557 episodes

Unleashed explores how to thrive as an independent professional.

Unleashed - How to Thrive as an Independent Professional Will Bachman

    • Business
    • 4.9 • 70 Ratings

Unleashed explores how to thrive as an independent professional.

    559. Paul Gaspar: AI Project Case Study

    559. Paul Gaspar: AI Project Case Study

    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 monit

    • 19 min
    558. Astrid Malval-Beharry: AI Project Case Study

    558. Astrid Malval-Beharry: AI Project Case Study

    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 an

    • 21 min
    557. Julie Noonan: AI Project Case Study

    557. Julie Noonan: AI Project Case Study

    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.

    • 18 min
    556. Markus Starke: AI Project Case Study

    556. Markus Starke: AI Project Case Study

    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 sol

    • 18 min
    555. Cheryl Lim Tan: AI Project Case Study

    555. Cheryl Lim Tan: AI Project Case Study

    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,

    • 16 min
    554. Barry Saunders: AI Project Case Study

    554. Barry Saunders: AI Project Case Study

    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 appl

    • 17 min

Customer Reviews

4.9 out of 5
70 Ratings

70 Ratings

pujitococogorda ,

Great tips and consulting resources

These podcasts have helped me grow my consulting practice

gunstreetgrrl ,

This podcast is a STANDARD.

I have been following Will’s podcast for several years now, and I am constantly amazed at the volume of useful information I get from him. I’m a marketing professional working in the tech sector, so maybe you’d think I wouldn’t get a ton out of a consulting podcast. Wrong. Almost every episode has me taking copious notes. How to approach solving business problems, how to navigate Excel/ChatGPT, why you should read Shakespeare to expand as a human being… Where else can you get this? Unleashed has been a training camp for curiosity, growth and humility. Helps me do better, be better. Much gratitude.

🐹onemouse ,

Very helpful!

This podcast is so helpful in understanding business consulting as a newbie! Great examples that help illustrate the different topics. Thank you!

Top Podcasts In Business

NerdWallet Personal Finance
Ramsey Network
Money News Network
Boston Consulting Group BCG
Dan Fleyshman
iHeartPodcasts

You Might Also Like

Consulting Success
Andreessen Horowitz
Harvard Business Review
McKinsey & Company
Kison Patel
Harvard Business Review