) opportunity forgone in production and delivery of a good or service. All expenses are costs, but not all costs (such as those incurred in the acquisition of an income-generating asset) are expenses." (2018). Simply, cost is what you pay solely for the product you wish to buy. An example would be a cashier checking out a customer who is buying a loaf of bread. At the time of payment, that customer is only paying for the bread and taxes. Value cannot be considered interchangeable or a synonym for cost. Value is defined as "a fair return or equivalent in goods, services, or money for something exchanged" by Merriam-Webster Dictionary (2018). The formula for value contains cost and thus cost cannot be similar to value. This essay will outline why value is inherently more important than cost and why value can be a confusing concept to customers yet crucial to understanding. With an objective definition set in place for cost, the value can be assessed in the objective and subjective perceptions many customers apply when buying a product or service. Value is not a specific metric that cannot be measured in certain terms. A great example that many people could relate to would be going out to eat. From a transactional standpoint, when someone goes out to eat they pay for food they choose to buy and receive. From a value standpoint, many more factors come into play. When you order food at a restaurant, what do you expect? It would be fair to assume that a customer would expect friendly service, sensitivity to time, high-quality food, drinks to be refilled before having to ask, plates to be cleared from the table upon completion of the meal, and accommodations in the event that a meal was not prepared correctly. One would expect a server to smile and remain calm through every portion of a customer's experience at a restaurant. It would also be expected that the staff would be knowledgeable and ready to make recommendations regarding food and drinks. There is also the expectation that servers will be very diligent when notified of possible food allergies. It can easily be seen that going out to eat is not purely a transactional experience where cost is the only concept to take into consideration. All of the other working factors in the dining experience are translated into value. A customer does not just pay for food rather they pay for the entire experience from being seated to being wished a good day or evening following their meal. They pay for the value of having access to a staff that can make recommendations, a kitchen that will accommodate the needs of customers, the ambiance that sets the mood for many restaurants, and the excellence in service by the server. If one wanted to simply have a transactional experience while dining, they would typically buy food from a store and make it at home. Why is this important for a business and a consumer to understand? From a business standpoint, it is crucial that the employees who are responsible for being the face of a company to understand that value and cost are concepts that must be separated and explained to the consumer. In my current line of work, it is very common for a customer to ask why the costs of a product are higher in a Sherwin-Williams store than in a big box store. As mentioned above, when a customer visits a Sherwin-Williams location, they are not simply buying a product and leaving. Customers are given the utmost of care, guided through any of the processes required for any of their projects, given expert advice on product selection, application methods, pairing products with the proper sundries, and being given the time by Sherwin-Williams' staff to ensure that a customer leaves with intention of coming back for any of their future projects. It is vital that the ambassadors of the Sherwin-Williams' brand convey to the customer that a higher level of cost can mean the difference between a successful project and one that is unsuccessful. The services provided by the sales associates within Sherwin-Williams perform functions that go far beyond selling a product. Much like the experience one expects when they dine out, a customer entering a Sherwin-Williams store should expect and will receive all of the advice and tools necessary to set them up for success. That very notion is the difference between cost and value. Value is what any strong, successful company provides to their clients and customers. Unfortunately, there are many who expect value in the form of cost. But when one truly has an excellent customer service experience, the increased cost of whatever service or product is being provided is often accepted or overlooked due to the satisfaction felt by the customer. In any customer service based industry, it is key to understand that value and cost are two separate concepts and that the former is vastly more important than the latter. Sherwin-Williams is a company that takes pride in its ability to provide value to its customers for any type of project. A successful company will even ensure that it provides value when taking care of customer complaints by walking through a customer's full experience and attempting to remedy any situation that goes beyond a simple refund. These concepts are crucial in a consumer based economy and a truly successful environment thrives for both the company and the customer when these concepts are understood and being utilized as best practices. References Cost (Business Dictionary). (2018). Retrieved May 30, 2018, from http://www.businessdictionary.com/definition/cost.html Merriam-Webster definition of Value. (2018). Retrieved May 30, 2018, from https://www.merriam-webster.com/dictionary/value

1055 words · Aug 1, 2026

Analysis of Professor Adam Eckerd’s Study on Risk Management and Risk Avoidance in Agency Decision Making Brian Costanzo PADM 594 Research Methods February 1, 2018 Executive Summary: This paper critically analyzes a study titled “Risk Management and Risk Avoidance in Agency Decision Making” by Adam Eckerd. The analysis begins with an issue statement that explores the main perception of risk and why it exists. It also examines whether the different views of risk held by the public and public agencies have an impact on public participation. The paper provides context on the issue, including assumptions from past studies, to clarify why the research question is being asked. This analysis also involves a discussion of the logic and assumptions behind the study and a review of data and facts that attempt to validate the author’s argument: that the perception of risk between the two relevant parties plays a significant role in public participation in agency decision-making. Findings are discussed to reveal what the author discovered through his analysis and where these findings point in terms of the overall research question. Finally, weaknesses in the study are identified, followed by a conclusion that puts the study's information into perspective. To wrap up the analysis, the paper discusses the implications of the findings, potential ramifications, and possible recommendations to address the issue. Issue Statement: The author identifies an issue with the incongruent views of risk held by agency decision-makers and the public. Past empirical evidence shows that agencies view risk as something to be managed, or something that can be managed, to calculate potential negative consequences when a decision is made. Conversely, the public holds the view that risk should be avoided or is not worth the chance, based on the impact it may have on an individual. The author chose this issue due to the low level of public participation in public agencies. He wants to explore what may be causing these incongruent views on risk and how to open the possibilities for dialogue to foster increased public participation in decision-making. Context: Past empirical evidence has shown that the traditional dialogue between decision-makers and the public took the form of a comment-response model. This evidence has also shown that this method is insufficient and creates an imbalance in decision-making. The author notes that there have been efforts to expand public input and influence in agency decision-making but argues that an insufficient framework was not the only issue in gaining genuine public participation, despite decades of efforts. The author looks at another factor that could potentially impact public input: the divergent ways in which an agency and the public view risk. Citing many other studies, he states that public inclusion has little effect on decisions made by agencies, creating what is referred to as a “democratic deficit” as the balance of decision-making lies mostly within the agencies. The framework for the issue in this study is the perception of risk and how it directly impacts participation. It is noted that public projects and decision-making are necessary, and risk is unavoidable. Agencies tend to view risk as something to be managed to measure the probability of negative outcomes. Juxtaposed to that is the public view that risk is not measurable and should be avoided by a generally risk-averse public. The research question is rooted in this: if these incongruent views of risk exist, do they have a significant impact on participation? Discussion and Analysis: The logic for this study is based on years of empirical research illustrating the underlying issue. While collaboration between agencies and the public was meant to be a means to more democracy, the opposite has been the dominant evidence. The author states that this issue is typically viewed through the framework of creating a better model for public input than the comment-and-response model. By assuming that the traditional framework for public inclusion was insufficient, the author proposed another factor that could be statistically significant in the low levels of public and agency collaboration. He argues that bureaucratic institutions are already in a state of conflict and tend to be closed off from public perceptions, which drastically diminishes the possibility of public input in agency decision-making. In attempts to explain the divergent view of risk, the author discusses literature from the Habermasian view, which states that under the right circumstances and rationality, public participation in agency decision-making could be viable. He also discusses how the Habermasian view requires individuals to discuss topics of importance at an aggregate level as opposed to an individual level. The assumption here is that individuals tend to view risk on an individual basis rather than a societal basis. Another assumption stems from the federal government’s Environmental Impact Analyses, which maintained the logic that if administrators have access to stakeholders, then administrators will utilize stakeholder input. As referenced earlier, bureaucratic organizations can be hostile to public input, creating a negative atmosphere for dialogue between agencies and the public. Findings: Data was used from four separate agencies: the Armed Forces Retirement Home (AFRH), the Federal Highway Administration (FHWA), the Army Corps of Engineers, and the District of Columbia’s Department of Transportation (DDOT). Each agency had a project serving as the subject matter for commenters. These projects included the development of land leased by the AFRH, the construction of a bridge (a joint venture between the DDOT and the FHWA), and the construction of an aqueduct managed by the Army Corps of Engineers. The author used a method of delineating between types of commenters, location of risk, object of risk, and view of risk. He also implemented a coding procedure to evaluate the thoroughness of the commenters’ responses. Using a method similar to surveys, the author gained various findings. Close to 70% of commenters were those who lived in the communities directly impacted by the three projects. It was also found that 90% of public participants discussed risk in human terms rather than environmental terms. This was in contrast to 58% of government agency actors who discussed risk from an environmental standpoint. Overall, government agency actors were shown to view risk objectively and from an aggregate viewpoint, while public participants viewed risk from an individual or community standpoint. These findings support the author's assumption: the data shows a divergent way in which public agencies and the public see and assess risk. Weaknesses: This study was well-conducted, and its mission was clearly conveyed. The author chose reliable sets of data for his analysis; however, there is an issue with only using three cases. By doing this, the author limited the scope of the study to the D.C. Metro area, producing findings that cannot be generalized to other locations. Another weakness admitted by the author was that agency actors were not required to provide answers with a great deal of substance or understanding, which could be a potential flaw in the study. A final weakness was the specific location used and the development of three hypotheses that take a more generalized tone. This seems to weaken the study slightly by trying to prove generalized notions with locale-specific data. Overall, however, the study was extremely well-assembled, conducted, and reported. Conclusions: By understanding past empirical studies on public participation and the factors impacting the lack thereof, one can conclude that it is necessary to use various lenses when studying this issue. The author’s approach of using the perception of risk shed light on findings proving that under-participation by the public is an issue that cannot be studied via only one or two variables. The findings point to the fact that agencies and the public have very different views on the meaning of risk, and these differing viewpoints could have a large impact on why the public tends to avoid taking part in agency decisions. There is also a struggle for agencies to properly convey policies and plans to the public due to this stark difference. While the idea of creating a more democratic atmosphere by enlarging the dialogue is ideal on paper, the author’s research pointed toward the opposite when risk is being assessed in reality. Implications: The issue of public participation in agency decision-making is vital when assessing the strength of representative republican institutions and their relationships with the public. Without public input on policy, there would be no need for public agencies to exist, much less the study of public policy itself. This issue is of immense importance to the field because it is necessary to gain more public input. We cannot be complacent with the public opting out of participation; however, with decades of effort and little to show for it, a shift in thought and research must be pursued to prevent a permanent democratic deficit. If the two parties cannot find ways to clearly convey their viewpoints on risk, it is very possible that public participation will continue to lessen and the gap of understanding will continue to grow. Recommendations: To bridge the gap in risk perception between the public and public agencies, efforts are needed on both ends. The author notes that organized public groups tend to view risk in a more aggregate sense. Ideally, the public should be more engaged in organizing themselves to better understand the agency's point of view and to engage the agency with concerns. It can also be argued that agencies must do more than the public. Since bureaucracies are often uninviting, public agencies need to work on restructuring initiatives to allow the open flow of ideas from within and outside the agency. In addition, public agencies should conduct further studies into public viewpoints on risk. A fundamental shared understanding of risk must be reached to increase participation rates. This can be accomplished through media and social media campaigns, town halls, enhanced literature, and public champions who can create the organization needed to interact effectively with public agencies.

1635 words · Aug 1, 2026

Abstract: The purpose of this paper is to apply a logic model to an existing set of employment service programs provided by Ephrata Area Rehabilitation Services. Specific inputs and outputs have been identified, along with initial, intermediate, and long-term outcomes and goals. An explanation of each is outlined in this paper along with a complete logic model, including lines of causality. In addition to these, there are external factors that have a direct impact on E.A.R.S. and its ability to provide employment service programs to its clients. The E.A.R.S. Organization: Ephrata Area Rehabilitation Services (E.A.R.S.) is a nonprofit organization located at 300 West Chestnut Street in Ephrata, Pennsylvania. The nonprofit was established in 1970. Upon opening its doors, there were two staff members and 20 clients. E.A.R.S.’s sole purpose is the training of individuals with intellectual disabilities in vocational activities to become productive and contributing members of the community (E.A.R.S., 2015). From 1970, E.A.R.S. expanded its services to include three locations, more than 250 clients, and over 50 staff members. Today, E.A.R.S. has grown to include seven different programs to meet the individualized needs of those served (E.A.R.S., 2015). “E.A.R.S. maintains that it strives to provide skill training for movement to other vocational opportunities, encourage and promote independence in all aspects of life, offer alternatives for personal adjustment in order to handle a variety of work and social situations, provide activities and workshops that develop problem-solving skills and decision-making, and to provide dignity for all accomplishments.” (E.A.R.S., 2015) E.A.R.S. provides four separate levels of work programs within the vocational training program. The first level is the Vocational Units. Licensed by the Department of Public Welfare, Vocational Units are an activities program that focuses on work (E.A.R.S., 2015). Each individual is encouraged to develop skills that allow them to move up through the levels of work programs with the ultimate goal of placement in competitive work. The second level of work programs provided by E.A.R.S. is called the Advanced Training Unit (ATU). ATU is also licensed by the DPW as an activities program. Following VU training, client placement into the second tier of the program is based on their ability to work under limited supervision while continuing to apply predetermined levels of job skills and performance. These individual training programs assist the clients by refining their work skills and allowing them to advance to the Mobile Work Crew and for placement in competitive employment (E.A.R.S., 2015). The third-tier program prior to placement in competitive employment is the Mobile Work Crew (MWC). The MWC is populated by clients chosen from the VU and ATU. According to E.A.R.S. (2015), jobs performed by the MWC involve lawn care, cleaning, light maintenance, and production work. The MWC conducts all work outside of E.A.R.S. facilities and integrates with the community and employer partnerships. The final tier of work programs provided by E.A.R.S. is called Transitional Employment Services (TES). This is the final program before independent employment for clients. The TES coaches clients on completing job applications, developing resumes and cover letters, locating jobs, coordinating job interviews, job training, and follow-up services (E.A.R.S., 2015). Logic Model: Four-tiered Employment Services Programs Inputs: In order for E.A.R.S. to effectively continue to provide its four-tiered employment services programs, there will need to be specific inputs, each a vital resource, such as staff and volunteers (human capital) to operate the programs and vocational training for staff and volunteers in order to develop a vocational curriculum and educate clients. Work equipment such as gardening, construction, and light maintenance materials are also important inputs that must be covered in the logic model. Other inputs like client transportation and physical space are resources of the four-tiered employment services programs provided by E.A.R.S. cannot do without. It is also vital to have staff training in Non-Abusive Psychological and Physical Intervention (NAPPI) and Cardio-Pulmonary Resuscitation (CPR) provided by NAPPI (NAPPI, 2017). Inputs of vital importance also include funding from stakeholders (donors) to be able to provide services. A final major input for this model is partnerships with employers and other nonprofits. Collaboration with nonprofits such as NAPPI (2017) and the ARC program provide the training and educational resources needed to provide the client services (Turnman, 2017). Processes: Processes for this logic model include training staff and volunteers in NAPPI and CPR, conducting van training for staff and volunteers, having staff attend customized employment job development seminars, exploring partnerships with employers and other nonprofits like the ARC—which provides many workshops such as customized employment job development seminars (Turnman, 2017)—conducting vocational training and classes for work-oriented clients, providing counseling for clients and encouraging independent and group work activities, and utilizing physical space to establish a vocational, learning atmosphere for clients. Outputs: Outputs for this logic model include staff and volunteers becoming familiar with NAPPI and CPR techniques, staff and volunteers completing van training, the establishment of a staff-developed vocational curriculum, securing partnerships with other nonprofits and employers to expand client opportunities, providing work-oriented clients with access to training and classes, clients seeking encouragement, counseling, and involvement in independent and group work activities, and the establishment of a vocational learning and practice space. Intermediate Outcome: (REVISED BUT REREAD) There are seven intermediate outcomes for the logic model. The first intermediate outcome would be an increase in the number of staff and volunteers trained in NAPPI and CPR techniques. An increased amount of staff and volunteers becoming NAPPI and CPR certified leads to the safety and well-being of clients. The second intermediate outcome is an increase in the number of staff and volunteers who are van trained. Van training for staff and volunteers is necessary for clients to have transportation. The third intermediate outcome is an increased amount of clients actively engaging with the staff-designed vocational curriculum. Client growth and success in the programs are dependent upon becoming heavily vested in the curriculum. Counseling and encouraging clients is key in retaining them. The fourth intermediate outcome would be an increase in the number of potential employers willing to hire clients out of TES and allow them to apply the skills they have learned under moderate supervision. Moderate supervision would include not allowing a client to be independent for more than 50% of the time they are working. The fifth intermediate outcome is having an increase in the number of clients in the workforce. Clients advance from the VU to the ATU and then advance to the MWC. Advancing through each program further prepares clients for independent employment. The sixth intermediate outcome is an increase in independent interest of clients to seek counseling and involvement in independent and group work activities. The seventh intermediate outcome is staff and decision-makers continuing to optimize facility space to provide client services. Optimizing facility space utilization is essential, ensuring maximal efficiency of the available square footage. Long-term outcomes and goals: (REVISED BUT REREAD) In addition to the intermediate outcomes, there are seven long-term outcomes and goals. The first is a continually growing number of employers hiring individuals with developmental disabilities and a growing number of said individuals entering into the competitive workforce. Clients offer significant value to the community, including competitive skills. The second long-term outcome would be the facilitation of clients advancing from the VU, the ATU, and the MWC to the TES using the developed vocational curriculum as a foundation for progress. The third long-term outcome centers around: clients planning and maintaining their own schedules, including bus transportation, and entering the workforce through TES. This self-structured behavior empowers clients to live as independently as possible. The fourth long-term outcome is continued attendance by staff and volunteers at educational, constructive conferences and training programs like the Arc’s customized employment job development seminars. Staff and volunteers effectively carrying out job coaching, application training, and employment placement services through attendance at seminars is invaluable as a resource. The fifth long-term outcome is total client integration into the community using their competitive work and vocational skills under limited supervision. Limited supervision would pertain to the same supervision a person who does not have developmental disabilities would see. The sixth long-term outcome is ensuring, disregarding turnover, 100% of staff and volunteers are trained and become certified in NAPPI and CPR, and 100% of staff and volunteers are van trained to provide transportation at all times. The seventh long-term outcome involves more awareness in community knowledge of individuals with developmental disabilities, programs to enhance the learning and vocational skills of individuals with developmental disabilities, and their value through their ability to work and compete in the job market, fostering a large amount of community support and understanding. Facilities also continue to be 100% utilized and opportunities for expansion are sought. External Factors: 1. Fundraising 2. Economy (Job Market) 3. Staff/volunteer turnover 4. Community perception of E.A.R.S and its programs 5. Partnership with employers, other nonprofits, and donors 6. Political Climate 7. Stakeholder commitment from decision makers, staff/volunteers, donors, and clients 8. Competition (Community Service Group- IDD Employment Services) Other Sources: E.A.R.S. (2015). Programs. http://www.ephratarehab.org/ears-programs.php Community Service Group. (2016). Programs and services. http://www.csgonline.org/programs-services/adult-services/ Turnman, N. (2017). The arc: Pennsylvania -- events. https://drive.google.com/file/d/0B8qMlhjBiW2hc05oWVNrVjdvdEk/view NAPPI Training. (2017). NAPPI training. http://nappi-training.com/

1518 words · Aug 1, 2026

One Hundred Applications. Zero Interviews. What Is Actually Going Wrong? Let me be direct about something that does not get said enough in professional circles: The hiring process is broken in ways that most job seekers are never told about. That is not a complaint. It is an observation grounded in data, and understanding it may be one of the most useful things a job seeker can do right now. I am currently navigating the job market. I bring a diverse professional background, graduatelevel education, and a structured approach to the search: I research organizations thoroughly, tailor each resume to the specific opportunity, track every application, and refine my strategy continuously based on what I observe. Yet, like many professionals I know, I have experienced extended periods of silence after submitting applications I believed were strong. That experience prompted me to stop asking “What am I doing wrong?” and start asking a different question entirely: “Where is the system breaking down?” What I found was illuminating and largely absent from the advice most job seekers receive. Your Resume May Never Be Read by a Human Being This is not an exaggeration. Before a recruiter ever lays eyes on a candidate’s resume, most large employers route every application through an applicant tracking system — software designed to scan submissions, identify relevant keywords, and rank candidates accordingly. According to Indeed (2026), ATS tools are filtering and ordering applications before any human review takes place.¹ The practical consequence of this is significant. A 2026 analysis by ResumeSync examining one thousand job descriptions found that 76% contained keywords that applicants commonly omitted from their submissions — and that 59% of applicants missed at least three required terms entirely.² These were not candidates who lacked the qualifications. They were candidates who described their qualifications in language the system did not recognize. The same research found that applicants who mirrored the precise terminology of a job description — specific software names, certification titles, exact tool references — achieved substantially higher ATS compatibility than those who relied on broader or more general phrasing.² Think about what that means in practice. Two candidates with identical experience submit applications to the same position. One writes “managed compliance documentation.” The other mirrors the job posting and writes “maintained regulatory records in accordance with Title 28 requirements.” The second candidate advances. The first does not. Their qualifications are the same. Their vocabulary is not. The Number That Should Change How You Think About Networking Here is a statistic worth considering. A 2025 LinkedIn survey of 443 people who had recently found employment found that 39% secured their position through a direct application — and 38% secured it through a referral or professional network contact.³ Nearly identical numbers. One pathway involves submitting documents into a digital queue and waiting. The other involves a conversation with a person who already trusts you. Most job seekers I know invest the overwhelming majority of their effort in the first pathway and treat the second as a supplement. The data suggests those priorities may be inverted. An 11% share reported being contacted directly by employers — meaning that for more than one in ten people who found work in that survey period, no application was submitted at all. The opportunity found them because of visibility, relationship, or reputation.³ Why Sending More Applications Is Often the Wrong Answer When a search stalls, the instinct is understandable: submit more. Cast a wider net. Cover more ground. The problem is that volume without strategy tends to produce more of the same results. Reporting from The Wall Street Journal in 2026 examined job seekers who had meaningfully improved their outcomes — and the common thread was not that they applied to more positions.⁴ It was that they applied more deliberately: identifying specific target organizations, pursuing referrals within those organizations, and investing time in optimizing how they presented themselves professionally before submitting anything. A targeted application to a role where you have already identified a connection inside the organization, tailored your resume to match the posting’s language, and researched the employer’s current priorities will outperform twenty generic applications submitted in an hour. That is not motivational advice. It is a description of how the process actually functions. The Question Worth Asking None of this is to suggest that individual effort, qualifications, or interview readiness are irrelevant. They matter. But they operate downstream of a filtering process that most candidates do not fully understand — and that most career advice does not adequately address. The professionals struggling to convert applications into interviews are often not failing at the job. They are failing at a parallel task they were never trained for: engineering a resume as a keyword document, cultivating networks before they need them, and treating a job search as a strategic campaign rather than an administrative exercise. That distinction matters. Because if the problem is structural, the solution is not simply to work harder at the same approach. It is to understand the system — and navigate it accordingly. What has your experience been on either side of this process? I am particularly interested in hearing from hiring managers and recruiters — as well as job seekers who have found approaches that actually moved the needle. #JobSearch #ProfessionalDevelopment #CareerStrategy #HiringTrends #PublicAdministration References ¹ Indeed. (2026). How applicant tracking systems work. Indeed Career Guide. https://www.indeed.com/careeradvice/resumes-cover-letters/ats ² ResumeSync. (2026). ATS keyword compatibility study: Analysis of 1,000 job descriptions. ResumeSync Research. https://www.resumesync.com/research/ats-keywords-2026 ³ LinkedIn News. (2025). How people are finding jobs: A survey of 443 recent hires. LinkedIn Talent Blog. https://news.linkedin.com/2025/how-people-find-jobs ⁴ Hiltzik, M., & Glazer, E. (2026, March). The smarter job search: Why targeting beats volume. The Wall Street Journal. https://www.wsj.com/articles/job-search-strategy-referrals-linkedin Note: Before publishing, verify all URLs directly as specific article paths may differ from those inferred from source descriptions.

967 words · Aug 1, 2026

1 Topic: Public Expectations vs. Realities for AI Integration into the Workforce Abstract Artificial intelligence (AI) integration currently occupies a complex "gray area" in the modern workforce, marked by tension between rapid technological advancement and widespread organizational resistance. While significant anxiety persists regarding job displacement, skill gaps, and the accuracy of AI outputs, evidence suggests these challenges are rooted more in organizational culture and communication failures than in technical limitations. This article examines the current state of AI adoption, contrasting prevailing skepticism with successful implementation models from industry leaders such as Colgate-Palmolive, JP Morgan, Salesforce, and MassMutual. By analyzing case studies and current research, this paper identifies five critical factors for successful AI integration: clear governance and democratization, decentralized business-unit spending, scalable training programs, model-agnostic architecture, and incremental scaling. Ultimately, the article argues that the potential benefits of AI— specifically workflow optimization and employee empowerment—can be realized when leadership moves beyond passive investment toward inclusive, hands-on workforce development, thereby bridging the divide between public expectation and operational reality. The Strategic Imperative: AI as a Tool for Empowerment The promise of AI in the modern workforce is not difficult to articulate: routine tasks compressed into seconds, analytical capacity extended beyond what any individual contributor could sustain, and organizational bandwidth freed for work that demands genuine human judgment. What is far more difficult to deliver is the cultural and operational infrastructure that allows those benefits to materialize at scale. The gap between what AI can do and what organizations are actually experiencing is not primarily a technology problem. It is a leadership problem—rooted in how institutions communicate change, invest in their people, and make decisions about the pace and shape of transformation. Understanding that gap requires a clear-eyed look at the anxieties driving resistance and the evidence already available from organizations that have begun to close it. The Challenges The most telling data point in the current AI landscape may not be how much organizations are spending on technology—it is how little they are spending on the people who use it. According to the Kingsley Gate Roundtable, companies invest three times more in AI platforms and infrastructure than in workforce development (Kingsley Gate, 2026). That imbalance is not incidental. It is the structural root of nearly every challenge documented in this section. Dr. Karin Kimbrough, Chief Economist at LinkedIn, testified before the U.S. Senate that 75% of global knowledge workers are already using AI—largely without formal instruction and, in many cases, 2 without full awareness that they are doing so—and that only 61% of the global knowledge workforce receives any structured AI training at all (Kimbrough, 2024). The question this raises is not whether employees are willing to adopt AI. Most already have. The question is whether organizations are willing to invest in helping them do it well. The consequences of under-investing in people show up consistently across the research. A 2024 Unisys Corporation report found that 82% of respondents expressed concern about inaccurate AI outputs, while many executives also reported limited organizational integration of AI into day-to-day workflows (Unisys Corporation, 2024). Employees operating tools they were never taught to use are not being irrational when they distrust the results; they are responding logically to an information gap their organizations created. Research reported by GovCIO Media & Research found that 37% of federal respondents identified skill gaps as the primary obstacle to accelerating AI adoption and described resistance as a people problem rather than a technical one (Oakland, 2026). The federal workforce is not uniquely skeptical—it is representative. Across industries, Stanton Chase reported that 41% of industrial-sector organizations had not communicated to employees which roles AI might change or eliminate, while 27% said AI ownership in the organization was still unclear or under discussion (Duniec, 2026). In an economic environment where cost-of-living pressures are already acute, the absence of that communication does not produce neutrality. It produces fear. Concerns over ownership, governance, and data security become more acute when platforms are deployed faster than the frameworks designed to manage them. The displacement anxiety that surrounds AI is not unfounded. Jeananne DeWitt Grosser, COO of Vercel, reported that her company’s AI adoption reduced a traditional customer support team of ten agents to a single agent supported by an AI assistant (Grosser, 2025). Vercel characterizes the transition positively, noting that displaced employees were reassigned to more creative, higher-value projects. That outcome, however, is not guaranteed—and the difference between workforce redeployment and workforce reduction is almost entirely a function of how deliberately an organization has invested in its people before, during, and after the transition. The Vercel example is instructive precisely because it surfaces the stakes: when technology investment runs ahead of human development, the efficiency gains are real, but the human costs are negotiable. Organizations that negotiate them well are the ones that build training programs, communicate transparently about role changes, and scale deployments at a pace their workforce can absorb. The urgency of closing this investment gap is underscored by the pace of change in the labor market itself. Dr. Karin Kimbrough testified that 25% of the work skills required by the global workforce have changed since 2015, and that employers are increasingly placing a premium on demonstrable AI proficiency (Kimbrough, 2024). Technology is not waiting for training programs to catch up. Kingsley Gate notes that AI platforms are being developed and deployed faster than the workforce development infrastructure needed to support them (Kingsley Gate, 2026). This is the central tension the data reveals: organizations have committed capital to the technology side of the equation and deferred the human side, treating workforce development as a secondary priority rather than a prerequisite. The evidence suggests this sequencing is 3 precisely backward. The companies demonstrating the most durable AI success—explored in the following section—are the ones that treated human investment not as a follow-on to technology deployment, but as the foundation it rests on. Despite these challenges, the organizations that have made the shift — investing in governance, training, and workforce empowerment alongside the technology itself — offer a clear picture of what successful integration looks like in practice. The Five Factors in Practice Clear Governance and Democratization Governance, in the context of AI, is not about restriction, it is about clarity. When employees understand what tools are available, what boundaries exist, and what is expected of them, the uncertainty that fuels resistance begins to dissolve. Democratization takes that one step further by distributing both access and ownership across the workforce rather than concentrating them at the executive or IT level. Colgate-Palmolive’s approach illustrates this distinction with unusual precision. Kli Pappas, Global Head of AI, did not deploy AI to her workforce—she invited them into it, building and launching a company-wide AI Hub with mandatory training designed not to restrict but to include (DeRose, 2025; Retool, 2025). Rather than positioning AI as a tool handed down from leadership, the program created an environment where employees could engage with it on their own terms, dismantling the stigma that so often precedes resistance. The result was not passive compliance but active participation: thousands of employees designed their own AI assistants and implemented workflows tailored to specific friction points in their daily work (Retool, 2025). As Pappas put it, the goal was to have the people closest to the problem involved in creating the solution (Retool, 2025). That outcome was not incidental. It was the direct product of a governance model that trusted employees with agency—and of an organization willing to invest in building that trust before expecting results. Decentralized Business-Unit Spending When AI investment is consolidated at the enterprise level, innovation tends to bottleneck. Decisions pass through layers of approval, pilots require centralized coordination, and the pace of deployment lags behind the pace of need. Decentralized spending addresses this by giving individual business units the budget authority and operational autonomy to experiment within their own domains. The effect is compounding: instead of one centrally managed rollout, an organization gains multiple simultaneous proof-of-concept environments, each calibrated to a distinct workflow. Coca-Cola Consolidated offers a clear illustration of this principle, though the underlying company data cited here should be treated as internal reporting rather than independently verified public research (Coca-Cola Consolidated, 2025). Walmart’s experience reinforces the point from a different angle. The company’s AI shopping assistant, Sparky, has been reported to drive larger baskets among users, with customers using the tool placing orders roughly 35% higher in value than those who do not, and units purchased through Sparky rising more than fourfold in a recent period (Furner, 2026; AI Weekly, 2026). Walmart has also described Sparky as a practical, daily-use tool that supports both customer discovery and internal momentum around AI adoption (Modern Retail, 2026). That kind of lateral innovation is 4 difficult to manufacture from the top down. It emerges naturally when spending authority and implementation responsibility are distributed to the people who understand their operational challenges most intimately. Scalable Training Programs The data on workforce AI training is, in a word, alarming. Dr. Karin Kimbrough’s Senate testimony showed that 75% of global knowledge workers are already using AI, largely without formal instruction, while only 61% receive structured training (Kimbrough, 2024). Meanwhile, Kingsley Gate reported that organizations are investing far more in AI technology than in the development of the people expected to use it effectively (Kingsley Gate, 2026). This imbalance is not a minor inefficiency. It is the structural root of many of the anxieties documented throughout this article. Concerns about inaccurate AI outputs and uneven adoption are far easier to understand when employees are being asked to trust tools they were never properly trained to use (Unisys Corporation, 2024). Scalable training programs do not simply close a skills gap; they interrupt a cycle in which under-prepared adoption breeds distrust, distrust breeds resistance, and resistance breeds the very stagnation organizations are trying to avoid. The strategic imperative now is to redirect a meaningful portion of AI investment toward developing the human capacity to use it well. Model-Agnostic Architecture The AI landscape is not static. Platforms evolve, new models enter the market, and vendor capabilities shift in ways that are difficult to anticipate. Organizations that build their AI infrastructure around a single platform or provider are, in effect, betting their long-term agility on the continued relevance of that one choice. Model-agnostic architecture is a hedge against that risk—a design philosophy that prioritizes flexibility over convenience, ensuring systems can adapt as the technology evolves without requiring a wholesale rebuild. MassMutual offers a strong publicly documented case for organizations operating at a different scale. Rather than locking into a single vendor, MassMutual pursued a deliberate strategy of short, 12-month contracts and architecture designed for model swapping as the market evolves (Plumb, 2026b). The company also defined success metrics before deployment, established trust-scoring and evaluation criteria, and incorporated user feedback into model selection and workflow design (Plumb, 2026a). The results were concrete: roughly 30% developer productivity gains, IT help desk resolution times reduced from around eleven minutes to one, and customer service calls cut from about fifteen minutes to one or two minutes (Plumb, 2026a; Plumb, 2026b). For organizations navigating the model-agnostic principle without the capital resources of the very largest firms, MassMutual’s framework—short contracts, defined metrics, and flexible architecture—represents a replicable path to the same foundational goal: the ability to evolve without rebuilding. Incremental Scaling Perhaps the most human of the five factors is also the most strategically undervalued. The instinct to move quickly—to capture competitive advantage, justify investment, and signal organizational ambition—can lead companies to deploy AI at a scale that outpaces their workforce’s ability to absorb and adapt. The organizations that produced the most durable 5 results took a different approach: they started small, measured carefully, and expanded only once the evidence supported it. Salesforce launched its AI support platform to a limited cohort— about 10% of traffic—and saw 126 conversations in the first week before scaling to tens of thousands of weekly interactions; public reporting later described roughly 45,000 conversations per week, an 84% autonomous resolution rate, and a 5% reduction in support case volume (Nuñez, 2025). MassMutual pursued a similar discipline in its developer productivity initiative, defining success metrics up front and scaling only after measuring results clearly (Plumb, 2026a; Plumb, 2026b). Cresta provides a parallel example from the enterprise vendor side: public summaries of the company’s early work describe an initial pilot with Intuit that demonstrated significant revenue impact and later expansion to Fortune 500 customers including AT&T and United Airlines (ZenML, 2025). Incremental adoption is not timidity. It is the infrastructure for sustainable, workforce-respecting growth—and it is consistently the approach that turns pilots into production results. A Call to Action for Leadership The evidence is no longer ambiguous. Organizations that invest in their people alongside their technology — that build governance frameworks before mandating adoption, fund training at a scale equal with their platform investment and communicate transparently about how roles will evolve — are outperforming those that do not. The gap between them is not a function of budget or technical sophistication. It is a function of leadership will. The companies profiled in this article did not succeed because they had access to better AI. They succeeded because they made a deliberate choice to treat their workforce as a partner in the transition rather than a variable to be managed around it. The challenge to every executive, department head, and organizational decision-maker reading this is direct: stop treating workforce development as the line item that comes after the technology budget is set. Reverse the sequence. Ask not what AI can do for your organization, but whether your organization has done enough for the people who will be asked to use it. Build the training infrastructure before the deployment timeline demands it. Communicate about role changes before anxiety fills the silence. Extend the governance framework to the employees closest to the work because that is where the most valuable implementation insight lives. AI will disrupt old practices — that is not a question. The only question is whether that disruption produces growth or displacement, empowerment or fear. That outcome is not decided by the technology. It is determined by the choices leadership makes about how much the people behind it are worth investing in. The organizations that get this right will not just integrate AI successfully. They will build the kind of workforce trust and institutional capacity that compounds over time — the kind that no off-theshelf platform can replicate, and no competitor can easily close. That is the real competitive advantage available to leadership right now. The technology is already here. The only thing left to decide is whether to lead with it or let it lead without you. 6 References Coca-Cola Consolidated. (2025). Bottlecap AI Assistant and Hackathon Results [Internal Company Report]. Coca-Cola Consolidated. DeRose, A. (2025, June 18). This Fortune 500 enables its employees to create AI solutions to workplace problems. HR Brew. Duniec, J. (2026, May). How industrial leaders are underestimating AI workforce disruption. Stanton Chase Insights. https://www.stantonchase.com/insights/blog/how-industrialleaders-are-underestimating-ai-workforce-disruption. ERGO Group. (2025). ERGO GPT Platform and Workforce Transformation. Qorus-NTT DATA Innovation in Insurance Awards 2025 submission. Forrester. (2026, April). AIQ 2.0: Measuring employee AI readiness [Research report]. Furner, J. (2026, February 19). Remarks on Walmart fourth-quarter earnings call, as reported by Modern Retail and CDO Magazine. HFS Research & Cognizant. (2025, July). Scaling AI in Consumer Goods: The 15% Club [Research Report]. HFS. Kingsley Gate. (2026, February 2). Are we leading AI? Who’s truly steering the future of work? LinkedIn. https://www.linkedin.com/pulse/we-leading-ai-whos-truly-steering-future-workkingsley-gate-uawfc Nuñez, M. (2025, July 18). Salesforce used AI to cut support load by 5%—but the real win was teaching bots to say “I’m sorry.” VentureBeat. https://venturebeat.com/ai/salesforceused-ai-to-cut-support-load-by-5-but-the-real-win-was-teaching-bots-to-say-im-sorry/ Oakland, S. (2026, May 22). Feds confront AI skills gap amid rapid adoption push. GovCIO Media & Research. https://govciomedia.com/feds-confront-ai-skills-gap-amid-rapidadoption-push Plumb, T. (2026a, April 6). How MassMutual and Mass General Brigham turned AI pilot sprawl into production results. VentureBeat. https://venturebeat.com/orchestration/howmassmutual-and-mass-general-brigham-turned-ai-pilot-sprawl-into Plumb, T. (2026b, June 10). MassMutual’s AI strategy: 12-month contracts, 30% productivity gains, zero lock-in. VentureBeat. https://venturebeat.com/orchestration/massmutuals-aistrategy-12-month-contracts-30-productivity-gains-zero-lock-in 7 Retool. (2025, December 4). How Colgate-Palmolive became the model for enterprise AI adoption. Retool Blog. https://retool.com/blog/colgate-palmolive-enterprise-ai-adoption Unisys Corporation. (2024). Unisys AI Insight Report: Executive Sentiment on AI Integration and Risk [Research Report]. Unisys.

2696 words · Aug 1, 2026

The longer I navigate this job market, the more convinced I become that there is no universal blueprint for landing the next great opportunity. Every search is shaped by different circumstances, constraints, and goals — and what works brilliantly for one professional may stall another entirely. So I find myself genuinely curious: what is actually moving the needle for people right now? I've noticed job seekers tend to fall into a few distinct strategic camps — and most aren't purely one type. They're blending approaches, pivoting mid-search, or discovering that what served them a decade ago no longer fits the landscape they're operating in today. Vote in the poll below — which strategy best describes your current job search approach? The Precision Strike — Crafting a carefully tailored resume and cover letter for every single application, treating quality as the non-negotiable variable. The System Builder — Engineering iterative workflows, structured trackers, and volume-based outreach to maximize touchpoints and maintain momentum. The Networker — Largely sidestepping the cold-apply process in favor of relationship-building, informational conversations, and the power of the internal referral. The Sniper — Identifying a tight shortlist of target organizations and waiting — patiently, strategically — for exactly the right opening. And if you're running a hybrid, or you've shifted strategies mid-search — what prompted that change? Drop it in the comments. 👇 #JobSearch #CareerStrategy #Networking #ProfessionalDevelopment #JobHuntStrategy

229 words · Aug 1, 2026

A multidisciplinary professional with over 6.5 years of experience at the intersection of administrative operations, public policy, and financial markets — where analytical rigor meets operational execution. In financial markets, that means disciplined trade execution across equities and options, using EMA alignment, MACD crossovers, RSI thresholds, and candlestick pattern recognition to identify high-probability setups, backed by systematic risk management and performance tracking. In institutional settings, it means personnel coordination, decision-grade reporting, and workflow engineering that measurably improved efficiency, developed through direct experience within Pennsylvania state government. That operational edge is increasingly amplified through applied AI fluency. I am proficient across 10+ leading AI platforms — including Claude, ChatGPT, Gemini, Microsoft Copilot, and DeepSeek — with hands-on expertise in advanced prompt engineering to design intelligent workflows and automate recurring processes. This fluency goes beyond prompting: I design and build functional AI-powered tools end to end, translating specific operational problems into working applications. Examples include a webpage summarizer that condenses long-form content into fast, accurate summaries; a job application tracker for managing active applications and follow-ups; a weekly task heatmap that visualizes recurring priorities and accountability; an interactive staff training system built for a restaurant client; and an AI-assisted email triage app that automatically sorts and prioritizes incoming messages. Each tool reflects the same process — identify a real inefficiency, then engineer an AI-driven solution for it — and this is a working competency I am now actively extending into freelance and applied AI development work. Across every environment, the throughline is the same: an evidence-based approach to complex information, an instinct for inefficiency, and the ability to convert data into decisive action — assess comprehensively, document precisely, execute with confidence. Core competencies: data analysis, process optimization, electronic records management, technical market analysis, policy research, cross-functional stakeholder engagement, applied AI tool development, and AI-assisted workflow automation. Adaptable across high-accountability environments — from government and policy infrastructure to independent analytical, administrative, and AI-driven roles.

321 words · Aug 1, 2026