Unleashed - How to Thrive as an Independent Professional

Show Notes: Karen Friedenberg discusses a project she worked on to design an Intelligent Automation Center of Excellence for a Fortune 500 medical supply company. The challenge was that the organization was initially looking to leverage robotics process automation (RPA) technology to automate repetitive and manual processes. This led to the development of Intelligent Automation, also known as hyper automation. Defining the Meaning of Intelligent Automation The first step in this project was defining Intelligent Automation and defining its meaning. The client wanted to develop a center of excellence to coordinate efforts across the company to take advantage of new technology and benefits quickly and in a coordinated way. The center of excellence would serve various needs and be a resource for the organization. Karen explains that the first step was to identify the pockets within the organization where people were learning about robotics, process automation, AI, and chatbots. She then interviewed stakeholders to understand their strategic imperatives and goals, and a key understanding was to let business lead the way, not the technology. The second step focused on developing the structure of the Intelligent Automation Center of Excellence (COE), its interaction with other teams, and the roles and competencies of the COE team. The COE team would be responsible for staying on top of the evolving technologies and coordinating efforts to leverage project management and program management capabilities in a coordinated way. One of the great things about new technology is putting it in the hands of the business and users, allowing them to solve problems themselves. However, there were challenges, opportunities, and fear to address,  such as change management and fear of the business starting to do this. For example, IT was beginning to fear redundancy in many of their roles.  As a solution to these challenges, it was necessary for the COE team to identify their mission, roles, and responsibilities. The Center of Excellence Explained The Center of Excellence (COE) is a team that works to identify and prioritize automation candidates in business units. Karen talks about the knockout criteria they use to assess if a process is an automation candidate and if it can be done within existing systems. The COE then uses a box prioritization matrix to assess the impact and effort of each candidate. If it is easier and less risky, it may be a candidate for a citizen developer role. Governance is also a key aspect of the COE's role. The COE's role involves oversight and sharing best practices. They train and certify citizen developers to use new technology and processes, ensuring proper controls are in place. The SDLC (Software Development Lifecycle) is a model that aims to maintain flexibility and speed while ensuring proper controls. People submit requests through various methods, such as email, phone, or using shared systems like Leisha shared through SharePoint and Microsoft tools. The COE's role is to ensure that the process is secure and efficient, while also ensuring that the right controls are in place to prevent unauthorized changes to code. Discussion on the Design Phase of a Project Karen explains that they are still in the design phase and it has not been fully executed yet. The vision was to analyze incoming requests and determine who gets help. The team is divided into a business lead and an IT lead who would work with business analysts to assess the project's feasibility. The group provide different levels of support, such as a half-hour conversation or a three-month project with a business analyst and consultants.The first step is to train the business unit citizen developer and to provide regular reviews to the client. The team would also provide additional technical, business process, and change management assistance. The goal is to help the client team navigate their blockers and be a centralized source for sharing learnings and best practices across the business. Integration with The Center of Excellence The COE is complex and interacts with multiple systems, including  project management teams and various departments across the business. The team would also be aware of other projects in the company and work with them to ensure each project is documented and shared within the ecosystem to share information across departments and projects as required. Karen discusses the development of an Intelligent Automation center of excellence and the marketing approach taken to promote the service.  The center consists of five people and is being promoted internally through business optimization managers. The company is taking a crawl, walk, run approach, starting small and growing. She explains that some barriers to the center include resourcing, funding, and fear of AI impacting employees' jobs. Organizational change management is crucial in these efforts, as it ensures sustainability and avoids unintended consequences for employees. The Benefits of a COE The company anticipates benefits from the center of excellence, such as faster deployment of technologies, reduced manual tasks, and cohesion of information. The technology has tremendous benefits, but the bigger benefit is the new ways of working that can be applied across various parts of the business. The center of excellence also helps in teaching new ways of working and chain collaboration between the business and IT. Timestamps: 01:02 Designing Intelligent Automation Center of Excellence for a Fortune 500 company 02:22 Establishing an Intelligent Automation Center of Excellence 06:40 Automation and citizen development in a business unit 10:49 Implementing a citizen developer program 14:32 Implementing an Intelligent Automation center of excellence Links Website: https://www.piconsult.net/ LinkedIn: https://www.linkedin.com/in/karen-friedenberg/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.  

Show Notes: In this episode of Unleashed, the panel discussion focuses on the pros and cons of becoming an adjunct professor. The panelists discuss the motivations behind teaching courses as an adjunct professor, how to get hired, whether to teach in traditional MBA programs or other certificate or degree programs, the amount of work involved, typical pay, relationship building opportunities, project opportunities, and ancillary benefits such as access to datasets or research services. The discussion kicks off with Adam Braff, a data analytics executive/advisor, shares his reasons for teaching, stating that the best reasons to teach are not practical instrumental reasons but more passion and love for teaching and believes it is a creative act. Mary Kate Scott follows Adam. She teaches at the University of Southern California, Marshall School of Business in the MBA program, and Keck School of Medicine, focusing on healthcare. She has taught the business of healthcare, innovation and health care, new business models in health care, entrepreneurship and health care, and medical device business models. Mary Kate also shares her background with Procter and Gamble and later joined McKinsey for two years to become a better professor. She found she loved the position and stayed there for seven years, but she states that she found the joy of teaching to be both inspirational and fun. She enjoys the level of engagement and interaction in her classes. Sven Beiker teaches Strategy Making in an MBA program at Stanford Business School, and also at a university in Sweden about AI and product development.  He discusses his experience teaching at Stanford and their passion for teaching. He began his teaching career at Stanford which led from a position as an automotive program manager. He also enjoys working with younger people, finding it intellectually stimulating. He has also found the position to be an asset in branding, and has found that it helps in terms of being considered as a keynote speaker from Stanford Business School.    Mohannad Gomaa shares his experience teaching at US Navy PostGraduate School, which was motivated by a contract with a colleague and his subject matter expertise. He designed and delivered the curriculum. He has also taught in consulting colleges, and recently, he was authorized by the Association of Supply Chain Management to teach supply chain certifications, including the CSCP certified supply chain professional certification. This allows him to associate with a reputable knowledge body and meet with stakeholders interested in his work. He has also signed an agreement to be a consulting partner for the ACM, which will allow him to explore more opportunities across industries. He believes teaching is a passion that can generate revenue beyond the passion. An adjunct professor at the University of Copenhagen shares her passion for teaching consulting and adds to her reputation for expertise in her field, but she finds teaching fun and energizing.    How to Secure a Position as an Adjunct Professor The conversation also touches on how to get started as an adjunct professor. To do this, one should be flexible about the institution they want to teach in and focus on the dimensions that are necessary to their field. Many schools have executive MBA programs and masters of leadership programs and other programs  that are growing and need teachers who can teach their specific subject matter area and create and pitch syllabuses. To reach out to the right people in these institutions, one should reach out to the Academic Director of different degree programs. This person will be responsible for the substantive side of these programs and can help with informational interviews. For example, if one wants to teach in New York City, one could reach out to HR or the dean of the school. Mary Kate discusses the benefits of adjunct teaching, including the joy of publications, networking, and credibility. She suggests starting as a guest speaker and gradually delivering classes, either shorter or elective, and eventually creating the curriculum. She also encourages reaching out to people teaching similar courses to your field to get started. She also mentions simply letting people know you are interested in teaching.  Sven mentions that many full-time professors don't like to teach, but they are constantly looking for someone to bring real-world experience into the classroom, to interact with a class, and bring their knowledge to the table. He states that, there are continuing education programs at universities, such as Stanford, that offer continuing education programs on both the professional side of education. These programs can help students gain experience and develop their interest in graduate programs and could be a first step into teaching. Networking is a key aspect of adjunct teaching, and can lead to a board position.  The Evaluation Process Revealed The panelists discussed the typical evaluation process for teaching positions, including the need for specific credentials or certificates, and how to express interest. Having someone internally who can vouch for you can make a difference. The first step in the evaluation process is to have a track record, such as a recording of a lecture, a written syllabus, and student evaluations. This ensures that when applying to another institution, they feel confident in their ability to teach a class.  Compensation for Teaching The compensation for teaching varies between $6,000 for a semester to 15,000, with a median of $10. The time commitment for creating a syllabus from scratch is around 200 hours. There may be additional benefits associated with teaching, such as subsidized healthcare benefits. The panelists discuss the range of compensation, which can range from $1,000 for a 90-minute class to $2,000 for a two-hour class and could for a 7, 12, or 14 week program.  The first time teaching, the teacher takes over the curriculum and develops it, however, they could be writing the entire curriculum, which can be a lot of responsibility but also an opportunity to shape the educational experience for students. It is worth noting that the course can also impact your consulting business, as committing to a class every week can limit your consulting business if you travel frequently. In contrast, in-person classes can be more effective due to scheduling. Another panelist, who is a Professor of Practice at Michigan State University's School of Business, states that the course is a salaried position, but it is not a full-time gig. The pay is based on a W-2 and a salary, which is a relatively small amount. The Benefits of Teaching The conversation revolves around the benefits of teaching and consulting, including inspiration, credibility, and carryover spillover benefits. Mary Kay shares her experience with getting clients and consulting project leads and converting leads into confirmed projects due to her credibility. Her students have become clients, and she concludes that the network is an enormous benefit.  Adam suggests that teaching should be synergistic with consulting work, and that it is synergistic to his writing work and that he has adapted the courses he teaches to corporate training. However, in this situation, it is advised to focus on the language of contracts to ensure that intellectual property rights are portable to a corporate context. Sven shares his experience with gaining project leads, which can be former students who become clients or organizations seeking advice from a professor who is also a consultant, and he has often been asked to be on the advisory board of startups by former students. This nurtures the network and gives the professor more standing and credibility. Clients often recognise the professor's expertise and reputation, making it a valuable asset. Best Practices for Networking Opportunities To maximize networking opportunities, Nick has found partnering opportunities with fellow professors. Mary Kate suggests connecting with other faculty members, attending university events, and partnering with fellow professors. She also shares her experiences of being wasted in the first semester of teaching and finding it difficult to find opportunities to meet with faculty members.  Developing a Curriculum in Academia The conversation turns to the complexity of developing a curriculum in academia. Developing a syllabus can be challenging, especially when it comes to creating evaluation materials and quizzes that can be objective and not lead to low grades. The tension between grades and evaluations can also be a challenge, but it becomes easier after the first time. The complexity of creating a syllabus depends on the type of class, for example, a seminar class at Stanford may require more discussion and bringing in guest lecturers. Another may require more content creation; a new class may require more detailed teaching material, including a reading list, quizzes, preparing exams etc.  Teaching As a Learning Experience Jared Lee, a faculty lecturer at McGill University and principal at Juniper, a Montreal-based consultancy, believes that teaching is a deeper way to learn and develop skills, as it requires a lot of preparation, the ability to defend theories against questions, and to be able to implement storytelling techniques. He believes that teaching 180 students who have detailed questions requires being bulletproof in preparation and how to apply the theories.   Jared also shares that this experience has built his ability in educating clients. Panelists also state that teaching has helped develop stronger public speaking skills, and the ability to manage a crowd. The discussion revolves around the challenges of teaching at universities like Stanford and the importance of facilitation in making discussions meaningful and meaningful. Access to Ancillary Benefits As an Adjunct Professor Additional ancillary benefits include access to datasets, academic journal articles, and other resources. Academic resources, such as the MSU library, are free and can be used in private practice. Academics can also leverage their academic connections to engage in conversations with people for various purposes, such as building lectures for their courses or collaborating on consulting projects. Health insurance is another asset. For example, at McGill, teaching three sections within a year can grant access to health insurance and supplemental pension and investment plans. The conversation ends with the participants discussing their takeaway from the discussion, including:  The importance of 200 hours of syllabus development The importance of fostering meaningful discussions and connections within academia for both students and faculty The importance of passion, preparation, and genuine effort in creating content for a class The need for preparation Staying updated on relevant topics and staying updated on the latest developments Credibility The panelists agreed that you should have good reasons for taking this position, and having a clear purpose for teaching can lead to better results. One additional tip was to be clear about why you are doing it and this will help you focus on how to achieve your goal. Another is to take advantage of a guest lecturer opportunity, and to be open to learning from your students. In conclusion, the panelists discussed the importance of passion, preparation, and genuine effort in creating content for a class. They also highlighted the importance of being proactive, asking questions, and embracing the unique experiences of students. By doing so, teachers can gain valuable insights and develop a deeper understanding of their field. Timestamps: 07:03 Consulting career paths and teaching experience 10:25 Adjunct teaching roles in economics 12:37 Finding teaching opportunities in higher education 15:06 Adjunct teaching opportunities and how to get started 17:24 Teaching at universities, networking, and evaluation processes 24:31 Teaching gigs, compensation, and time commitment 27:07 Teaching and consulting gigs for experts in customer experience management 31:22 Leveraging academic faculty status for consulting opportunities 34:48 Curriculum development and networking at a university 36:42 Teaching methods and challenges in higher education 39:58 Teaching and learning theories in consulting 42:48 Teaching strategies and access to academic resources 45:16 Academic benefits, networking, and health insurance 53:21 Teaching and consulting in academia   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.  

Show Notes: Russell Reynolds, founder of Russell Reynolds Associates and RSR Partners, shares his story of starting his own executive search firm in the 1960s. He served in the Air Force and later joined JP Morgan. After working there for six or seven years, he joined William Clark Associates. However, shortly after, he decided to start his own firm with his friend OB Clifford and a few other friends. They collected $50,000 and started Russell Reynolds Associates. He also decided to invite his friend Lee to join the firm as partner. The firm was established in 1969, and the partnership worked well. Today, Russell Reynolds Associates is one of the largest search firms in the world. As a big producer, Russell believes that success in a service business is about doing a good job and connecting with clients. He was introduced to the senior partner of Oppenheimer and company; they became great friends which eventually led to many more clients. Key Factors in Hiring Talent  Russell states that it is important to look for people who are well adjusted, positive, and excited about the future. He believes that integrity is the single most important ingredient for success, and if people are honest and try to do the best they can, they will prevail. He shares the key points he looks for in people, including whether they are givers or takers and the questions he asks candidates. When hiring for Russell Reynolds Associates, one of the key questions is whether the person has integrity or adapts to their style of client service. Russell asks for samples of their writing, because communication skills are so important, and he also asks about family relationships and what they do on weekends. He also emphasizes the importance of taking them off base to see how they really behave, and  allows him to see how well they are prepared and how they can be receptive to new ideas. Russell believes that bright young people are the key to success in a business because they are motivated, hungry, and want to please you.  Building the Board and Expanding the Firm Russell discusses the role of an external board of advisors, which included prominent business leaders from JP Morgan and Shell. He shares the firm's approach to governance, and how it  was run like a public corporation. He also discusses the institutions and practices set up to develop people. The firm grew through branch offices, and rules established by each branch, but there were certain rules that were set up across all branches, and he explains what they were and certain aspects which were encouraged such as involvement in charitable and political activities. Russell shares stories of when he was involved in fundraising for both charitable and political campaigns, including meeting then Prince Charles, and time spent raising funds for George H.W. Bush and Ronald Reagan. Success Factors of the Firm He talks about maintaining and building relationships and shares a few tips on maintaining positive client relationships and how his firm offered new ways of providing value to clients. The firm's search businesses are broken down into practice areas such as healthcare, financial services, wealth management, consumer, industry, board, and recruiting. He also talks about building a service firm and practice management.  In 1993, Russell sold his shares in RSR Associates and decided to start RSI Partners. The firm expanded into executive search, which is still going well today. He explains why he made this decision. He is now chairman emeritus, and although he is not directly involved, he is on the board. He shares why he sold RSR Associates and why he decided to come out of retirement to start a new company. The conversation turns to career mistakes and Russell recounts a story of being charmed and betrayed, why he believes physical fitness is important in the assessment of a candidate, why he’s leary of academic achievers, and what he considers valuable assets.  Professional Career Advice Russell advises young college graduates to focus on developing their skills and investing in them. He suggests attending seminars, conferences, and listening to podcasts to learn new skills. He emphasizes the importance of having a balanced life, including vacations, family, and relationships. He also suggests being on outside boards, both charitable and for profit, for educational and helpful experiences. For those building a professional services firm, Russell suggests not taking no for an answer, not to be limited by one's imagination, and the importance of being grateful, humble, respectful, and recognizing that they are not the most important person in the world. He emphasizes staying in good health physically and mentally. However, he also recognizes that the advice depends on the individual's interests and goals. Timestamps: 05:37 Leadership, client service, and hiring practices in professional services 16:01 Leadership, governance, and talent development in a consulting firm 24:42 Political connections and relationship-building in the recruitment industry 31:43 Career development, business growth, and leadership lessons 40:46 Career development, leadership, and success   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Show Notes: In this episode of Unleashed, Paul Gaspar discusses his experience working with artificial intelligence at a major global insurance conglomerate in Japan. The company faced pressure to streamline operations and reduce costs within its auto business. Paul, who was in a role leading the data science function, suspected that the claims area in insurance was a target-rich environment for delivering value with advanced analytics and technology. He found that similar processes were being utilized on claims regardless of the size, leading to the opportunity to put analytical rigor behind the claims estimation process. AI Use for Processing Insurance Claims Paul and his team looked at information flows at various points in the process, specifically evaluating how information collected at the time of the accident could be used to provide insight on losses. Using this information, they built predictive models using AI techniques that would allow them to predict the ultimate value of these claims from a $1 perspective, using a subset of the initial information collected at the time of loss. By building models that could do this quickly and accurately, they were able to set thresholds that would allow for automated processing and payment of claims amounts on about a quarter of the total claims volume. This reduced the workload for the team handling claims and sped responsiveness to customers with smaller claim amounts. The Process of Assessing Information Paul explains the process of assessing the quality, consistency, and reliability of information for a client. This involves assessing the types of information, blending them with data analysts experienced with using different modeling techniques and programming languages. Paul and his team used Python to investigate particular approaches, and testing results to identify useful data elements for creating meaningful insights. This process is not necessarily feasible for a data analyst with minimal data science knowledge. Instead, a step-by-step approach involves evaluating the data, considering viable modeling techniques, and experimenting with them to ensure accuracy, speed, and processing power. A team of experienced data scientists can help guide the technical approach and modeling techniques used in the case. This approach is essential for evaluating claims and determining the appropriateness of claims based on the available data. To ensure precision across various claim types, it is crucial to segment claims by value and look at the ones with the lowest value. This helps identify potential risks and minimizes leakage, which is the risk of overpaying for claims relative to processing costs. Predictive analytics is a complex art and science, and it is essential to be careful about how and where to use it, ensuring that risks are well understood and balanced against the benefits of the process.To turn a scalable business process into a working scalable business process, Paul states that change management work must be done across various functional areas. This includes ensuring that information is passed into payment systems, how automation impacts existing processes, and how to contact customers and inform them of potential benefits. Building AI Algorithms to Prevent Human Errors In the claims process, Paul states that human errors can be a significant issue, as they can lead to false positives and false negatives. To prevent human errors, AI algorithms should be trained to match human judgments and set error tolerance thresholds. This is a time-consuming part of the process, and it is essential to work with claim handling professionals to assess the performance of the models and identify errors. He also mentions that risk management is crucial in ensuring that systems make accurate decisions and avoid making mistakes. Machine learning operations (ML ops) have emerged as a concept that accounts for model performance over time, and it is crucial to continually monitor and adjust models as needed. To ensure that the model does not become overly sympathetic to human errors, it is essential to conduct testing and monitoring over time. Companies that excel in this field have developed software programs that allow for systematic monitoring of decisions. By setting thresholds and balancing processing time and error, companies can set acceptable thresholds and auto-process claims at risk-acceptable levels. The Evolution of Predictive AI Paul discusses the evolution of predictive AI, specifically generative AI, which uses existing knowledge bases and training models to generate content that is most likely to be related to an end user's query. This is the basis of foundational models used by open AI and Perplexity to create a new paradigm and use case for predictive AI. The accessibility, power, and intuitive nature of these models make them exciting for experimentation. Generative AI tools have become multimodal, allowing them to take textual, voice, image, or video inputs and respond to queries about that type of content. This allows for an incredible range of possibilities, even in the mobile first world. For example, in the case of auto claims, the estimation process could change from a low value subset to a higher value and sophistication of claims.   The multimodal input, the ease of interaction with providing information to these tools, and the ability to access from both practitioner and end user perspectives are key game changers in the future of predictive AI. Paul emphasizes the importance of change management in implementing AI tools in corporations. Timestamps: 01:04 Implementing AI in claims handling at an insurance company 08:34 Using predictive analytics in claims processing 13:41 AI-powered claims processing and error management 18:25 Generative AI's transformative potential in various industries   Links: LinkedIn: https://www.linkedin.com/in/paulmgaspar/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Show Notes: In this episode of Unleashed, Astrid Malval-Beharry discusses an AI case study with a top 50 homeowners insurance carrier in the US. Astrid was approached by their underwriting and innovation teams to digitally transform their underwriting workflow.  Astrid shares an overview of the industry at present. The industry is facing challenges due to an increase in natural catastrophes, inflation, disruptions in the supply chains, and policyholders who prefer to have an Amazon or Uber experience with their insurance carrier. The client had three goals for the digital transformation project: increasing the level of straight-through processes, improving risk assessment, and realizing greater investment in inspection. Astrid explains what  straight-through processing is and how it works using data analytics and AI-based and technology solutions.  The second goal was to improve risk assessment by analyzing the location of the property, the condition of the property, and the policyholders themselves. The client wanted to know how AI solutions could help enhance risk assessment, reduce premium leakage, and charge the right price for coverage. The third goal was to improve the inspection process, which currently costs carriers a lot of money but only yields a few actionable insights. To achieve this, Astrid’s team shadowed underwriters across both regions and senior IDI to understand how consistently underwriting guidelines are being applied. The team also interviewed and benchmarked against competing carriers, InsurTech carriers, and carriers that look at the underwriting workflow with a different lens. This allowed them to see the art of the possible and make informed decisions about their underwriting practices without disrupting the workflow. Employing AI Solutions for Insurance Companies Astrid talks about what follows the research and benchmarking exercise and how they mapped the workflow and the ideal future state.  Premium leakage occurs when insurance companies charge less for a policy than the actual premium should be to reduce losses and charge the right price for the coverage. The inspection process is often done by agents or license inspectors, leading to a lack of actionable insights. To address this issue, a preferred digital transformation engagement was conducted by shadowing underwriters across both regions and senior IDI. This allowed the team to understand the consistency of underwriting guidelines and the impact of different levels of underwriters on the process. Competitive intelligence benchmarking was conducted against carriers with similar profiles and InsurTech carriers. This allowed the team to map the workflow as the ideal future state from an underwriting workflow perspective. However, the change should not be too abrupt, as the procurement process in the insurance industry is notoriously long.   A middle ground was identified by analyzing claims activities on the book of business NIS to identify the biggest losses and how implementing AI solutions would give the highest return on investment. Change management is also important, as it involves both technology and people and processes. The organization's readiness to implement new digital tech-driven solutions is also crucial. Astrid also touches on the convergence of people and processes when implementing technological solutions in change management. Questions to Ask an AI Vendor Astrid shares a list of questions to ask an AI vendor, including accuracy, model explainability, model bias and fairness, and scalability. She has experience working with insurance carriers, analytics, technology vendors, and private equity firms, giving her a deep understanding of what solutions work and don't work. When selecting an AI vendor, it is important to understand a series of fundamentals about the solution. The first question is about the accuracy and performance of the AI model. It's crucial to understand how the vendor measures accuracy and how they handle situations where the model may not perform as expected.  The second question is about model explainability, which is crucial in the highly regulated insurance industry.  The third question is about model bias and fairness, and how the vendor addresses and mitigates biases in their AI models. The fourth question is about scalability. While some solutions are considered vaporware, and Astrid explains what vaporware is, there are legitimate, enterprise-grade solutions that have legitimate AI technology. By asking these questions, clients can better engage with the right AI vendor and ensure the right decision-making process. She states that licensing data from a vendor is the right path due to the ongoing maintenance required. AI vendors are now incorporating large language models, such as chat GPT, into their AI models. However, this is not the core competency of an insurance carrier, which is to assess risk.   Astrid stresses that results should not be expected too quickly. However, she does mention that they are already seeing results. She mentions a project that has been 16 months in development, and it is not expected that a solution will immediately bring new business or reduce expenses. However, the results have been significant, with a client seeing a 75% increase in straight-through processing and reduced manual injury interventions. Operational efficiency has also soared, and better risk assessment has been achieved. Timestamps: 01:02 Digitally transforming underwriting workflow for a top 50 US homeowners insurance carrier 03:08 AI solutions for insurance industry digital transformation 07:14 AI implementation in insurance industry 13:42 AI model accuracy, explainability, bias, and scalability in insurance industry 17:54 Evaluating AI vendors for insurance industry use cases Links: Website: https://www.stratmaven.com/ LinkedIn: https://www.linkedin.com/in/astridmb/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.  

Show Notes: Julie Noonan shares a case study on using AI while working with a top 15 global pharma company to get the most insight from the data and reduce time to market or time to development of their particular molecules and drugs.  In early 2022, the pharma company was using artificial intelligence and machine learning to analyze clinical and research data. The organization Julie worked with was a digital and data concentration alongside data scientists and computer scientists. Julie shares where this organization placed focus and what their goal was with regards to using AI and machine learning(ML), and the role she played in developing this center of excellence.    Company Use Cases of AI and ML Most of the early use cases involved clinical data and research data. Clinical groups were conducting the first clinical trials with animal populations, and recording their data in various tools. They were studying a specific model molecule to understand its implications across projects. For example, they were studying a molecule for one disease indication and wanted to predict its relevance for another project that another team was working on. AI and machine learning prompts were used against the data, allowing them to organize and prompt data to return potential other indications that could be tested with the collected data. Julie talks about how companies are grappling with the rapidly evolving AI technologies, and a center of excellence can be a solution. However, concerns may arise about adding bureaucracy and slowing down innovation. She explains how she helped her client deal with these concerns. The company culture of this global organization highly values entrepreneurialism, and allows data ownership within its group, allowing for experimentation unless it directly impacts patients. She mentions that they were able to educate interested groups about the importance of patient safety and ethics. The organization rewards innovation by publicly recognizing those who come forward with project ideas. Even if the project is not great or a failure, it is a lesson learned. The company's top priority is the patient, and they reward those who come forward with ideas without imposing penalties or shutting down projects. The organization also stresses the need to comply with correct procedures to avoid ethics violations.  Inspiring a Company Culture of AI and ML Innovation  Julie talks about how her role in change management helped inspire innovation within the company.  They used polls to encourage innovation and encourage change. They run exciting advertising, competitions, and partnerships with universities, allowing for the introduction and excitement of new AI technologies. This approach helps companies navigate the challenges of AI adoption and ensures that their innovation is not stifled by bureaucracy. Julie explains that for change to be successful, leader support plays a key role. The center of excellence (COA) is a key change management initiative within an organization. It involves making people aware of AI and machine learning, which can be achieved through various marketing strategies. The organization chose a name that aligns with its culture and annual message from the CEO, highlighting the future and benefits of AI and machine learning in drug delivery. The COA also held pop-up events where individuals could access learning materials, certifications, and practice using fake data. Office hours were provided for those who had no idea about IT architecture or how the organization operated. Newsletter articles, posted posts, and video monitors were used to promote the COA's existence. A community of practice was formed, which met monthly for educational sessions and discussions on AI usage. Julie also explains how they monitored ethics and DEI to represent the target patient population. Measuring the Efficacy of the COA Measuring the effectiveness of the COA is challenging due to the lack of metrics. Julie talks about measuring awareness, and how the organization has grown from six members to a global community of over 1500 people. She also mentions accessing use of the learning, accessing use of the sandbox, and the number of projects brought into be evaluated,  focusing on their metrics. For example, in the first year, 10 projects were part of a competition with a local university, where teams of university and company employees worked together to implement AI/ML elements in their projects. The project metrics included surprises, opportunities, and lessons learned. This success was significant in the pharmaceutical industry, as more drugs and experiments fail than succeed. Over the last two years, the number of data scientists has grown dramatically, and the COA has become a vital tool for the organization's digital transformation efforts. Timestamps: AI use cases in pharma company 06:33 Balancing innovation and governance in a large organization 11:29 Marketing a new AI center of excellence internally 15:47 AI and ML center's effectiveness measured through awareness, access, and project metrics. Links: Website: www.jnoonanconsulting.com LinkedIn: https://www.linkedin.com/in/jnoonanconsulting/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Show Notes: Markus Starke, an advisor for cybersecurity and digital process transformation, has recently been working in cybersecurity for the AI applications that corporations are using. Marcus explains that, AI plays a significant role in work, particularly in intelligent process automation. This concept involves combining technologies like robotic process automation, process mining solutions, chatbots, Optical Character Recognition, and more advanced forms of machine learning and generative AI to build end-to-end processes. However, cybersecurity issues can affect these automation systems, especially as more users use them individually. Safety Measures with AI Automation Markus talks about several dimensions of cybersecurity with AI automation. To ensure the safety of AI-related automation situations, clients are asked to review their setup from a Target Operating Model perspective. A framework is created to guide this process, including governance, secure development processes, and creating awareness about potential risks. Governance involves governing roles and responsibilities, access, user rights, and other aspects of the system. Secure development processes ensure that solutions only access the data they should access, store data securely, and use encryption. Securing the platform is another dimension, involving standard frameworks for cloud-based solutions. Awareness about the human factors in reducing risk levels is crucial for achieving good cybersecurity. And lastly, monitoring and reporting ensure that the environment is controlled to a degree. Examples of Cybersecurity Threats Using AI Tools Markus discusses cybersecurity threats with AI tools, such as generative AI (GPT) for working on company data. One example is a human user extracting data from their corporate data pool and sending out an email with this data, and sending it to their private email account, which could be used in a public chat GPT instance. This can be controlled by creating awareness and setting up standardized IT security control mechanisms to limit data extraction from corporate networks. Another example is using proprietary corporate data for advanced data analytics on GPT, which could expose it to a potential attacker. Private computers are typically less secure than corporate ones, making them more prone to being attacked or losing data to an attacker. Corporations generally want to limit the type of data that is made publicly available in generative AI applications. He states that it is not always clear what happens to the data that is input to AI applications.  Markus also discusses the risks associated with using consumer versions of chat GPT, as any data uploaded could potentially be put into its training data. However, there are options for setting up AI applications in a limited way for specific corporate use cases, but it is important to evaluate these solutions on a case-by-case basis to ensure they fulfill specific needs and governance. With Gen AI, it is crucial to balance between limiting too much while maintaining control. AI Tools Retaining Data The discussion revolves around the use of AI tools, such as Zoom, which may be retaining data on calls or transcribing them without letting users know. This raises concerns about the accessibility of information to organizations. It is essential to ensure that these tools align with cybersecurity standards and are compliant with protection requirements. However, this may be a case-by-case consideration, and Markus emphasizes that it is always necessary to question security processes. In addition, he mentions that it is crucial for independent consultants to raise awareness about cybersecurity and AI. Basic rules apply to the use of AI, such as ensuring data is stored in controlled instances and using strong protection mechanisms like passwords, access rights, and encryption. When working with clients, it is important not to make their lives too simple by creating AI solutions for specific business problems. Cybersecurity can sometimes be perceived as slowing down businesses, but it is an essential control that must be maintained. Independent consultants should review these aspects and not make their work too easy. Markus strongly recommends that consultants should be aware of active and forthcoming regulations that apply to AI when setting up solutions for clients.  Timestamps: 0:03 Cybersecurity risks in AI-powered process automation 03:10 Governance and security for AI-related automation 05:53 Cybersecurity risks with AI tools and data 10:48 AI data security and control 14:47 Cybersecurity and AI in business   Links: Freelance Website: http://starkeconsulting.net/ Company Website: https://www.ten-4.de/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.  

Show Notes: Cheryl Lim Tan discusses her experience working with a financial wellness product powered by AI. The client was early in their journey and needed to raise awareness of their product. They needed to refine their product further and gain more users to gain feedback and make adjustments to its features. Cheryl was brought in to take care of the entire marketing function.  Cheryl's approach involved figuring out the company's brand, target audience, and value proposition. She also focused on articulating the unique value proposition of the product compared to free tools like Chat GPT. By addressing these aspects, the consultant was able to create a clear framework for the client's marketing function and reach investors. Prompting AI Tools Cheryl highlights the importance of education in the AI world, as AI tools are prompt-driven and consumers may not know how to interact with the interface and how to prompt it. To address this, they developed a suite of YouTube videos on how to prompt the tool for different situations or information. Another key aspect of targeting the client was developing personas. These personas were identified and distilled into a framework that included the top three messages, pain points, and expectations for each persona's customer journey. Consumer Education and AI Tools Cheryl emphasizes the importance of consumer education in the AI world, as it helps to draw the right audience in and ensures the success of the product.She also shares consumer insights about the types of users who are open to using AI tools, such as Gen Z, who are digital natives and more likely to adopt AI in their everyday lives. The proliferation of AI in 2023 has helped AI companies get in front of their target audience and engage with them. Gen Z is likely to be one of the highest adopters of AI, while millennials and Gen X are more cautious and hesitant. To ensure AI adoption applies to their market, companies must be clear about their personas and target audience, and consider using colors and layouts that appeal to the everyday consumer rather than catering to programmers. SEO and AI In terms of SEO, search engine optimization, and paid search, Cheryl highlights the importance of being conscious about who they are trying to reach and how to present their brand accordingly. She also discusses the challenges faced by early AI startups in figuring out who they are targeting and how to signal their preferences. She shares their marketing mix, which includes SEO, content marketing, working with influencers, an affiliate program, email marketing, and discord communities. They found that email marketing still works and was a great way for them to pick up new users. They also mention brokers for finding AI email lists that are a good fit for their brand and audience. The Benefits of a Discord Community Cheryl talks about the importance of having a dedicated Discord community related to your product to gather information, which is valuable for marketing and product refinement. She explains how Discord can be used, and how she has used it in marketing.  She emphasizes the need for authenticity in inserting oneself into conversations and promoting the product. Reddit, she believes, is taking over Google in terms of cost for acquisition, with a cost per click down to $1 compared to Google's $4-6. Reddit also allows for targeted placement in relevant conversations, making it more cost-effective than Google. Timestamps: 00:03 AI-powered financial wellness product and marketing strategy 04:00 AI marketing strategies for consumer education 07:45 Targeting audiences for AI technology 11:13 Digital marketing strategies for a startup 14:14 Marketing an AI product using Reddit and Discord Links: Website: https://www.cheryltanconsulting.com/ LinkedIn: https://www.linkedin.com/in/cherylltan   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Show Notes: Barry Saunders, a digital expert at McKinsey, discusses his background in the firm and his experience in AI-related projects. He worked in the LEAP practice, which built platforms for video streaming, preventative maintenance, and optimization tools. He left McKinsey to become Chief Product Officer at an Australian fashion company and recently joined MXA, a strategic digital technology company in Australia. Barry suggests a two-by-two typology classification scheme for AI-related projects. The first quadrant focuses on understanding patterns of behavior, while the second quadrant focuses on predictive behavioral modeling, third is more about text orientated and understanding meaning. The fourth quadrant focuses on regenerative AI and content creation. Barry believes that combining these quadrants can lead to personalized content for different customers and valuable insights and can unlock interesting value.  AI Use Case Study Barry and his partner have been working on an AI toolkit to automate time-consuming work for management consultants. They developed a startup called First Things, which uses Gen AI to create classic McKinsey storylines from unstructured data. This tool has helped executives work through their strategies and report outcomes. They have also worked with clients on the AI journey, especially regulated industries. They have found that some tasks can be done more effectively with AI. One project they did was analyzing insurance policies for large-scale agricultural businesses, which are often complex and drift in meaning as language is updated. They created a tool that would analyze these policies, extract semantic meaning, and identify where drift took place, allowing them to align documents and simplify policies. One of the projects they are currently working on is simplifying lending policies for banks. In Australia, many lenders do home lending as their primary base, but the technical platforms used by banks and non-bank lenders are ancient and difficult to navigate. They are working on simplifying policies and offering home loans more simply. Building AI Tools The level of effort required to build a tool like this is not limited to building it. Many of the tools available are free, and there are many software as a service tools available that can perform similar tasks. To build a tool like this, one should be clear on what they are trying to do, such as simplifying a policy or comparing two different policies. The AI toolkit has proven to be effective in automating time-consuming work for management consultants and other clients. It is essential to be familiar with the tools and their capabilities to effectively utilize AI in various aspects of business operations. The legal space offers a vast array of tools for generating and analyzing contracts, including software as a service tools. To use these tools effectively, it is essential to be familiar with the large language model and the tool being used. Tuning these tools to get the desired response requires understanding the chain of logic and the outputs. To build a production-oriented tool, consider using large language model operations (LLM ops) or large language model operations (LLM ops) in a broader software architecture or workflow. Google, AWS, and Microsoft offer guides on how to integrate these tools into their software stack. It is crucial to be clear about the tasks and outputs of these tools, and to work with teams who are familiar with these systems.  Using AI Applications Barry discusses his work on AI applications, specifically RF cues and analyzing large documents. He built a proof of concept using a tool called mem.ai. He talks about a template he built to analyze questions in RFQs, which are often templated and consistent across government agencies. The system is particularly useful for handling open-ended questions and generating text about your company's services, processes, etc. This speeds the process of applications, and the system can be used to set the tone for the next step in a project. Timestamps: 00:03 AI projects and experience at McKinsey with Barry Saunders 01:57 Using AI to analyze text data and create personalized content 05:23 Simplifying complex insurance policies using AI 09:06 Building a tool for analyzing and comparing legal documents 12:31 Using AI to automate RFQ response generation Links: Whitepapers: https://www.mxa.com.au/whitepapers Company Website: https://mxa.com.au/ LinkedIn: https://www.linkedin.com/in/barrysaunders/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

Show Notes: In this episode of Unleashed, Phil Bellaria shares a case example of building a Chat GPT using open-source large language models. The client was a large telecommunications company with an immense amount of unstructured data, including customer feedback, feedback from employees through surveys, and transcript transcripts from millions of phone conversations and text chats. The problem statement was to derive insights and understand the state of the business, identify trends and topics as quickly as possible. The process took place through 2018-2020. Working with a data scientist, and using Google's BERT methodology for natural language processing, the team coded an algorithm that identified topics and classifiers from the unstructured data, scored each topic and phrase on sentiment (positive or negative comments) and created a short summary of customer or employee comments related to each topic. The process of building and running the model was processing intensive, and the first step was testing and iterating the model on smaller samples of data. The company held employee surveys, which was processed through the test model, the data was reviewed by HR business partners and business leaders to check for accuracy. The model was trained on all the information in Wikipedia, but other specific information and words were added to refine it. Over six to eight months, the model was able to accurately represent what employees were saying.  Using AI to Improve Sales Pitches Phil discusses the use of AI in business applications and how it can be used to improve sales pitches. He explains that the problem was to understand why sales agents were not pitching a strategic product effectively. By feeding data from conversations with customers about the product, the algorithm was able to identify words and phrases associated with successful sales and non-successful sales. This information was then used to train sales agents on the right expressions and words to use when pitching the product.  Phil shares some phrases that work well and those that don't, such as promoting a streaming product by associating it with popular shows. He also discusses the challenges of building AI models and securing and protecting data. He also addresses the cost of building an AI model. Using AI for Next Best Customer Actions Phil shares one example of AI-related projects which used AI algorithms to predetermine the next best action for a customer that can be used in real time to learn the best approach in customer interaction. The AI engine uses reinforcement learning to improve the power of the recommendations. The process involved building the right APIs into existing systems and ensuring SLAs in terms of responsiveness. The algorithm itself uses sophisticated statistical modeling techniques, but the main challenge was integration and timeliness. Challenges Implementing AI Phil talks about the challenges of implementing this process. He emphasizes the importance of defining the business problem and getting the technical team involved early in the process. He talks about time spent translating the problem into technical applications, allowing technical personnel to use their skills to solve the problem.  He also shares a timeline for starting a recommendation algorithm. The process includes writing, pulling in data, creating a data environment, scoring, and algorithms. Another consideration is change management which involves limited pilots and controlled AB tests across the population, and time allotted to roll out and testing.  Phil discusses the power of AI in data analysis, stating that it can provide insights and interactions that are not always available before. The real power lies in bringing new agents to speed up the process and elevating the performance of middle-tier agents. The lower performing agents often wouldn't use the tool, so they don't see as much impact.  Timestamps: 00:02 Using AI to analyze unstructured data for business insights 03:23 Using AI to analyze customer feedback and sales data 08:14 AI-powered next best action engine for sales 12:16 Implementing AI-powered customer service tool 16:43 Using data and analytics in call centers Links: Company website: https://www.cdaopartners.com/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.