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  • 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

  • How do Salesforce CRM, Microsoft Dynamics, and Hubspot CRM Compare according to Copilot?

    How do Salesforce CRM, Microsoft Dynamics, and Hubspot CRM Compare according to Copilot?

    As a leader in the AI CRM Sales tech space I was curious how AI’s would evaluate competitive CRM/AI enhanced offerings and how likely it would be that it would be factual vs. the usual CRM marketing puffery we see on a daily basis. I was also curious how much AI “hallucination” would take place and whether or not it would be to the advantage of the AI parent company (in this case Microsoft) or just generalized AI hallucinations or perhaps non at all.

    Copilot comparison of AI capabilities and features across Microsoft Dynamics, Salesforce CRM, Hubspot CRM:

    FeatureMicrosoft DynamicsSalesforce CRMHubspot CRM
    AI-Assisted SalesAdvanced AI for sales insights and personalization1AI with Einstein for predictive analytics and recommendations1Basic AI functionalities for sales insights2
    Automated Data CaptureComprehensive automation in data entry1Automation with a focus on sales productivity1Automation for basic tasks2
    Intelligent ForecastingDetailed forecasting using AI1Predictive forecasting with Einstein AI1Forecasting based on standard sales data2
    Performance RecommendationsPersonalized suggestions for sales actions1Recommendations for next best actions1General guidance based on performance metrics2
    Conversation IntelligenceConversation analysis for actionable insights3Detailed conversation insights with Einstein Call Coaching1Basic conversation tracking2
    Lead & Opportunity ScoringAI-driven scoring to prioritize leads4Predictive lead scoring with AI1Lead scoring based on user-defined criteria2
    CustomizationHighly customizable with AI applications1Extensive customization options with AI1Customizable to a lesser extent compared to others2
    Ease of UseRequires some technical expertise4Complex, with a steep learning curve1User-friendly and intuitive interface2
    CostStarts at $65 per user per month2Starts at $25 per user per month1Free for basic features; pay for more as you go2
    Copilot generated table comparing Dynamics, Salesforce & Hubspot

    This table provides a high-level overview of how each CRM utilizes AI to enhance the sales process. Microsoft Dynamics and Salesforce CRM offer more advanced AI capabilities, which are suitable for larger organizations or those with complex sales processes.

    The challenge with this table is not knowing what is the “latest” and most correct information vs. what Copilot was able to access (and the time period). If I look at the cost, I think the research is a bit *old however, I look to my readers to help out and tell me what they think. How accurate is this table and what is AI marketing puffery vs. what these three CRM platforms can actually deliver.

    *Microsoft Dynamics Official Seat Pricing Page

    Salesforce CRM Official Seat Pricing Page

    HubSpot – I went to their website looking for a HubSpot pricing page (like Microsoft and Salesforce) and was intercepted by the worst AI chatbot I have seen this year. It refused to answer my questions, no matter my change in prompt, instead it was just sell, give me your email address, sell, sign up for demo, sell and my last contact with it was “Thanks Hubspot Chatbot – your performance has been so horrid that I have just taken your CRM off my consideration list.”

    As my dear readers know AI must be constantly monitored by humanoids. In this case my guess is they turned up the aggressive sell chatbot feature and forgot the sentiment / human part of the CX equation. Will check back in a week to see if it was an AI hallucination or programmed in.

    This is your average Blended AI SME Homosapien signing off and respectfully requesting your comments and perspective. Who is going to win or will it be a dark house like Sentia AI – that will take over the CRM landscape. Stay tuned!  

  • 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.

  • Embracing AI: The Competitive Edge in the New Financial Year

    Embracing AI: The Competitive Edge in the New Financial Year

    A Few Things to Know Right NOW

    The Australian financial year is ending soon.

    While reviewing past decisions is important, it’s even more crucial to look ahead and start planning for what’s next.

    AI: More Than Just Hype

    Artificial intelligence (AI) isn’t a passing trend; it’s here to stay. Tools like Copilot, Claude, Gemini, and ChatGPT are revolutionising workplaces, offering solutions that once seemed like science fiction.

    These tools go beyond automating basic tasks. They enhance human capabilities, drive innovation, and improve efficiency. Companies that start building AI capability now won’t just survive; they’ll thrive and leave competitors behind.

    Investing in the Future

    As you plan for the new financial year, budgeting and allocating resources for AI adoption is essential. It’s not about a massive over hauls about a strategic investment. Even small steps can lead to a significant competitive edge.

    Starting Your AI Journey

    The saying “It doesn’t matter how slowly you go as long as you don’t stop” by Michael Jordan applies perfectly to AI adoption. It’s a long road, but taking that first step and staying committed is key.

    How to plan for AI capability

     Here’s what to consider when planning for AI:

    1. Identify opportunities: Where can AI benefit your business the most? Can it help deliver faster, cheaper, or better performance in specific functions, activities, processes, or steps?
    2. Choose the right solution: There are four main AI options, listed in order of cost:
      • Public chatbots (Copilot, Claude, Gemini, ChatGPT): These are readily available but require training staff for optimal use and addressing weaknesses like security and factual accuracy.
      • AI Extensions to existing systems: This involves integrating AI with your existing Enterprise Information Systems (EIS) to enhance their capabilities. Sentia is expert in this area, especially with CRMs.
      • Private chatbots: Similar to public chatbots, but trained on your company’s private data, making it inaccessible to outsiders.
      • Specialist Large Language Models (LLMs): These offer the benefits of private chatbots with the added advantage of being tailored to your specific industry or organisation.
    3. Prioritise initiatives: Which opportunities should you address in the coming year?
    4. Estimate benefits: Consider potential gains like revenue growth, cost savings, efficiency improvements, quality enhancements, and a stronger competitive edge.
    5. Analyse costs: Factor in the costs associated with your chosen AI solution.
    6. Calculate the return: Estimate the return on investment (ROI) to justify your plan.
    7. Move forward: If the ROI is positive, argue for adding your initiative to the budget.
    8. Implement: Turn your plan into action.
    9. Watch and adjust: Monitor as you go. Adapt as you learn.

     The Final Word

     Don’t let ‘the AI issue’ languish in your company for twelve more months – budget for those first steps this financial year coming!

    As Principal of Nielsen Knowledge, Brian helps organisations adopt AI intelligently. In 28 years in consulting, he has worked with private enterprise, the social impact sector, and the three tiers of government. Brian’s experience covers industries as varied as: agriculture, defence, energy generation and transmission, engineering, financial and insurance services, healthcare and medicine, information services, legal and justice, media, mining and resources, oil and gas, professional services, telecommunications, and waste and recycling.

  • What Data can CFO’s Trust Right Now?

    What Data can CFO’s Trust Right Now?

    Are CFOs just like Sherlock Holmes, always searching for accurate data?

    Ah, the mystical world of finance—the place where numbers dance, spreadsheets sing, and CFOs sip their morning coffee while pondering the existential question: “Is this balance sheet lying to me?” Did my CSO get those pesky salespeople to enter accurate data this week? Can I trust it this time?

    You see, dear reader, CFOs are a unique breed. They’re like the Sherlock Holmes of the corporate world, except instead of solving crimes, they’re unraveling financial mysteries. And trust me, there’s no shortage of those in today’s corporate databases. Its like a CSI crime scene that so many walked on the data is no longer accurate, can’t be used to solve the case.

    So, why the skepticism? Why do CFOs eye their financial data like a suspicious cat eyeing a new houseplant? Why do CRO’s hate the dreaded CFO Call “What is really going on with your sales projections – Gandalf?” Let’s dive into this with the grace of a synchronized swimmer and the wit of a stand-up comedian.

    1. The Data Comes from Too Many Sources

    Imagine you’re at a dinner party, and everyone’s talking at once like an ADD conference. The wine is flowing, the appetizers are aplenty, and the conversation is a delightful cacophony. Now, replace the wine with Excel sheets, the appetizers with databases, and the conversation with financial data. Voilà! You’ve got the CFO’s dilemma.

    CFOs juggle data from accounting systems, CRM platforms, spreadsheets, and that one ancient ledger in the basement guarded by a grumpy janitor with a dwarf axe. It’s like conducting an orchestra where the violinist is playing Beethoven, the flutist is jamming to jazz, and the tuba player insists on beatboxing like a rap star.

    We asked AI CRM SME and CEO Mr. David Forder – Why don’t CFO’s trust their CRM data? David said: “CFO’s know their data isn’t reliable. Sales people don’t enter data or enter it incorrectly or at the last minute-making accurate forecasting extremely difficult. CFO’s must have close relationships with sales directors to ensure they know what is likely vs. what is actually in the CRM or system of record. At Sentia AI we assume sales reps won’t put in data and do it for them. This significantly increases CRM accuracy.”

    2. Clunky Spreadsheets: The CFO’s Arch-Nemesis

    Picture this: a CFO, hunched over a computer, squinting at a spreadsheet that looks like it was designed by a blindfolded octopus. Rows and columns twist and turn, formulas multiply like rabbits, and somewhere in cell D42, the meaning of life is hidden (or maybe just the quarterly revenue).

    Spreadsheets are the Bermuda Triangle of financial data. CFOs fear that one wrong keystroke could plunge them into a vortex of miscalculations, where profit margins vanish, and debt multiplies like rabbits (again). No wonder they’re wary.

    3. Outdated Processes: The Ghosts of Accounting Past

    Remember the days when accountants used quills and inkwells? Neither do I, but apparently, some financial processes haven’t caught up with the 21st century. Manual data collection, handwritten ledgers, and carrier pigeons delivering invoices—okay, maybe not the pigeons, but you get the idea.

    CFOs stare at these relics and wonder, “Did our ancestors also struggle with reconciling petty cash?” The answer is probably yes, but they didn’t have to deal with Sarbanes-Oxley compliance, so it evens out.

    4. The Suspiciously Perfect Numbers

    Imagine you’re baking a cake, and every ingredient magically falls into place: exactly 2.5 cups of flour, precisely 1.75 teaspoons of baking powder, and a dash of unicorn tears. Suspicious, right? Well, that’s how CFOs feel when financial statements align too perfectly.

    “Is this real, or did the accounting elves visit last night?” they muse. “Did the revenue curve really follow a Fibonacci sequence or did they put in numbers they think I want to see?” It’s like finding a four-leaf clover every day—lucky, but also a tad eerie.

    In Conclusion: Trust, but Verify

    So, dear CFOs, keep your skepticism sharp and your calculators sharper. Trust your financial data, but verify it like a detective chasing down leads. And remember, even if the numbers seem fishy, at least they’re not as slippery as that salmon mousse at the dinner party.

    Now, if you’ll excuse me, I’m off to write a spreadsheet-themed murder mystery. Spoiler alert: The killer is the pivot table.

    David Brown, signing off with a wink and a formula. Here is the formula – if you need help figuring out how to get AI into your CRM to create believable forecasts contact the AI experts at www.sentia.online

    Disclaimer: The views expressed in this article are purely fictional and meant for entertainment purposes. No unicorns nor CFOs were harmed during this articles creation. Stay tuned!

    Tags: #CFO #CSO #CRO #Data #CRM #Salesforceai #AI #accounting #funny #cybersecurity