The Sentia AI Community

Category: Training

  • Things I learned from Bill and Not For Profit Investing

    Things I learned from Bill and Not For Profit Investing

    10 Things I Learned from Bill Walton: The Not-for-Profit Investor and Board Member Who was much More Than Just a Tall Tale

    As the former CEO of Fund Our Community, I had the unique pleasure of working with a legend, both in stature and in spirit—Bill Walton. He was a man who did it all: from scoring baskets and winning NCAA and NBA Championships to scoring big for charities like Wounded Warriors.

    The first time I met Bill was at a Wounded Warriors event at Cavallo Point Lodge in Marin California. He was presented with the highest “normal” bicycle I had ever seen and went out of his way to thank the builder of the bike, the company and the people who designed and built it for him. He didn’t need to do that, but that was the kind of good guy he was.

    Bill had serious charisma, a great smile and an infectious quest for knowledge as well as giving it back. Yes, he was a physical giant. But I personally believe he was under appreciated for all of the charity work he did, the not for profits he supported and the investments he made in so many communities. That is how I will remember him. He wanted to make a difference and he did.   

    So, without further acclaim (and please pardon the Archer like puns), here are the top 10 slam-dunk lessons I learned from the man himself:

    1. Height Isn’t Everything, But It Sure Helps When You’re Raising the Bar – Bill taught us that setting high goals is like going for that high rebound; it might seem out of reach at first, but with enough jump (effort / ambition), you’ll grab it with hard work and perseverance.

    2. The Best Defense Is a Good Offense – In the startup world, like on the court, you’ve got to be proactive. Bill’s strategy? Always be on the offensive, whether it’s pushing for innovation or pushing for a cause. Don’t wait for something to happen. Make it happen.

    3. Passing Is Not Just a Play, It’s a Philosophy – Sharing success, like a good basketball pass, creates opportunities for others to score. Bill was all about the assist, how can I help my teammates succeed?

    4. You Can’t Score If You Don’t Shoot – Bill’s investment in Fund Our Community (our startup) was a shot worth taking. He was a firm believer that you miss 100% of the shots you don’t take, and in business, that means every opportunity is worth evaluating.

    5. Rebounds Are Just Opportunities in Disguise – Missed a shot? No problem. Every setback is a chance to grab the ball back, gather yourself and go for another shot. No business succeeds 100% of the time. It’s what you do after the “ball” drops that differentiates the winners.

    6. Footwork Matters – Whether it’s pivoting in the paint or pivoting your startups business model, nimble footwork can save the day. Bills history of foot surgeries notwithstanding, he was a big believer in changing things up if it wasn’t working.

    7. Charity Work Is the Real MVP – Bill’s dedication to causes like Wounded Warriors showed us that the most valuable plays are made off the court, making a difference in the lives of others. Every time I spoke with him, it was tell me where you’re at, what you need and what I can do to help. He was focused on giving back and applied his resources accordingly.

    8. Keep Your Eye on the Ball, But Also on the Horizon – Vision is everything. You need to see the whole court to develop the best strategy. Keeping your eyes open to where your NFP business translates into affecting the bigger picture both in business and philanthropy.

    9. A Good Teammate Lifts You Up—Literally – Standing next to Bill, I felt taller and I know the people around him did as well. His support elevated everyone around him, proving that a good team member is a force multiplier.

    10. The Game Never Ends – Just like his many stories, his passion for life and charity work reminds us that there’s always another quarter to play, another mountain to climb, and another community to uplift.

    So, there you have it. A Few lessons I learned from a man who was as comfortable in the boardroom as was amazing on the boards. Thanks, Bill, for being the ultimate teammate and for showing us that the game of giving back is the most rewarding one to play.

    You will be remembered!

    Tags: #NFP #Notforprofit #charity #Woundedwarriorproject #dogood #giveback #lifelessons #NBA #NCAA #BillWalton #business #success #successtips #life

  • How to Assess if you Need an AI Risk Audit

    How to Assess if you Need an AI Risk Audit

    AI is amazing and if you have worked with it you know what the future holds. However as the Amazing Spiderman’s uncle said “With Great Power, Comes Great Responsibility.” Truer words were never spoken when it comes to AI! Just last week the

    Head of Open AI Sam Altman dissolved the group he setup just one year ago to protect us from the risks of AI. So, who is in charge of guarding the proverbial Open AI hen house when the boss doesn’t seem to care about anything but profits?

    You should. If you’re in the C-Suite or management you have already seen the power of AI. Some of the risks are quite apparent and obvious. Many of the risks are unknowable but many more have been identified and AI Risk Management best practices are becoming a “thing.”  So what is an AI Risk Mitigation audit? What do they do? How do I know if I need one?

    Let’s go back to the future as Michael J Fox would say! An AI Risk Audit is a comprehensive assessment of the risks associated with the deployment and use of artificial intelligence (AI) systems within an organization. It aims to identify potential vulnerabilities, ethical concerns, and operational risks related to AI technologies. Let’s delve into the details:

    An AI Risk Audit involves evaluating the entire AI lifecycle, from development to deployment, to ensure that your AI systems are trustworthy, secure, and aligned with organizational goals.

    Here are the key components of an AI Risk Audit:

    1. Risk Assessment:
      • Identify and assess risks specific to AI technologies, including biases, privacy violations, security breaches, and unintended consequences.
      • Evaluate the impact of AI on stakeholders, such as customers, employees, and the broader society.
    2. Governance and Policies:
      • Review existing policies and governance frameworks related to AI.
      • Develop or enhance policies that address AI risks, ethical considerations, and compliance requirements.
      • Prepare human resource guidelines for AI use by employees and get written sign off they understand the policies.
    3. Data Quality and Bias:
      • Analyze training data for biases and fairness issues. (Especially in HR)
      • Ensure that data used for AI model training is accurate, representative, and free from discriminatory patterns.
      • Where is the data going? Where is it coming from? How are you protecting your data from intrusive AI?
    4. Model Validation and Testing:
      • Validate AI models using robust testing methodologies.
      • Assess model performance, interpretability, and robustness.
    5. Explainability and Transparency:
      • Evaluate the transparency of AI decision-making processes.
      • Implement techniques to make AI models more interpretable.
    6. Security and Privacy:
      • Assess the security of AI systems, including protection against adversarial attacks.
      • Address privacy concerns related to data handling and model outputs.
      • Review employee use of data, what software they are authorized to use and what they are using that is not authorized – potentially harmful.
    7. Complete Scorecard of AI Risk
      • Rank all AI’s being authorized for risk.
      • Rank all AI’s being used but not authorized for risk.
      • Current state AI Risk scorecard and score after applying risk mitigation strategies.
    8. Ethical Considerations:
      • Examine the ethical implications of AI, such as fairness, accountability, and transparency.
      • Ensure that AI aligns with organizational values and societal norms.

    The majority of companies have not completed an AI risk assessment and many of the corporate lawyers are trying to figure out what is “going to be legal” vs. what won’t. Since there is so little precedent and many of the laws are being written, how do you know if you should complete an AI audit?

    Five ways to assess if your organization should consider an AI audit:

    1. Complexity of AI Systems:
      • If your business relies on complex AI models, such as deep learning neural networks, natural language processing (NLP), or reinforcement learning, an audit is essential.
      • Complex models are more prone to biases, errors, and unintended consequences. An audit helps identify and mitigate these risks.
    2. Data Sensitivity and Impact:
      • Consider the sensitivity of the data used by your AI systems. If they handle personally identifiable information (PII), financial data, or health records, an audit is critical. Government compliance is coming…
      • Assess the potential impact of AI decisions on individuals or society. High-impact decisions warrant thorough scrutiny. Ethics are important.
    3. Regulatory Compliance:
      • States are putting their own laws in place before the fed does. Until then there will be a patchwork of AI laws (like in Colorado last month) that are going to be easy to trip over.
      • Stay informed about AI-related regulations in your industry and region. Many sectors have specific requirements for AI transparency, fairness, and accountability.
      • If your business operates in a regulated environment, an audit ensures compliance.
    4. Ethical and Social Implications:
      • Evaluate the ethical implications of your AI systems. Are they aligned with your organization’s values and societal norms?
      • Consider the impact on fairness, privacy, and human rights. An audit helps address these concerns.
    5. Business Goals and Risk Tolerance:
      • Align AI audit decisions with your business goals. If AI plays a critical role in achieving those goals, an audit is necessary.
      • Assess your risk tolerance. If your organization cannot afford AI failures (financially, legally, or reputation-wise), prioritize an audit.

    Five Legal Reasons an AI Risk Audit maybe Needed:

    1. Mitigate Legal and Reputational Risks:
      • Organizations face legal consequences if AI systems violate privacy laws or discriminate against certain groups.
      • A thorough audit helps prevent legal disputes and protects the organization’s reputation.
    2. Avoid Bias and Discrimination:
      • Human resources must monitor and ensure their AI is not discriminating in hiring or firing. The consequences of failure will be legally expensive.
      • Biased AI models can perpetuate discrimination and harm marginalized communities.
      • An audit identifies bias and allows corrective actions to be taken.
    3. Enhance Trust and Stakeholder Confidence:
      • Transparent and accountable AI systems build trust among customers, employees, and investors.
      • An audit demonstrates commitment to responsible AI practices. Employees see it as well as customers
    4. Optimize Performance and Efficiency:
      • Identifying risks early allows for timely adjustments, improving AI system performance. This needs to be ongoing. It isn’t set it and forget it.
      • Efficient AI systems lead to better business outcomes.
    5. Stay Ahead of Regulatory Changes:
      • Governments are increasingly regulating AI technologies.
      • Europe is first up with AI Laws, US States second and Fed likely last.
      • An audit ensures compliance with evolving regulations.
      • Piece of mind for C-Suite – An AI Risk Audit allows a good sleep with fewer Terminators popping up in bad places.

    Remember that AI Risk Audits are not one-time events; they should be conducted periodically to adapt to changing risks and advancements in AI technology.

    Evaluating whether your business needs an AI audit is crucial for ensuring responsible and effective AI deployment. The tips we provided for understanding where risk maybe, what to look for and then a score card type risk assessment needs to be maintained and upgraded on a regular basis.

    AI is here to stay (unless the Dune movie scenario takes out all the “thinking machines”). Prepare appropriately or suffer the consequences of AI gone rogue (shudder).

    AI is amazing and if you have worked with it you know what the future holds. However as the Amazing Spiderman’s uncle said “With Great Power, Comes Great Responsibility.” Truer words were never spoken when it comes to AI! Just last week the

    Head of Open AI Sam Altman dissolved the group he setup just one year ago to protect us from the risks of AI. So, who is in charge of guarding the proverbial Open AI hen house when the boss doesn’t seem to care about anything but profits?

    You should. If you’re in the C-Suite or management you have already seen the power of AI. Some of the risks are quite apparent and obvious. Many of the risks are unknowable but many more have been identified and AI Risk Management best practices are becoming a “thing.”  So what is an AI Risk Mitigation audit? What do they do? How do I know if I need one?

    Let’s go back to the future as Michael J Fox would say! An AI Risk Audit is a comprehensive assessment of the risks associated with the deployment and use of artificial intelligence (AI) systems within an organization. It aims to identify potential vulnerabilities, ethical concerns, and operational risks related to AI technologies. Let’s delve into the details:

    An AI Risk Audit involves evaluating the entire AI lifecycle, from development to deployment, to ensure that your AI systems are trustworthy, secure, and aligned with organizational goals.

    Here are the key components of an AI Risk Audit:

    1. Risk Assessment:
      • Identify and assess risks specific to AI technologies, including biases, privacy violations, security breaches, and unintended consequences.
      • Evaluate the impact of AI on stakeholders, such as customers, employees, and the broader society.
    2. Governance and Policies:
      • Review existing policies and governance frameworks related to AI.
      • Develop or enhance policies that address AI risks, ethical considerations, and compliance requirements.
      • Prepare human resource guidelines for AI use by employees and get written sign off they understand the policies.
    3. Data Quality and Bias:
      • Analyze training data for biases and fairness issues. (Especially in HR)
      • Ensure that data used for AI model training is accurate, representative, and free from discriminatory patterns.
      • Where is the data going? Where is it coming from? How are you protecting your data from intrusive AI?
    4. Model Validation and Testing:
      • Validate AI models using robust testing methodologies.
      • Assess model performance, interpretability, and robustness.
    5. Explainability and Transparency:
      • Evaluate the transparency of AI decision-making processes.
      • Implement techniques to make AI models more interpretable.
    6. Security and Privacy:
      • Assess the security of AI systems, including protection against adversarial attacks.
      • Address privacy concerns related to data handling and model outputs.
      • Review employee use of data, what software they are authorized to use and what they are using that is not authorized – potentially harmful.
    7. Complete Scorecard of AI Risk
      • Rank all AI’s being authorized for risk.
      • Rank all AI’s being used but not authorized for risk.
      • Current state AI Risk scorecard and score after applying risk mitigation strategies.
    8. Ethical Considerations:
      • Examine the ethical implications of AI, such as fairness, accountability, and transparency.
      • Ensure that AI aligns with organizational values and societal norms.

    The majority of companies have not completed an AI risk assessment and many of the corporate lawyers are trying to figure out what is “going to be legal” vs. what won’t. Since there is so little precedent and many of the laws are being written, how do you know if you should complete an AI audit?

    Five ways to assess if your organization should consider an AI audit:

    1. Complexity of AI Systems:
      • If your business relies on complex AI models, such as deep learning neural networks, natural language processing (NLP), or reinforcement learning, an audit is essential.
      • Complex models are more prone to biases, errors, and unintended consequences. An audit helps identify and mitigate these risks.
    2. Data Sensitivity and Impact:
      • Consider the sensitivity of the data used by your AI systems. If they handle personally identifiable information (PII), financial data, or health records, an audit is critical. Government compliance is coming…
      • Assess the potential impact of AI decisions on individuals or society. High-impact decisions warrant thorough scrutiny. Ethics are important.
    3. Regulatory Compliance:
      • States are putting their own laws in place before the fed does. Until then there will be a patchwork of AI laws (like in Colorado last month) that are going to be easy to trip over.
      • Stay informed about AI-related regulations in your industry and region. Many sectors have specific requirements for AI transparency, fairness, and accountability.
      • If your business operates in a regulated environment, an audit ensures compliance.
    4. Ethical and Social Implications:
      • Evaluate the ethical implications of your AI systems. Are they aligned with your organization’s values and societal norms?
      • Consider the impact on fairness, privacy, and human rights. An audit helps address these concerns.
    5. Business Goals and Risk Tolerance:
      • Align AI audit decisions with your business goals. If AI plays a critical role in achieving those goals, an audit is necessary.
      • Assess your risk tolerance. If your organization cannot afford AI failures (financially, legally, or reputation-wise), prioritize an audit.

    Five Legal Reasons an AI Risk Audit maybe Needed:

    1. Mitigate Legal and Reputational Risks:
      • Organizations face legal consequences if AI systems violate privacy laws or discriminate against certain groups.
      • A thorough audit helps prevent legal disputes and protects the organization’s reputation.
    2. Avoid Bias and Discrimination:
      • Human resources must monitor and ensure their AI is not discriminating in hiring or firing. The consequences of failure will be legally expensive.
      • Biased AI models can perpetuate discrimination and harm marginalized communities.
      • An audit identifies bias and allows corrective actions to be taken.
    3. Enhance Trust and Stakeholder Confidence:
      • Transparent and accountable AI systems build trust among customers, employees, and investors.
      • An audit demonstrates commitment to responsible AI practices. Employees see it as well as customers
    4. Optimize Performance and Efficiency:
      • Identifying risks early allows for timely adjustments, improving AI system performance. This needs to be ongoing. It isn’t set it and forget it.
      • Efficient AI systems lead to better business outcomes.
    5. Stay Ahead of Regulatory Changes:
      • Governments are increasingly regulating AI technologies.
      • Europe is first up with AI Laws, US States second and Fed likely last.
      • An audit ensures compliance with evolving regulations.
      • Piece of mind for C-Suite – An AI Risk Audit allows a good sleep with fewer Terminators popping up in bad places.

    Remember that AI Risk Audits are not one-time events; they should be conducted periodically to adapt to changing risks and advancements in AI technology.

    Evaluating whether your business needs an AI audit is crucial for ensuring responsible and effective AI deployment. The tips we provided for understanding where risk maybe, what to look for and then a score card type risk assessment needs to be maintained and upgraded on a regular basis.

    AI is here to stay (unless the Dune movie scenario takes out all the “thinking machines”). Prepare appropriately or suffer the consequences of AI gone rogue (shudder).  

    Tags: #OpenAI #SamAltman #AI #AIRisk #AIRiskmitigation #technology #humanresources #CEO #lawyer #AILaw #AILawyer

     

  • Private LLM vs. ChatGPT, Which is Better Right Now?

    Private LLM vs. ChatGPT, Which is Better Right Now?

    In the rapidly evolving landscape of artificial intelligence, businesses are increasingly faced with the choice between leveraging commercial AI solutions like ChatGPT and developing their own private Large Language Models (LLMs). Each option presents a unique set of benefits and challenges, impacting areas such as data privacy, continuity, security, and cost management. While commercial AI services offer convenience, they often come with hidden risks related to data exposure and dependency on external providers. On the other hand, private LLMs, though requiring a significant initial investment, promise enhanced control, customization, and long-term value. This article delves into a detailed comparison of these two approaches, highlighting the critical factors that businesses need to consider to make an informed decision.

    Data Privacy

    One of the primary advantages of using a private LLM (Large Language Model) over a commercial AI solution like ChatGPT is data privacy. Commercial AI providers, even though they might not explicitly use your data to train their models, still collect and analyze the information you input. This can include what questions are being asked, how often, and on what topics. Such insights can inadvertently leak sensitive information about your business’s activity, growth, and market positioning. Competitors might glean valuable insights from this aggregated data, potentially putting your strategic advantages at risk.

    In contrast, a private LLM ensures that all data remains within your business’s control. By claiming a private LLM, you can offer a more secure and private experience for your clients, reinforcing trust and confidentiality. This level of privacy is especially crucial for industries handling sensitive information, such as finance, healthcare, and legal services.

    Continuity & Security

    Reliability is another significant consideration. Commercial AI providers, despite their robustness, are not immune to market fluctuations, service outages, or even potential collapses. Dependency on these providers can expose your business to unforeseen risks, including forced outages or disruptions.

    Private LLMs, on the other hand, offer greater control over continuity and security. Since the infrastructure is managed internally or through a dedicated provider, you can mitigate risks associated with external service dependencies. This ensures uninterrupted access to AI capabilities, enhancing operational stability and customer satisfaction.

    Quality of Answers

    The quality of responses from private LLMs can be meticulously controlled. These models can be trained exclusively on curated datasets pertinent to your industry, ensuring that the information is accurate, up-to-date, and relevant. For instance, a private LLM in the medical field can be trained using the latest medical resources, avoiding outdated or erroneous information commonly found on the broader web.

    Moreover, private LLMs can be programmed to acknowledge the limits of their knowledge, offering responses like “I don’t know, seek medical advice” when they encounter questions beyond their training scope. This ensures that users receive reliable and responsible guidance, enhancing trust in your AI solutions.

    Asset Not an Expense

    Investing in a private LLM transforms it from a recurring expense into a valuable business asset. Unlike commercial AI services that incur ongoing subscription costs, a private LLM can be developed, trained, and optimized over time, becoming an intellectual property asset on your balance sheet. This asset can appreciate in value as it continuously learns and adapts to your business needs, providing a long-term return on investment.

    Directed Responses

    Private LLMs offer the flexibility to align responses with your business preferences. They can be engineered to recommend specific products, vendors, or service providers, tailoring interactions to support your strategic goals. This directed approach ensures that the AI’s output consistently aligns with your business objectives and customer service standards.

    Visible & Recognized Costs

    The cost predictability of private LLMs is another advantage. With a private LLM, you can establish a clear cost structure, such as costing per response, making it easier to forecast and manage expenses. In contrast, commercial AI services often have variable pricing models, which can result in unpredictable costs over time. Every interaction with a commercial AI incurs a fee, which can quickly accumulate and become a significant, often underrepresented, expense.

    Future Marketable Data

    A private LLM, trained on user interactions, complaints, and concerns, becomes a valuable repository of marketable data. This data can provide insights into consumer trends, interests, and pain points, which can be leveraged for additional revenue streams. For example, insights derived from a healthcare-focused LLM can inform product development, marketing strategies, and customer engagement approaches, enhancing your competitive edge.

    Additional Benefits

    1. Compliance: For businesses operating in highly regulated industries, a private LLM can be tailored to comply with specific regulatory requirements, ensuring that data handling and processing meet all necessary legal standards.
    2. Performance Optimization: Private LLMs can be optimized for performance based on the unique usage patterns of your business. This can result in faster response times and more efficient processing.
    3. Integration with Proprietary Systems: Private LLMs can be seamlessly integrated with proprietary systems and databases, ensuring that all relevant information is accessible and that the AI can provide the most accurate and contextually appropriate responses.

    Conclusion

    While commercial AI solutions like ChatGPT offer convenience, the benefits of a private LLM are compelling for businesses seeking enhanced privacy, security, and control. By investing in a private LLM, you not only safeguard sensitive information but also create a valuable business asset that can be tailored to your specific needs. The ability to manage costs predictably, ensure the quality of responses, and leverage future marketable data further underscores the strategic advantages of opting for a private LLM over a commercial alternative.

    #ChatGPT #PrivateLLM #OpenAI #AI #GenAI #AIRisk #RiskMitigation #Bestpractices #ROI

  • 10 Ways Salespeople Can Up Skill with AI Today!

    10 Ways Salespeople Can Up Skill with AI Today!

    As an AI consultant and writer I have many C-Suite Executives asking me whether or not AI will replace their sales representatives. Most importantly they want to know whether Blended AI (See Augmented AI in Sales) is the short term answer and should they be up skilling their team or going out and hiring AI enhanced Sales People that already know how to and are using blended AI?

    My answer is that the advent of AI in sales tech is not a signal for human obsolescence (nor sacrificing sales people)! Nay I Say!

    It is a once in a generational opportunity for sales professionals to rapidly upskill and become irreplaceable as sales rep churn continues unabated. This is the Sales Rep Holy Grail to staying relevant and winning, by up skilling with AI:

    Here are ten ways to ensure Sales Reps stay ahead of the pack with AI

    1. Embrace AI Tools: Familiarize yourself with AI-powered CRM and sales automation tools to enhance efficiency and accuracy in your sales process1.
    2. Data Analysis: Develop the ability to interpret and leverage data analytics to understand customer behavior and preferences2.
    3. Personalized Selling: Use AI insights to tailor your sales approach to individual customer needs, creating a more personalized buying experience3.
    4. Continuous Learning: Engage in ongoing education through platforms like Coursera or LinkedIn Learning to stay updated on the latest AI trends and applications in sales4.
    5. Collaborative Skills: Enhance your ability to work alongside AI by understanding its capabilities and limitations, ensuring a synergistic relationship5.
    6. Critical Thinking: Cultivate the ability to analyze situations and make decisions beyond the suggestions of AI, adding a human touch to automated processes2.
    7. Adaptability: Stay flexible and ready to adopt new technologies and methodologies as AI continues to evolve the sales landscape2.
    8. Ethical Selling: Maintain a strong ethical framework in your sales practices, ensuring that AI is used responsibly and transparently6.
    9. Communication Skills: Hone your communication skills to effectively convey complex AI-driven insights to customers in an understandable way5.
    10. Creative Problem-Solving: Develop creative solutions for challenges that AI cannot address, showcasing your unique human value6.

    Top 5 New Skills Salespeople Need:

    1. Tech Savviness: Understand the technical aspects of AI tools to utilize them effectively in the sales process7.
    2. Strategic Thinking: Plan and execute AI-enhanced sales strategies that align with business goals7.
    3. Emotional Intelligence: Build strong relationships with customers, something AI cannot replicate8.
    4. Negotiation Skills: Use AI data to inform negotiations but rely on human intuition to close deals5.
    5. Resilience: Be prepared to pivot and adapt strategies in response to AI-driven market changes7.

    Where to Go for AI Sales Training:

    Sales Prompt Training

    In the age of AI, sales prompts have become a critical tool for engaging with customers and personalizing the sales experience. Here are some resources where you can get top-notch Sales Prompt training:

    These resources will help you understand how to use sales prompts effectively, ensuring that you’re equipped to leverage AI in your sales interactions and maintain a competitive edge.

    Bonus: Immediate Actions to Protect Your Sales Job:

    1. Update Your Skillset: Learn about AI and how it impacts the sales process1.
    2. Leverage Your Network: Connect with peers and industry leaders to learn best practices10.
    3. Showcase Your Value: Demonstrate how your unique skills complement AI capabilities11.
    4. Be Proactive: Take initiative in learning and implementing AI tools in your sales strategies12.
    5. Focus on Customer Experience: Use AI to enhance, not replace, the personal touch in customer interactions13.

    In conclusion, AI is not a threat but a tool that, when wielded with skill, can elevate your sales career to new heights. By embracing these strategies, sales professionals can ensure they remain indispensable in an AI-driven world.

    Please feel free to post any additional articles or resource you feel would be helpful for our Sales Colleagues.

    Tags: #AI #OpenAI #GenAI #BlendedAI #AugmentedAI #salespeople #salesreps #CRO #CFO #CHRO #CEO

  • Risk and AI for Corporations – 10 Things to Evaluate Right Now!

    Risk and AI for Corporations – 10 Things to Evaluate Right Now!

    The corporate landscape is changing faster than ever before and its speed is increasing exponentially.

    The road to business and sales success which were easy to follow in yesteryear has been replaced with crooked pavement and too many potential twists and turns to easily predict the right path forward.

    Artificial Intelligence (AI) is the reason for this corporate revolution, offering unprecedented opportunities for speed, growth, knowledge, and efficiency. However, as every comic book reader knows with great power comes great responsibility, and AI is no exception.

    In fact, the biggest concern is that AI is moving so fast that what is legal or not, is in question as new precedents are set and new laws in Europe and the USA start to form. Sam Altman CEO of Open AI has even gone on record to state that the AI Revolution maybe moving too fast for Society to Adapt. Same with Business!

    The challenge for any C-Suite team is understanding which AI risks are real vs. AI fear mongering.

    Corporations must be vigilant when integrating new AI applications into their operations. They must be clear regarding the known risks and be prepared if their AI goes rogue how they are going to handle it. With AI you can’t know everything that is coming, but corporations must look at the big picture when implementing AI, and know the potentially amazing, good while being prepared for the ramifications of a bad AI install or employees using AI illegally (likely without even knowing it).

    Top 10 AI Risks Corporations Face Today 

    AI Risk Potential Impact Risk Mitigation Strategies 
    Data Privacy Unauthorized access to sensitive information – AI “sees everything”  Implement robust encryption and access controls 
    Bias in Decision-Making Unfair treatment and discrimination Regularly audit algorithms for bias and fairness 
    Security Vulnerabilities Cyber-attacks exploiting AI weaknesses Conduct frequent security assessments and updates 
    Compliance Risks Fines and penalties for non-compliance Stay updated with regulations and ensure AI adherence 
    Lack of Explainability Difficulty in understanding AI decisions Develop explainable AI models and provide transparency 
    Dependence on AI Over-reliance leading to skill degradation Encourage human oversight and continuous learning 
    Job Displacement Loss of jobs due to automation Invest in workforce reskilling and upskilling programs 
    Ethical Concerns AI actions conflicting with moral values Establish an AI ethics board and clear guidelines 
    Misinformation Spread of false information by AI Design AI with fact-checking and source verification 
    Intellectual Property Risks Theft or misuse of AI-generated content Secure intellectual property rights for AI creations 

    This article covers just ten current AI concerns and each of these is an entire paper with a risk mitigation strategy associated with it and a team of AI lawyers ready to deploy! If you’re interested in learning more check out Sentia AI’s interview with AI Lawyer Gary Kibel.

    The long and the short of it is the rapidly evolving landscape of AI, corporations must stay ahead of these risks to harness AI’s full potential while maintaining trust and integrity. By proactively addressing these concerns, businesses can pave the way for a future where AI and human ingenuity work hand in hand for the betterment of society. 

    — 

    Paper Source(s) 

    1. AI: These are the biggest risks to businesses and how to manage them 

    2. Identifying and managing your biggest AI risks | McKinsey 

    3. How Organizations Can Mitigate the Risks of AI 

    4. 12 Dangers of Artificial Intelligence (AI) | Built In 

    5. Risks of AI vs. Risks From AI: Where Should Enterprise Focus … 

    6. What are the risks of using AI in business? – Financial Times 

  • 10 Ways Banks are Using Generative Artificial Intelligence Right Now!

    10 Ways Banks are Using Generative Artificial Intelligence Right Now!

    Banks are the life blood for many small and medium-sized businesses across North America. They often know the community, business leaders and local business environment better than anyone else.

    Their typically fiscally conservative but willing to try new business practices or technology if they can see a clear value proposition for their customers and their bottom line.  Is AI something that could differentiate their businesses?

    The answer is a resounding YES!

    Savvy, forward thinking bankers are increasingly turning to Generative AI to enhance their competitive edge. Here are ten ways they’re doing it, complete with real-world examples: 

    AI in Banks (see use links below)

    1. Personalized Customer Experiences: Banks like Wells Fargo are pioneering Large Language Models (LLMs) to create multimodal banking experiences, offering personalized services [3]. 
    2. Efficient Risk Assessment: Commonwealth Bank of Australia (CBA) uses Generative AI for enhanced customer experience and combating financial abuse [3]. 
    3. Fraud Detection: JPMorgan Chase employs Generative AI to analyze emails for signs of fraud, improving their detection systems [5]. 
    4. Automated Customer Service: Bunq has introduced a GenAI bot for its banking app, streamlining customer service interactions [3]. 
    5. Market Trend Analysis: Deutsche Bank provides AI-powered personalized investment guidance, helping customers navigate market trends [3]. 
    6. Regulatory Compliance: HSBC is training AI software to manage back-office functions, ensuring compliance with regulations [4]. 
    7. Operational Efficiency: Royal Bank of Canada (RBC) uses AI to automate process-heavy tasks, increasing operational efficiency [4]. 
    8. Credit Scoring Models: Banks are refining credit scoring with AI, though specific examples are under proprietary use and not publicly disclosed. 
    9. Investment Strategies: AI-driven models assist banks like Deutsche Bank in developing robust investment strategies [3]. 
    10. Customized Banking Products: Generative AI helps banks create financial products tailored to their community’s needs, as seen with Wells Fargo‘s LLMs [3]. 

    5 Ways Community Banks Can Integrate Private LLMs to Safeguard Corporate Content 

    Community banks are also cautiously adopting Private LLMs to protect sensitive data and reduce AI Liability exposure. Here’s how they’re doing it: 

    1. Data Control: Morgan Stanley Wealth Management uses an advanced chatbot powered by OpenAI’s technology to leverage the bank’s research and data library while maintaining data control [7]. 
    2. Customized Solutions: Banks can develop bespoke tools that align with their operational models, similar to ABN AMRO‘s use of Generative AI to summarize conversations between bank staff and customers [7]. 
    3. Secure Customer Interaction: By managing customer data securely, banks like ABN AMRO ensure privacy and build trust with their customers [7]. 
    4. In-House Expertise: Developing in-house expertise with Private LLMs allows banks to produce more accurate and relevant insights, as demonstrated by Morgan Stanley [7]. 
    5. Strategic Innovation: Private LLMs enable banks to innovate strategically, developing unique services that differentiate them from competitors, much like Morgan Stanley‘s approach [7]. 

    In conclusion, banks are not just keeping pace—they’re setting the pace, using Generative AI and Private LLMs to redefine the banking experience for their customers and secure a competitive edge in the digital age. However, they do need to keep pace on the evolving AI Legal Landscape as new precedents are set and new laws hit the book. See this video for more on AI Legal Challenges.  

    This article includes specific examples of large banks and community banks using Generative AI, Private LLMs, Data Security and with references to further information on their applications. 

    Article Link Source: Conversation with Copilot 

    1. Generative AI in Banking Best Practices in 2024 [With Examples] 
    2. Here’s how banks are using and experimenting with generative AI 
    3. Leading banks rolling out more generative AI – Oracle 
    4. Generative AI in finance: How banks are using LLMs in 2023 
    5. Generative AI in banking and financial services | McKinsey 
    6. Generative AI’s unfolding transformation of banking 
    7. Top 4 Use Cases of Generative AI in Banking [2024] – AIMultiple 
    8. Bridging the AI Divide: How Banks Can Responsibly Adopt Large Language … 
    9. Private LLMs on Your Local Machine and in the Cloud With LangChain … 
    10. Private LLMs: Key to Business Innovation and Secure AI Integration 

    Tags: #banks #banking #communitybanks #finance #OpenAI #LLM #LLMs #AI #usecase #CEO #CFO #CMO #technology #WallStreet #financialservices #loans

  • The Risks & Legal Perils of AI Right Now

    The Risks & Legal Perils of AI Right Now

    In this impressive AI Legal Panel review top NYC AI Lawyer Gary Kibel JD/MBA and AI Startup SME CEO David Forder are interviewed by David Brown MBA on the legal Risks and Rewards of AI right now.

    AI Legal Risk Topics Include:

    What are corporate employees doing that is illegal and they probably don’t know it?

    How does the C-Suite View AI from a Legal Perspective?

    What can CEO’s do right now to significantly mitigate AI Legal Risk?

    What has Europe done around AI, Data Privacy and business use, the USA may follow?

    How can you evaluate your AI software use internally?

    How do you legally protect your data against AI malfeasance?

    How do you evaluate AI from a legal perspective before deployment?

    Should Corporate AI Lawyers create new Human Resource policies specific to employee AI use?

    These are just some of the many AI legal topics we reviewed in this fascinating AI panel discussion.

  • This is How Corporations are Using AI Right Now

    This is How Corporations are Using AI Right Now

    Since I write about AI, business and marketing I’m often asked to provide specific examples of how AI is used right now vs could be or might be. The following ten companies have all used AI in a myriad of different ways and are expanding their use daily.

    Ten Corporations Use of AI

    1. Netflix – Personalized Recommendations: Netflix uses AI algorithms to analyze your viewing history, ratings, and other user data to suggest movies and shows you might enjoy. This personalization is a key factor behind their subscriber base and keeps users engaged on the platform. Case Study: How Netflix Uses AI to Personalize Content Recommendations and Improve Digital Marketing
    2. Amazon – Recommendation Engine: Amazon utilizes AI to recommend products to users based on their browsing history and past purchases. see “How amazon recommendation engine works“
    3. UPS – Delivery Route Optimization: UPS leverages AI to optimize delivery routes, saving time and fuel. See “UPS Leverages AI for Route Maximization”
    4. Spotify – Personalized Playlists: Spotify uses AI to create personalized playlists for users based on their listening habits and preferences. They can also use AI to replicate foreign speakers to the users preferred language. See Spotify will use AI to replicate podcasters’ voices and translate them to other languages
    5. Bank of America – Fraud Detection: Similar to JPMorgan Chase, Bank of America uses AI to analyze transactions and identify fraudulent activity. See “Bank of America’s Erica AI Surpassed 1.5 Billion Client Interactions: Here’s What That Means for the Future of AI in Banking.”
    6. Facebook – Content Moderation: Facebook employs AI to identify and remove inappropriate content from its platform. See “AI content moderation problems.”
    7. Nest Labs (Google) – Smart Thermostat: Nest thermostats use AI to learn your heating and cooling preferences and adjust automatically for optimal comfort and efficiency. See: Generative AI can now help you automate your home
    8. Ford – Predictive Maintenance: Ford utilizes AI in their manufacturing plants to predict when equipment might fail. By analyzing sensor data and historical maintenance records, the AI can identify potential issues before they occur, preventing costly downtime and production delays. See “FORD ESTABLISHES LATITUDE AI TO DEVELOP FUTURE AUTOMATED DRIVING TECHNOLOGY.”
    9. JPMorgan Chase – Fraud Detection: JPMorgan Chase leverages AI to analyze vast amounts of financial transactions in real-time to detect fraudulent activity. The AI can identify suspicious patterns and flag them for further investigation, helping to protect the bank and its customers from financial losses. See “How AI will make payments more efficient and reduce fraud.”
    10. Salesforce – CRM with AI: Salesforce incorporates AI features in its CRM (Customer Relationship Management) software to automate tasks, predict customer behavior, and personalize interactions. See Salesforce AI.

    This is a just a subset of corporate America’s use of AI. Every Fortune 500 company is using AI in some way and testing it in 100 others. The only thing I can guarantee with AI is that change is happening at an existential speed and it will create generational change like moving from steam power to electricity. Stay Tuned it is going to be a wild ride!

    Contemplating the future of AI and looking for the right AI sales tech implementation partner? Use this guide to find the best one. “Top tips & tricks to pick the best Salesforce AI implementation Partner.”

    Tags: #AI #Google #Ford #Salesforce #Banks #Netflix #OpenAI #Facebook #spotify #UPS #Amazon #JPMorgan #BofA #Casestudy #corporate

  • 10 Ways COO’s are Harnessing AI – Right Now and a look to the future

    10 Ways COO’s are Harnessing AI – Right Now and a look to the future

    Artificial Intelligence (AI) is revolutionizing the business landscape, and Chief Operating Officers (COOs) are at the forefront of this transformation. From streamlining operations to making informed strategic decisions, AI is a powerful tool in the hands of a COO. Let’s explore the top ten ways COOs are currently using AI and look at some real-world examples.

    Top Ten Ways COOs are Using AI

    1. Predictive Maintenance1: AI algorithms can analyze data from sensors and machinery to predict when a piece of equipment is likely to fail, allowing maintenance to be performed just in time.
    2. Demand Forecasting1: AI-driven demand forecasting uses past sales data, market trends, and consumer behavior to predict future product demand accurately.
    3. Price Optimization1: Dynamic pricing strategies are revolutionized by AI, which considers factors such as market demand, competitor pricing, and inventory levels to determine the optimal price for products or services.
    4. Supply Chain Optimization1: By analyzing data across the entire supply network, AI can optimize routes, reduce shipping costs, and improve supplier selection.
    5. Chatbots and Virtual Assistants1: Intelligent chatbots and virtual assistants enhance customer service while reducing manpower costs.
    6. Fraud Detection1: In finance and e-commerce, AI can identify patterns indicative of fraudulent activity, flagging transactions for further investigation and preventing financial loss.
    7. Customer Relationship Management2: AI is used to improve customer service, cut business costs, and supercharge business processes.
    8. Digital Personal Assistants2: AI-powered digital personal assistants are used to make technology more accessible and offer support to customers.
    9. Inventory Management2: AI is used to manage inventory effectively, reducing holding costs, and avoiding stockouts or overproduction.
    10. Content Production2: AI models can generate marketing content, financial reports, and even assist in product design.

    Bonus: Future Use Cases for COOs

    Looking ahead, AI continues to offer exciting possibilities for COOs. Here are three potential use cases a year from now:

    1. Real-Time Global Market Shifts3: Advanced AI systems might provide businesses real-time global market shifts, nuanced consumer sentiment evaluations, and even predict emergent industry trends by correlating diverse data sources.
    2. Strategic Business Proposals3: We can anticipate AI generating strategic business proposals, crafting comprehensive market strategies, or even simulating product launches to predict outcomes.
    3. Sales Forecasts and Risk Analysis3: Businesses may use AI for sales forecasts, risk analysis, and customer behavior predictions.

    COO’s are experimenting with a number of different use cases, price points and integration challenges. Over the next two years there will be less experimentation and more focus on implementing tools that provide AI advantages. That will also involve everything around the AI as well as the tools themselves.

    Conclusion

    As we’ve seen, AI offers a myriad of opportunities for COOs to enhance efficiency, inform strategic decisions, and create new value streams. As AI continues to evolve, we can only expect its impact on business operations to grow.

    For More information on how these roles are using AI:

    What do CEO’s Need to Know about AI to Avert AI Legal Disaster

    Keeping Counsel – What are AI Corporate Lawyers worried about?

    Top Ten Most Used CFO AI Prompts

    CMO? Top Ten AI Use Cases

    Tags: #COO #AI #usecase #CEO #CFO #CMO #technology

  • Unlocking the Mysterious World of Private LLMs

    Unlocking the Mysterious World of Private LLMs

    By Sentia AI’s Word Sorcerer & AI Jargon Decipherer

    What’s the Buzz About Private LLMs?

    Ah, my fellow digital wanderers! Gather ’round the virtual campfire, for today we delve into the clandestine realm of Private Large Language Models (LLMs). Picture this: a secret society of linguistic marketing and sales wizards, huddled in dimly lit chatrooms, whispering magic AI incantations to protect our words from prying algorithms. Yes, my friends, it’s time to demystify the enigma that is the Private LLM, lets see if the mystical golden dragon can protect your Castles AI Database.

    Chapter 1: The Quest for Privacy

    In the age of oversharing and data breaches, privacy is our golden fleece. Publicly available LLMs like GPT-3 are like chatty parrots—repeating everything they hear. But fear not! The Private LLM is a trusty invisibility cloak (TM Doctor Strange). It’s like whispering secrets to a mime—only you know what’s being said.

    Chapter 2: Crafting Your Own LLM

    1. Set Your Objectives: Before embarking on this epic journey, ask yourself: What’s your LLM’s purpose? Internal memos? Content creation? Or perhaps decoding ancient hieroglyphs? Define your quest, young linguist!
    2. Choose Your Architecture: Imagine LLMs as magical Unicorn like creatures. You can fine-tune existing beasts, birth a new one from scratch, or adopt a friendly neighborhood open-source model (like GPT-2). Choose wisely, for your LLM shall inherit its lineage.
    3. Data Collection and Preprocessing: Ah, the potion-making phase! Gather texts, poems, and cat memes (for err… balance). Cleanse them of typos, heresies, and misplaced semicolons. Tokenize like a caffeinated squirrel.
    4. Training Your Model: Picture a wizard’s tower, filled with humming GPUs and mystical incantations. Training your LLM takes time—days, weeks, or until your beard grows longer than Gandalf’s. Did you say prompts create the best answers?
    5. Securing the Model: Guard your LLM like Smaug guards his gold. Encrypt it, set traps, and hire a digital Cerberus. Remember, loose LLMs sink ships. AI risk is everywhere – every company wants to steal your data and learn your secrets! Your brave Private LLM – stands tall like a giant risk wall!

    Chapter 3: The Legal Scroll

    Why is this arcane knowledge legally crucial?

    Well, my friends, imagine a world where LLMs spill secrets like gossiping witches. Legal contracts, confidential emails, and classified cat videos—all exposed! Private LLMs keep our secrets safe, like a dragon guarding its hoard. Lawyers nod approvingly. Yes, “this is the way” echo the Mandalorians.

    Chapter 4: Marketing and Sales Sorcery

    Behold, the five ways marketers and sales wizards wield Private LLMs:

    1. Hyper-Personalized Emails: Picture an email that knows your soul better than your therapist. Private LLMs craft personalized pitches, making you feel like the protagonist of your own epic saga. With your magic AI marketing quill, you leap castle moats like King Arthur & Merlin– no mortal can ignore you anymore!
    2. Content Generation Spells: Blogs, social media posts, and product descriptions—Private LLMs churn them out faster than a caffeinated scribe. Quality? As high as a dragon’s eyrie.
    3. Sentiment Analysis Potions: LLMs sip from the cauldron of sentiment. They gauge customer feelings, predicting whether they’ll dance a jig or summon a storm. Like Sun Tzu said if you know your target, you know how to win.  
    4. Chatbot Enchantments: Ever chatted with a bot that understands your existential crisis? Thank the Private LLM. It’s like having a digital therapist who doesn’t judge your sock choices. Nor take a nap during your session.
    5. Market Research Wizardry: LLMs analyze trends, decipher cryptic hashtags, and predict the next unicorn startup. They’re the Merlin to your Arthur.

    And there you have it, dear reader! The Private LLM—a magical quill in our digital spell book. So go forth, build your LLM fortress, and remember: “With great language comes great responsibility.”

    SEO Tags: #LLMs #LLM #PrivateLLM #AI #ChatGPT #OpenAI #marketing #digitalmarketing