¿Cuáles son las mejores prácticas para establecer contactos con profesionales de la IA en el mundo académico?

Connect, Learn, Collaborate! 🎓🤖 I'd advise focusing on strategic interactions. As Dr. Emily Thompson notes in "Networking in AI Academia," "Purposeful connections are key." Identify your academic interests, join relevant AI online communities, and actively participate in academic events. Reach out to peers and experts, fostering dialogue and collaboration. Follow-up is crucial in maintaining these relationships. Expanding and diversifying your network broadens your academic horizons, enabling cross-disciplinary insights and opportunities. It's about creating meaningful, lasting connections in the AI academic field. 🌐🔬

¿Buscas profundizar en un área específica de la IA o encontrar un mentor? Esta claridad guiará tus esfuerzos de networking. Qué quieres lograr con la IA? ¿Estás convencido de que es más que solo Hype o ves que el mundo está yendo allí y por eso quieres dirigirte tú también ahí? Por qué es importante realizarse estas preguntas? Por qué tener un propósito firme alrededor de un tema hace que no lo soltemos. Si no quedará como esa dieta que nos gustó mucho, la hicimos dos días y quedo en el olvido!

I feel it's important to recognize the value you bring and how your network can benefit from your contributions. Identifying a specific area of expertise or interest within AI (e.g. NLP, GenAI, CV, or AI in Healthcare) is essential as it helps you seek academic networks focused within that domain. This not only helps you build a meaningful network but also opens doors for potential collaborations. Networking is not just about making connections; it's about maintaining them. Stay active on platforms like LinkedIn, stay updated with their works/publications, and express your passion for similar topics. Lastly, keep adding to your knowledge base. The more expertise you have, the more people you can rope into your network!

Attend Events: Go to AI conferences, workshops, and seminars to meet people in person. Use Social Media: Connect with AI professionals on LinkedIn and Twitter. Collaborate on Projects: Work on projects with others to build relationships. Ask for Advice: Request informational interviews with researchers for guidance. Join Online Groups: Participate in AI forums and groups for discussions. Share Your Work: Publish your research and share it online. Be Respectful: Be polite and professional in your communications. Stay Informed: Keep up with the latest AI trends and research. Contribute to Open Source: Get involved in open-source AI projects. Attend Local Meetups: Join or organize local AI meetups for face-to-face connections.

Like many technology areas AI is Vast and it is important to have niche or at least focus areas defined. Academics generally have wider domain but it is always good to identify which sub domain area along with goal - Like just to get knowledge or proficiency or getting something implemented. On industry side, We have mostly goals of solving a business problems as goal and making COE and developing capabilities are two requirement we often collaborate with Academics.

Initially and foremost, Treat networking with sincerity and decency. Relationship building takes time, and networking is a two-way street. Strive to establish connections with academic AI professionals by being proactive, persistent, and patient. Academic institution should focus on organising research seminars, talks & workshops. An excellent way to meet professionals, ask questions, and engage in discussions to gain insights. Attending in-person conferences and workshops. Such events provide opportunities for networking, attending talks, and engaging in discussions. Joining Online Forums and Communities, such as academic social media platforms, where researchers and professionals share their work and insights.

- Attend conferences to connect in-person. Volunteer for committees. - Follow top researchers on social media. Comment thoughtfully on work. - Collaborate on papers and open source projects. Demonstrate skills. - Lecture at universities. Highlight real-world applications. - Sponsor academic workshops tailored to your business. - Keep in touch through thoughtful discussion, not just proposals. Focus on building relationships.

In academia, AI researchers prioritize R&D, wielding deep theoretical knowledge and the scientific method. To connect effectively, explore the impact of their work through accuracy, optimization, and key metrics. Then, pivot the conversation towards potential industrial or commercial applications where their research can create real business impact. While networking, be a great listener, focusing on their achievements rather than solely your own goals.

I would start with the approach to find what you are passionate to learn about, and share your learnings with the rest of the world (because chances are - other people care about it too!). Once you are a subject matter expert, people naturally will reach out to you. In my case - I started writing about AI and folks started reaching out for mentoring, consulting, etc. Not only was I able to meet great folks with diverse backgrounds, but I also was invited to collaborate on amazing projects! I think this also helped me in my career and transition from academia to industry

Networking with AI professionals in academia is about creating memorable connections. Catch their attention by sharing a unique experience or story that resonates with their work. Highlight the synergies between your goals and their expertise, illustrating how their contributions inspire you. After a genuine introduction, always present a specific ask. This approach not only demonstrates your interest but also gives clarity on how you envision working together and what your expectations are. After all, networking is all about building a meaningful relationship where both parties see the value in collaborating.

Host your own virtual "AI Open Mic" events. Organize virtual events through online communities where AI researchers and enthusiasts can share their work in a casual, low-pressure setting. These can be done through simple Zoom meetings in a small-group setting. This fosters conversation, idea exchange, and potential collaborations outside the confines of traditional conferences. You can pick a specific topic for each event, and then invite established professionals from that topic as mentors or guest speakers to attract a wider audience. With this approach, you’ll add value to the academic community by connecting each other while establishing yourself as an authority in the subject matter.

Sal y asiste a conferencias y eventos académicos relacionados con tus intereses. No solo te limites a asistir, sino participa activamente. Intercambia ideas y establece conexiones con ponentes y asistentes que compartan tus intereses. Hazte visible en comunidades académicas en línea. Únete a grupos de LinkedIn, participa en foros especializados y colabora en proyectos de código abierto. Esta es una manera excelente de conectar con expertos y colegas. Crea contenido relacionado con tu campo de interés en IA. Inicia un blog, un podcast o un grupo de discusión. Esto te posiciona como una figura clave en el área, atrayendo a mentores y colaboradores hacia ti.

I highly recommend looking for the social media accounts of your favorite AI experts and following them on Twitter (or X) and LinkedIn, as it enables you to stay updated with their latest work, keep track of their trials and errors, and engage with them in interesting and stimulating conversations. This also provides you with a huge advantage, as it breaks the barrier of them not knowing you, so when you meet them in real life, you both already feel closer and it makes it easier to interact, exchange, collaborate, or work together.

In the past, networking with AI professionals in academia often involved attending physical conferences, workshops, and seminars, fostering face-to-face interactions. Building connections through traditional channels like email and in-person meetings were prevalent best practices. Currently, advancing technology is playing more significant role. Online conferences, webinars, and collaborative platforms are becoming increasingly important after the pandemic. Staying active on evolving professional platforms, embracing AI-driven matchmaking for potential collaborations, and participating in decentralized and globalized research initiatives may shape the future landscape of effective networking in AI academia.

Online communities are the knowledge hubs for networking, sharing, and learning: 1) Choose the right medium platform that is accessible and aligned with your interests. Some communities offer various channels (e.g., slack, discord) 2) Follow the leading voices in the AI industry and research and get notifications to stay on top of current events 3) Contribute to each post, when feasible, sharing your own experiences and knowledge 4) Attend the online community forums and panels to learn and share

One strategy I've found helpful as an AI Law Master's student is actively engaging in online communities. Joining LinkedIn groups and following Twitter accounts focused on AI and data privacy law has been invaluable. I participate in discussions, share relevant articles, and attend webinars. Creating a personal online presence by posting my research insights and interacting with AI professionals has also opened doors for collaboration and mentorship, significantly enriching my academic journey in AI Law.

Engaging with online communities is a powerful strategy for networking with AI academics. Platforms like LinkedIn, Twitter, and Facebook, along with specialized forums, blogs, and webinars, serve as conduits for connecting with experts in the field. Participation in these spaces by following AI professionals, contributing to discussions, and sharing relevant content can elevate your visibility and credibility. Additionally, establishing your own digital presence through the publication of your work, sharing insights, and posing thoughtful questions encourages interaction and can attract the attention of academics and peers.

I personally prefer to learn from online groups , o foin some of the best in Telegram, LinkedIn , discord and readit. Git hub is also pretty good in this matter

One thing I’ve found useful is to attend in-person events to figure out who in the industry you want to connect to, collaborate with and learn from, and then find out which online communities they are connected to.

Find and Join Online Communities: Drawing on my experience as a three-time UiPath MVP and AI Brand Ambassador, I emphasize the significance of online communities in expanding your network with AI professionals in academia. Leverage platforms like LinkedIn, Twitter, and Facebook, as well as forums, blogs, podcasts, newsletters, and webinars, where these professionals actively participate. As you join discussions, share insights, and engage with AI experts, you not only build connections but also stay updated on the latest trends and research. Utilize these platforms to showcase your projects, share valuable insights, and pose questions, fostering a dynamic online presence.

So I would also suggest, go beyond standard networking, consider cross-disciplinary events that might benefit from AI, like biotech or finance conferences. Bringing AI insights to non-AI spaces not only widens your circle but also sparks innovative collaborations. Start a conversation about ethical AI use in other fields or how AI can drive sustainability. You become a bridge between domains, which can open up unique research and funding avenues.

Participation in academic and industry events is a highly effective method for networking with AI professionals. Conferences, workshops, seminars, hackathons, and meetups are fertile ground for learning about the latest advancements and for personal interactions with leading figures, as well as peers in the AI field. Engaging as a speaker, volunteer, or organizer can significantly enhance your profile within the community. Utilize platforms like Eventbrite, Meetup, or specialized AI conference calendars to discover opportunities that align with your interests.

Some mistakenly believe that attending an industry event means striving to watch as many lectures as possible, within a given industry event. I was exactly one of those about 25 years ago... Experience and maturity taught me that most, if not all of the lectures could be watched later on demand, mainly now with the proliferation of VOD, while physically meeting the experts face to face is priceless... The best advice professor Brian Uzzi (Kellogg) gave me about 20 years ago, was NOT to stay in my comfort zone and meet and great the friends, colleagues and experts I already now, but rather to be proactive, approach anonymous people, shale hands, go eat lunch in a round table with all unfamiliar faces... This is where growth will come from.

From my experience, actively engaging in new opportunities is key to networking success. I encourage you to step out of your comfort zone. Sign up for that hackathon, even if it feels daunting; attend webinars to broaden your knowledge, and volunteer at events to make meaningful contributions. Being proactive and open to new experiences can lead to serendipitous connections. It’s often in those slightly uncomfortable situations that I’ve forged my most valuable and influential relationships. These events are not just about learning AI; they’re platforms to connect with others who share your passion, creating a diverse and enriching community. Seize these opportunities – your network will grow stronger by attending as many events as you can!

Actually, I think these recommendations are too generic. I would forsake entirely most of the recos around Eventbrite, Meetup, etc and skip conferences unless you're hitting those for the masterclasses. Go to where the work happens. Workshops, hackathons are great while smaller events organized by local incubators and accelerators are often excellent. Look for smaller venues and learn enough basics to be able to follow their work and ask intelligent questions. This space is dominated by geeks of all kinds who often don't speak the language of networking but are fluent in math and algorithms.

Assister à des conférences, compétitions robotique, séminaires, etc. Permet de connaître des gens du milieu et se familiariser avec les technologies et l’état de l’art.

Research about the speakers, and topics in advance. AI being a vast domain, it's important to streamline your interests in AI. This will help you identify key people you want to connect with and allow you to engage in more meaningful discussions. Also, volunteering for events or presenting your own work can significantly boost your visibility. It will position you as an active contributor to the community and would open doors for more conversations with other professionals.

Present your work in relavent mediums, develop free YouTube video classes and also present your work in conferences and publish your manuscripts in journals.

In addition to attending online events, I strongly recommend attending "real" events. While it is often more convenient, cost-effective, and time-efficient to participate virtually, online meetings cannot replicate the human connection, intensity of exchange, and depth of discussion and relationships that are built in person.

To meet new peers, I started one-on-one conversations with individuals presenting posters. I also introduced myself, leading to connections with others interested in my research topic. It was surprising to discover people at my university working on similar projects whom I hadn't known about before these poster sessions.

Most papers have email addresses of the authors or atleast the corresponding author. My experience has been that emailing the author(s) with a thought provoking question or clarification based on the paper establishes rapport. It demonstrates that you have absorbed the material they have written about and can discuss it. I have rarely found an instances where I did not get a response back. A nice way to break the ice and get connected to discuss ideas.

Reaching out to AI professionals in academia requires a thoughtful and respectful approach. After identifying individuals whose expertise and interests align with yours, initiate contact with a personalized message that conveys genuine interest in their work and clearly states your intentions, whether it's seeking advice, feedback, or potential collaboration. Be succinct and professional, and avoid overstepping by making premature requests for favors. Building rapport is key. Following up is equally important; express gratitude for their time, keep them informed of your progress, and share information or opportunities that may be of mutual interest.

Here’s my mantra: 1. Send them an article you like and ask what they think. 2. Ask to chat for 15 minutes about a cool AI topic. 3. Help them out first, maybe by introducing them to someone else you know. 4. Tell them about your work, but link it back to something you both care about. 5. Cheer on their achievements to show you’re really paying attention.

When we reach out to someone with an intent to collaborate, it is important to mention what we bring to the table. A collaboration works only when both sides contribute something unique to the project.

If you don't reach out you always loose but If you reach out and follow up chances are that you I'll get a new perspective. Also one thing is for sure - The world out there is willing to help you. You can quickly realize, Majority of them would love to give back. Only few will not respond but the majority will follow one world and one learning concept which is "sharing and learning"

🚀 Networking with AI Academics: A Strategic Approach 🤖 📚 Initial Research: Dive deep into their publications on Google Scholar. Understand the essence of their work. 👩🏫 Faculty & LinkedIn Profiles: Get a holistic view by reviewing their academic and professional profiles. 📝 Summarize Your Insights: Condense your findings onto one page. Grasp their interests and expertise thoroughly. 🤝 Making Contact: Now, reach out with a specific question or a modest request. Show that you value their work and have done your homework. Laying the groundwork makes your approach more personalized and effective! #AICommunity #AcademicNetworking #ProfessionalGrowth

Read research papers and publications. You could find them on various platforms like IEEExplore, Science Direct, Research Gate etc. I believe the best place to find topics that interest you is Google Scholar. Once you find papers that interest you and read them, you could find similar papers from the references and continue the chain. Further, in most cases, the email id or other contact info of the authors would be mentioned in the paper, to whom you can reach out with doubts and ask for help. This generally works out because even the esteemed authors want to network with people who are willing to read and understand their works. This approach allows you to gain the authentic knowledge about the research and topics you are interested in.

Stop sending general emails or messages to connected AI Professionals. The People whom you have connected with may have different resources/ideas to offer, so when you are approaching them, approach them with your needs politely, follow up with them respectively, don't spam, and don't send general messages, if they are not up for help, please don't worry as we as human beings have different things to do in life.

Show that you genuinely care about their work before asking for help. Recommend articles they'd love and slowly build the relationship from there. People can smell good intentions from a mile away... So, don't just try to network just for the sake of it.

Effective communication is vital when reaching out and expressing your goals. Articulate your thoughts and intention crlearly while providing your background. Maintain your motivation when recipient does not respond promptly when sending cold emails as they might have competing priorities to work on. Stay perseverance and eventually you just might achieve your desired outcome.

In the realm of networking with AI professionals in academia, it's essential to view the process as an ongoing commitment to building and nurturing relationships. Your contacts should be regarded not merely as gateways to information or opportunities but as potential long-term partners, mentors, or even friends. Demonstrating authentic interest in their professional endeavors and personal well-being, while being mindful of their time and personal boundaries, is fundamental. Offer your support and expertise when appropriate, and be receptive to their feedback and mentorship. Embrace a learning mindset and be open to evolving.

In the field of AI Law, building and maintaining relationships with professionals is essential, and it's more than just a one-time interaction. Treat your contacts not merely as informational resources but as potential mentors, collaborators, or even friends. Show genuine interest in their work and personal lives, while respecting their time and boundaries. Offer support and add value where you can, and don't hesitate to seek their feedback and guidance. Being open to learning and improving through these relationships fosters a sense of trust and loyalty, paving the way for meaningful and collaborative partnerships in the academic and professional world of AI Law.

To build and maintain professional relationships, actively listen and show genuine interest in others' work. Network through conferences, workshops, and social media. Follow up with contacts regularly, sharing relevant information and offering assistance. Collaborate on projects and be reliable. Respect boundaries and appreciate diversity in thoughts and backgrounds. Remember, consistency and mutual respect are key to lasting relationships.

Now more than ever, we need each others as partners. We are all in uncharted territory, on a voyage of discovery. Leveraging individual relationships in our network is vital. Personally, I appreciated it when a LinkedIn contact reached out to me, asking to discuss hairy AI business issues. The video call was symbiotic, helping me just as much as it helped him. As Dean at a highly respected university, his research perspective validated my business experience. We both enjoyed talking and have kept it up. His outreach prompted me to connect with others. Talking with my ever-expanding network helps me navigate the many AI challenges & opportunities. It also makes me happy. Let’s continue to pay it forward and solve problems, together.

Building and maintaining relationships in the professional world goes beyond mere networking; it's akin to cultivating a garden of diverse and enriching connections. It's important to approach each contact not just as a stepping stone to your next opportunity, but as a unique individual with whom you can forge a meaningful partnership or friendship. Showing authentic interest in their professional journey and personal experiences creates a deeper, more genuine connection. Equally important is the willingness to give as much as you take, whether it's offering help, sharing insights, or simply being a listening ear.

Try co-authoring papers or creating joint proposals for conferences. Such collaborations can build bonds stronger than any casual meetup. Remember, a strong network in academia often comes from shared struggles and triumphs in pushing boundaries. Be a champion for others' work as well; celebrating their achievements amplifies your goodwill and establishes a network based on mutual respect and support.

Maintaining authentic relationships with experts from the Academia, never stops surprises me on how much value it contributes to my overall knowhow. Lucky me I do have some top notches AI experts in the academia who are also personal friends. Despite the fact I meet them for meals and beers, I keep learning a lot from watching them in their lectures and reading their ground breaking researches and discussing the researches with them. Just earlier this week I was fortunate enough to watch Professor Mor Naaman and Dr. Jonathan Rosenfeld demystifying GenAi alleged current superiority and discussing many aspects we all need to be very cautious about.

Again networking isn't a one-time deal or a transaction. It's about building lasting connections. My approach? Treat these contacts as mentors and/or collaborators. Share your achievements and challenges, offer support, and seek advice. It’s these consistent interactions that turn professional contacts into valuable connections.

Building a professional network is a life-long journey. When you meet someone interesting, ask them for introductions to others in their network, people they work with on AI projects or perhaps business leaders using AI solving problems they think are interesting.

Diversifying your AI network can also mean looking at emerging markets and regions where AI is just taking root. Engage with startups and accelerators in these areas. Their fresh approach can offer valuable insights and untapped opportunities for collaboration. Additionally, consider sectors where AI is underutilized, like agriculture or humanitarian aid, and bring your expertise there to make a tangible difference. This can lead to novel applications of AI, expanding your professional landscape in unexpected, rewarding directions.

Technical, domain, functional, hardware, product management, research, marketing and sales all AI perspectives are important and a combination of these will ensure you launch good AI products and services that can really cater to the needs of the industry and bring in sustainability in your solutioning approach.

A way to diversify your AI learning is chat with many different people in the same area of AI focus. For example, AI in HR is a focused area. Talk to those doing research and in think tanks. And, talk to those in practice at organizations implementing AI in HR applications. Don't forget about the vendors and startups. Talk to them as well, because they are doing some of the most innovative things around in AI and HR. Once, I called a thought leader (in a private firm) in this space to ask him questions about his AI framework. I did not expect him to reply but he did and spoke with me for over an hour! Never be shy or hesistant.

Embracing diversification and interdisciplinarity is key to unlocking new opportunities and fostering innovative thinking. By expanding your network beyond traditional academic circles to include AI professionals from various sectors like industry, government, non-profits, and media, as well as from diverse disciplines such as mathematics, engineering, psychology, and art, you gain a richer, more comprehensive understanding of AI's potential and challenges.This approach not only broadens your perspective but also opens up unexpected paths and collaborations, encouraging a blend of ideas and insights that can lead to groundbreaking advancements in AI.

It’s important to consider reaching out to people who may come from a different background and perspective as you. AI is often interdisciplinary and hence knowing people who are not just from the technical team but from other support teams can help gain more understanding about the application.

In my experience, AI is cool technology but it’s really difficult to get the right use case which benefit business. Interacting and networking with wide variety of people across multiple business domains helps understanding the business problem and applying AI. I personally benefited learning from outside my domain and how other people are solving same problem was really helpful. It’s all about use case and once that’s understood, it really helps to strategize and get buy in and required investment and execute it.

AI and more specifically GenAI was already proven to have the fastest adoption rate of any technology in human history. The adoption and the disruption is literally in any domain and vertical one can imagine... from automotive to pharmaceutical, from cyber security to telecommunications, from education to civil engineering... I am always amazed to realize how much business value in accelerating innovation, productivity and efficiencies new disruptive technologies bring mainly in the intersections between domains... 5G and AI enabled Autonomous transportation as a one noticeable example... limit oneself go stay within his/her comfort zones is leaving a lot of value on the table. Diversification in any means and form is the way to overcome.

AI is a tool that can be used in different settings to solve different challenges. So as a professional, either in academia or the business world, it is important to focus on the technical side of it and the applied part. That is to say, having a network with professionals/experts from different domains helps you cultivate an understanding of how this tool (AI) can be powerfully leveraged to tackle different challenges from different perspectives. It also opens the door to several growth and innovation endeavors where you can shine.

Beyond academia, broaden your network by connecting with AI professionals across diverse sectors and disciplines. Engage with experts in industry, government, non-profit, and media to gain varied insights. Collaborate with professionals from different disciplines like mathematics, engineering, psychology, or art to enhance your knowledge and creativity. Diversifying your network opens doors to unique opportunities and perspectives in the dynamic field of AI.

Actively seek out opportunities to collaborate on research projects. This could involve reaching out to researchers whose work aligns with your interests or participating in collaborative initiatives within your academic institution. Leverage academic networks and associations related to artificial intelligence. Join relevant groups, attend their events, and contribute to discussions to establish your presence within the community. Regularly publish your research in reputable journals and conferences. Sharing your work increases your visibility and can attract the attention of other AI professionals interested in similar topics.

My view is a little different… The best way to network in this space is to do the work. Experiment. Learn. Grow. But this time do it in public so everyone can see. You’ll naturally attract the right people and if you’re lucky maybe even an opportunity.

Go back to the basics , be hands on , join a startup part time , learn by doing , even if we have tons of experience , AI 🤖 is here to disrupt so accept the change , learn and re- learn , unlearn - existing knowledge with a new AI perspective. If possible invest money, be slow and methodical in approach.

In my experience Networking in academia involves attending events, using academic networks, offering value through collaboration ideas, joining research groups, being active on social media professionally, and participating in seminars. Engaging with academics, sharing insights, and seeking collaboration opportunities fosters connections within the academic community.

El mundo de la IA es muy grande, encuentra tu nicho y sé auténtico! Define lo que para ti sería una mejora continua: - Tener más seguidores? - Aumentar en likes tus publicaciones? - Tener más mentores? - Tener más ofertas de trabajo? - Más Solicitudes de conexión? No podemos decir que mejoramos si no podemos medir lo que hacemos! Establece unas metas claras y la motivación detrás de ellas, para que puedas revisarlas con tiempo. No te pongas mucha presión y disfruta del camino!!

1. Being able to offer a concrete problem statement that requires novel solutions 2. Great-to-have budget to co-fund research, most likely be successful to find time with the academics😂 3. Able to contribute in terms of skills, expertise and supervision to shape and deliver the project

Gartner correctly predicts that GenAI is currently at the peak of inflated expectations. One of the key challenges of AI strategic experts nowadays is how to assist managements and boards of directors to demystify the hype, while keeping the eyes on the ball. Currently it is the time for all to up-skill exercise extreme experimentations, build the mussels and look for the most cost effective use cases. It will take 2-5 years until the dust will start to clear the horizons... being patient should not mean neglecting or being passive. Managers should proactively build and adopt errors tolerant culture, if they haven't so far... organizations that will fail To adopt that will not be able to cope with the keep increasing pace of innovation.

An important point not considered. Mentorship & tutoring. Within the realm of AI academia it is indispensable for reciprocal professional development. Senior professionals impart wisdom and guidance to their mentees, fostering the next generation of AI experts. Also, mentors often find that the fresh perspectives and innovative ideas from mentees can significantly contribute to their own work, creating a symbiotic relationship. This cycle of giving back ensures a continuous exchange of knowledge and skills, where today's learners become tomorrow's leaders, ready to pay forward the mentorship they once received. It also enriches the individual and the broader academic community, ensuring the vitality and advancement of the AI field.

Take the leap and ask the questions to who you want to ask them to, the worst case is you won’t get a reply, and onto the next. Reaching out to people may seem like a daunting task but the immense opportunity and knowledge to be gained through others should be enough motivation to do so. The magic you’re looking for is in the work you’re avoiding.

Introducing AI applications to small businesses is a great way to build case studies that can be introduced to the classroom teaching. This ensures authentic learning experiences for the students. Student led projects can further help the business and enhance the student learning.