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  • 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

  • Open AI focus now on AGI says Sam Altman

    Open AI focus now on AGI says Sam Altman

    Unless you have been living under the proverbial rock you know AI is the hottest tech on the planet right now! Everyday it seems like a new breakthrough is happening or in the news.

    Sam Altman, the CEO of OpenAI, made waves in the tech world today with the launch of Sora, an impressive AI video generator. But what’s even more intriguing is his unwavering commitment to advancing artificial general intelligence (AGI). According to Wikipedia this is what AGI is:

    “An artificial general intelligence (AGI) is a hypothetical type of intelligent agent[1] which, if realized, could learn to accomplish any intellectual task that human beings or animals can perform. Alternatively, AGI has been defined as an autonomous system that surpasses human capabilities in the majority of economically valuable tasks.”

    With that definition in mind and also knowing this is new, I will take a stab at understanding what it means and why Sam is so focused on it.

    Top five reasons behind Altman’s laser focus on AGI

    1. Bridging Creativity and Intelligence

    Altman believes that AGI may represent the pinnacle of human achievement—a system that can outperform humans in nearly every cognitive task. Sora, with its ability to transform text prompts into lifelike videos, exemplifies the intersection of creativity and intelligence. By pushing the boundaries of what AI can achieve creatively, Altman aims to pave the way for AGI that seamlessly integrates both aspects.

    2. Sora’s Success as a Catalyst

    Sora is pretty cool and remarkable. It generates breathtakingly realistic videos from mere text inputs, captivating audiences worldwide. Altman see’s Sora’s success as a stepping stone toward AGI. The rapid progress in less than a year—from choppy, short videos to Sora’s cinematic quality—fuels his optimism. Sora’s capabilities demonstrate that AGI is within reach, and Altman is determined to accelerate its development.

    3. Red Teaming and Responsible AI

    OpenAI’s cautious approach is evident in its red-teaming strategy. Altman restricts Sora’s access to a select group of experts who assess risks associated with its release. Responsible AI development is paramount, especially considering the potential misuse of AI-generated content. By involving red teamers and creative professionals, Altman is trying to ensure that AGI evolves ethically and securely. Lets hope that continues!

    4. Challenging the Status Quo

    Altman’s commitment to AGI challenges existing norms. While other organizations opt for closed-source models, OpenAI remains transparent. Sora’s unveiling redefines the AI landscape, emphasizing collaboration and openness. Altman’s battle cry—“game on”—echoes through the tech community, inspiring innovation and pushing AGI research beyond its limits.

    5. AGI’s Global Impact

    Altman understands AGI’s global implications. The World Economic Forum’s 2024 Global Risks Report highlights AI-generated misinformation and disinformation as a significant risk. Altman’s pursuit of AGI aims to mitigate these risks by fostering responsible development. He envisions AGI as a force for good, revolutionizing industries, solving complex problems, and benefiting humanity at large.

    The focus on AGI after Sora’s launch is part of a strategic focus on building the “blocks of AI” as we go. Assuming Sora dazzles as anticipated, Altman will stay steadfast in his pursuit of AGI—a future where creativity, intelligence, and responsibility converge in a hopefully beneficial AI stew for humanity’s benefit.

  • 10 Ways you can use Augmented & Blended AI to Sell on LinkedIn – Now!

    10 Ways you can use Augmented & Blended AI to Sell on LinkedIn – Now!

    There are so many ways you can use AI in sales these days, it’s hard to know where to start, especially if you’re a newbie to all of the excitement. So, this article will focus on something almost everyone in the business world uses, knows, and understands how It can be used right now, on every business persons favorite, LinkedIn.

    Using AI with LinkedIn selling can enhance your sales and marketing efforts by automating tasks, providing valuable insights, and streamlining processes. As noted in previous articles, to maximize AI’s usefulness it requires a combination of Blended AI* and Augmented AI** to maximize its impact.

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    10 ways to use Augmented & Blended AI with LinkedIn

    1.    Personalized messaging: AI-powered tools can analyze data from LinkedIn profiles and interactions to create personalized messages that resonate with individual prospects, increasing engagement and response rates.

    2.    Relationship insights: AI can analyze communication patterns and interactions with prospects to provide valuable relationship insights, such as the best times to reach out or the most effective ways to engage with specific individuals.

    3.    Lead generation and prospecting: AI algorithms can help identify and prioritize potential leads based on specific criteria identified in your sales process, such as job titles, industries, or geographic locations, making your prospecting efforts more efficient.

    4.    Sales forecasting: AI-driven analytics can analyze historical data, sales patterns, and market trends to provide more accurate sales forecasts, helping you plan and strategize your selling efforts better.

    5.    Competitor analysis: AI can gather data on competitors’ activities and strategies on LinkedIn, providing valuable insights to refine your own selling approach and stand out in the market.

    6.    Performance tracking and analytics: AI-driven analytics can measure the effectiveness of your LinkedIn selling efforts, tracking key performance indicators (KPIs) like response rates, conversion rates, and engagement metrics. This data allows you to continuously improve your strategies.

    7.    Content recommendations: AI can analyze user behavior and preferences to suggest relevant content, such as articles, blog posts, or videos, that align with your prospects’ interests and pain points.

    8.    Social listening and sentiment analysis: AI tools can monitor conversations and discussions on LinkedIn to gauge the sentiment around your brand and industry. This information can help you tailor your messaging and identify potential opportunities or challenges.

    9.    Automated follow-ups: AI-powered sales automation tools can handle routine follow-up tasks, sending timely messages and reminders to prospects, helping to maintain engagement and nurture leads.

    10.Chatbots for instant responses: Implement AI-driven chatbots on your LinkedIn profile to provide instant responses to common questions, qualify leads, and engage with prospects at any time, improving customer experience and response times.

    There is no single AI tool (yet) that can accomplish all ten use cases noted on my list and it isn’t necessarily in order of usefulness. Instead, each seller must ascertain what is most important in their sales efforts and then use Blended AI to attack their sales patch in a coordinated and concise manner. Best practices are to pick a vendor that know what is actually working to advise when to use it and when not to, because:

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    The key isn’t using AI.

    The key is using the right AI with your already successful, winning Sales Process and applying the right form of AI at the right stage in the sales process. That is the Winning Ticket!!!

    Bottom line

    By leveraging Augmented and Blended AI in your LinkedIn selling efforts, you can streamline working sales processes, deliver a significantly more personalized experience for your target audience, and make data-driven decisions (not just gut feelings), ultimately increasing each seller’s success rate by connecting with the best prospects and converting them into valuable customers.

    *Blended Artificial Intelligence Is the amalgamation of different artificial intelligence techniques or approaches to achieve more robust and effective solutions. It involves integrating multiple AI models, algorithms, and / or technologies to leverage their respective strengths and compensate for their weaknesses. See Wikipedia:

    ** Augmented artificial intelligence is a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making and new experiences.

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  • In Marketing? Wondering how to use AI? Top 10 AI use cases for CMOs

    In Marketing? Wondering how to use AI? Top 10 AI use cases for CMOs

  • Top Tips to Select the Best AI Enabled Salesforce Partner

    Top Tips to Select the Best AI Enabled Salesforce Partner

    Unless you have been like the proverbial Ostrich with its head under the sand AI is here and making its presence felt quickly! Within a couple of years it will no longer be which company is using AI, rather it will be which company isn’t?

     

    Since sales has the most scrutiny and oversight within the majority of companies (see CRO’s Get Fired First) it is often the first place AI is applied, for many good reasons. Sales rep turnover is usually high, there is lots of training (see Blended AI for Sales Rep Training) that needs to take place and companies spend a small fortune on hiring, training and getting reps “field ready). Also reps want new tech that works (if it doesn’t they will refuse to use it) and are used to being the first to use it.

    AI is that “new” tech and many companies have already put AI implementation strategies into place (See CEO’s Must have an AI Strategy Plan) and those that don’t will, or they can fall behind competitors. The biggest challenge is AI is so new that top quality talent is hard to source, evaluate and hold onto. Many organizations (especially those using the number one CRM Salesforce) want to use AI strategy consultants that are also Salesforce partners. They know that these certified partners are the key to plan, strategize, and successfully implement AI.

    The biggest challenge is finding the right one. There aren’t many case studies or client testimonials out there (yet) since it is so new. Falling back on past partner evaluation techniques are a starting point. However, the most advanced AI Salesforce partners are often smaller and harder to find (See Sentia AI).

    With that in mind here are 7 high level tips for Selecting the right Salesforce partner with AI capabilities. Organizations looking to leverage artificial intelligence within their Salesforce ecosystem often have to look far and wide to find them. The following tips will act as a guide to help you in making an informed decision:

     

      1. Check AI Certifications and Expertise: Ensure that the Salesforce partner possesses relevant certifications and a track record in AI. Look for certifications or experience with Salesforce Einstein Analytics and Discovery Consultant, which demonstrate expertise in leveraging AI within the Salesforce platform. If you’re not going to use Einstein / Discover – it isn’t as important.

      1. Evaluate AI Use Cases and Experience: Assess the partner’s experience in developing as well as implementing AI solutions, especially within the Salesforce environment. Look for case studies or examples of successful AI integrations that align with your business needs. A partner with diverse AI use cases demonstrates versatility. Don’t pass on an AI Salesforce certified partner if they have limited or even no case studies – remember AI is in its infancy – be on the people at the company first and foremost, not slick marketing materials.

      1. Assess Industry-Specific Knowledge: Industry expertise is crucial for understanding the unique challenges and opportunities within your sector. Choose a partner with a deep understanding of your industry, as this knowledge will be beneficial when tailoring AI solutions to your specific requirements.

      1. Check Integration Capabilities: AI solutions often require seamless integration with existing systems. Ensure that the Salesforce partner has experience integrating AI capabilities with Salesforce and other relevant tools in your technology stack. This capability is crucial for a cohesive and efficient implementation.

      1. Verify Data Security and Compliance: AI involves handling sensitive data, so it’s vital that your Salesforce partner prioritizes data security and complies with relevant regulations. Request information on their data protection measures and ensure they align with your organization’s compliance requirements.

      1. Evaluate Training and Change Management Support: Assess the partner’s approach to training and change management. AI implementations often necessitate a shift in processes and workflows, so your partner should provide comprehensive training programs and support for change management to ensure successful adoption.

      1. Consider Scalability and Future-Readiness: Select a partner with a focus on scalability and future-readiness. The AI solutions implemented should be scalable to accommodate your organization’s growth, and the partner should stay abreast of advancements in AI technology and Salesforce updates to ensure ongoing relevance.

    Bonus Tips: Look for all in one help. Salesforce partners that can evaluate your business needs, sales process, configure Salesforce, then train your people are far more valuable than having to go out and find 3 separate partners to do each phase. It will cost a lot more in both time and money to do this and perhaps most importantly something is almost always lost with every handoff along the way. Also make sure the partner is going to be with you long term – AI is so new it is guaranteed to require tweaks, adjustments and outright replacement along the way. A partner with a history of run and gun is the last one you need right now. Check references to ensure they will work with you long after the sale is completed.

    Seek Client References and Feedback: Request references from the Salesforce partner’s past clients who have implemented AI solutions. Gather feedback on their experiences, challenges faced, and the overall success of the projects. This firsthand information can be invaluable in your decision-making process.

    By considering these tips, doing your homework and keeping an open mind to new partners that are ahead of the game, you can enhance your chances of selecting a Salesforce partner with the right AI capabilities. Finding that special, highly qualified partner to effectively integrate artificial intelligence into your Salesforce environment is the key to your future AI success!