Tag: strategy

AI and Salesforce Einstein. Is it’s AI Ready for Deployment?
In the ever-evolving landscape of enterprise AI, Salesforce’s Einstein platform stands as an emerging enterprise grade comprehensive suite tailored for the modern corporate buyer. Yes, it has lots of room for improvement just like every other AI in the market, that being said if you are already a Salesforce customer and want to get some help evaluating it here is a high-level view of what exactly Einstein is and is not.

Setting the table. As an enterprise leader, I recognize the critical importance of integrating AI capabilities that not only enhance operational efficiency but also secure a competitive edge.

As a strategic thinker. It is important to go back to basics and remember MBA 101.

It isn’t about the flashing AI sizzle, slick AI PowerPoint presentations or those “gee whiz you can do that,” features. It is about what can AI do, that I can’t do now, that will make an immediate impact on my business. Use the following before you start your AI buyer journey.

AI Evaluation Checklist:
1. Take AI out of the equation. What are the problems your company needs to solve.
2. These specific problems can be solved by AI
3. How long will it take to develop these AI tools?
4. How much will it cost to develop, integrate and train employees?
5. Are there internal resources to install it or will you need AI Implementation help?
6. When will you see a return on your AI investment?
Einstein 1 Platform serves as the cornerstone of Salesforce’s AI offerings, encompassing a wide array of tools from predictive analytics to advanced AI-driven actions. The inclusion of both legacy and cutting-edge features, such as Einstein for Developers, ensures a robust and versatile environment for any organization.

The Einstein Copilot is particularly noteworthy for its user-friendly interface and customizable actions, providing a glimpse into the future of organization-wide AI chatbots. Its simplicity out of the box belies its potential for extensive customization, empowering users to tailor it to their unique business needs.

The Einstein 1 Studio is where the magic of personalization happens. Here, you can fine-tune the Copilot’s actions, imbue it with a distinct personality, and meticulously test and debug to ensure seamless deployment.

The Prompt Builder is a game-changer, offering unparalleled value by enabling the creation of sophisticated prompts that leverage both internal and external data sources. This tool is instrumental in harnessing the full power of the Data Cloud, akin to crafting an intricate email merge template on a grand scale.

The Einstein Trust Layer is a robust set of features and guardrails that protect the privacy and security of your data, improve the safety and accuracy of your AI results, and promote the responsible use of AI across the Salesforce ecosystem. With features like dynamic grounding, zero data retention, and toxicity detection, the Einstein Trust Layer is designed to help you unleash the power of generative AI without compromising your safety or security standards.

Lastly, the Model Builder may appear modest, but it is a fundamental component that grows in significance as one delves deeper into the AI models suitable for enhancing Copilot Actions.

It’s essential to note that while Salesforce’s solutions provide a secure and structured approach to AI within an organization, they do not restrict the ingenuity and expertise of AI specialists. The safety measures in place are commendable, yet the flexibility remains for specialists to extract maximum value from AI, ensuring that the enterprise remains at the forefront of innovation and efficiency.

Fortune 500 companies are testing, deploying and have been using AI for some time. From the field the number one issue I hear is that it takes time to get it right, it can produce amazing results but it isn’t set it and forget it software. To max the value it requires constant tweaking and enhancements from a knowledgeable team. This often requires mixing inside and outside talent to ensure outside best practices are brought in.

There you go. Grab a certified Salesforce partner (like Sentia AI) to help understand how large enterprise businesses like yours, can evaluate how, when and where they can leverage the amazing potential of AI in their businesses, profitably.

tags: #Salesforce #AI #Einstein #CRM #Salesforce.com #GenAI #AIadoption #AIRisk #CRMChecklist
July 31, 2024
Have one of these Jobs? AI will Replace You!

The rapid advancement of artificial intelligence (AI) technology is reshaping the job market, posing significant risks to various professions. According to a recent report by Goldman Sachs, AI tools could impact 300 million full-time jobs worldwide, leading to considerable disruption in the labor market. As businesses increasingly adopt AI to improve efficiency, certain jobs are more vulnerable to automation. Here are five fields at risk from AI.

5 Jobs at Risk to AI Replacement

Data Analysts and Bookkeepers: Jobs that involve data analysis, bookkeeping, and basic financial reporting are highly susceptible to automation. These roles, which focus on repetitive administrative tasks, are prime candidates for AI-driven efficiency improvements.

Customer Service Representatives: The customer service sector is already experiencing significant changes due to AI. Chatbots and AI-powered assistants are increasingly handling customer inquiries, reducing the need for human agents. According to a study by Gartner, by 2025, 80 percent of customer service and support organizations will be applying generative AI technology in some form.

Media Content Creation: AI’s ability to read, write, and understand text-based data puts media jobs at risk, including roles in advertising, content creation, technical writing, and journalism. LLMs, in their most basic form, have a tenuous relationship with the truth but are facile with written text.

Legal Assistants and Paralegals: The legal industry is not immune to the impacts of AI. Roles such as paralegals and legal assistants, which involve consuming large amounts of information and synthesizing it into legal briefs or opinions, are particularly vulnerable. AI can replicate many of these tasks, although complete automation is unlikely due to the need for human judgment.

Financial Analysts: Financial analysts and personal financial advisors are also at risk. These professionals handle significant amounts of numerical data, identifying trends and making investment recommendations. AI can analyze data, highlight investment trends, and forecast better investment mixes more efficiently than humans¹⁰.

Bottom Line

There are many jobs that are already on the way out and many more professionals especially those with advanced degrees are going to need to figure out a way to retool and reinvent themselves.

On a weekly basis I talk to C-Suite execs trying to get a handle on what jobs AI can do that they can reduce headcount and invest in other areas. The smartest employees are embracing AI, learning how to best deploy it and they are the ones that will be replacing humans. Blended AI is the number one way to keep your job as we move into the fascinating world of AI enabled business.

#AI #JobMarket #Automation #FutureOfWork #ArtificialIntelligence #TechTrends #JobDisruption #AIImpact #WorkplaceAutomation #FutureJobs #AIRevolution #JobSecurity #AIandJobs #TechInnovation #AIAdvancements #JobRisks #AIinBusiness #AIinCustomerService #AIinMedia #AIinLegal #AIinFinance

July 16, 2024
Ten AI Innovations to Protect your AI Data Privacy Now

July 10, 2024
“Rise of the CAIO: Ready to Embrace the AI Overlord?”
Today I’m here to talk about a new trend that’s sweeping the business world faster than a Roomba on overdrive. Yes, folks, we’re talking about the rise of the CAIO – the Chief Artificial Intelligence Officer.

What is a CAIO?

A CAIO is like the captain of the Starship Enterprise, but for AI. They’re responsible for setting a company’s overall AI strategy, including the design, development, testing and implementation of artificial intelligence technologies. They’re the ones who ensure that AI isn’t just a buzzword in the company, but a tool that’s used effectively and ethically.

How Many Companies Have a CAIO?

According to research from Foundry, 11% of midsize to large organizations have already designated a CAIO, and another 21% of organizations are actively seeking one. That’s right, folks, the CAIOs are coming, and they’re coming in hot!

Do You Need a CAIO?

Well, that depends. If your organization is struggling to understand, develop, or apply AI technologies, you may need a CAIO. If you’re still using a calculator to do your taxes, maybe not. But if you’re looking to leverage AI to create personalized care plans for clients or to automate daily tasks to improve efficiency, a CAIO might just be what you need.

Fractional CAIO’s?

Most companies aren’t ready for a full time CAIO while a part time one makes perfect sense as they develop their AI strategy. Alternatives are to pay for AI strategy services and have a vendor implement them. Since AI Strategies are critical to future business success finding a good part time CAIO may be a good bridge to a FTE in the future while keeping costs lower.

What are CAIO’s getting paid?

CAIO compensation ranges from $225K to $400K before stock options, bonus etc. See wage article from freelancer.

Specific skills and qualifications required for a Chief AI Officer (CAIO) role

Technical Skills:

Proficiency in AI and machine learning, data science and analytics, traditional software development, and an understanding of AI infrastructure.

Experience with several technologies across multiple job functions.

5-10 years of experience in the machine learning field, working with or building AI-based products/platforms/solutions.

5-10 years of experience with cloud computing technologies, including both public or private clouds.

Leadership and Strategic Vision:

The ability to develop and drive the AI strategy to align with the organization’s broader digital transformation roadmap and business goals.

The ability to identify opportunities where AI can add value, such as improving operational efficiencies, enhancing customer experiences, or creating new revenue streams.

The ability to work collaboratively with many departments in developing AI strategy and vision.

The ability to design and target relevant business use cases, assess project outcomes, and measure ROI in each case.

Ethical Insight:

The ability to recognize and balance AI benefits with risks.

The ability to navigate an ever-shifting global regulatory environment and the serious ethical implications involved in AI development.

Educational Background:

A strong educational background in computer science, data science, or a related field is often a prerequisite.

Many CAIOs hold advanced degrees such as a Master’s or Ph.D. in a relevant field.

Top 5 Things CAIOs Are Focused on in the Next 3 Months
1. Learning about each business to see where AI opportunities are: CAIO needs to understand the ins and outs of the company, from the financials to the people. Where does data flow? What are my IT resources vs. what I need to implement AI? Where are the biggest corporate problems that the C-Suite agrees on and are well known – that is usually where they greatest opportunity for AI successes lie.
2. Engaging with leaders: They need to get their finger on the pulse of the company and keep it there. Where are the problems in each business unit? How are they being addressed? What have they done in the past? Who can they count on as internal champions? Who is scared of AI and needs to be brought up to speed?
3. Communicating with the team: The CAIO needs to introduce themselves, share their vision, and keep everyone in the loop. People are worried about their jobs especially when AI comes up – part of the job is calming the team, being clear what is going to happen and have human resources ready with retraining opportunities. Yes, it’s a fact. The CAIO will be replacing old jobs with Augmented AI. There is going to be disruption plan accordingly.
4. Developing or amending plans: They need to work with leaders to develop strategies and get board approval for AI strategies, investments and change management. AI is just like any other business unit. It must have clear objectives and measurable goals – and be held accountable.
5. Executing and delivering: The CAIO needs to deliver on their promises and make things happen. Often the CAIO will develop smaller AI demonstration projects to get team buy in and acceptance prior to implementation. Aligning tech feature enhancements with training and acceptance is critical to success.
Bonus: CAIO Top Concerns Right Now!

AI Risk Mitigation. AI has tremendous opportunities to greatly expand what a company can do with technology and it will become a clear differentiator in the marketplace. However…

Today’s AI laws are only on the books in a few states with no federal laws likely in the next year. The CAIO must work closely with human resources, internal legal team and the C-Suite to actively watch their known AI installments for bad behaviors as well as rogue employees using AI in unauthorized ways that can legally impact a company or put corporate data in the wild.

So, there you have it, folks! The rise of the CAIO is upon us. Will your business embrace its new AI overlord? Only time will tell!

Additional Reading:

How to Assess if you Need an AI Risk Audit

Hiring a CAIO – Here is what you need to know

What is a CAIO – According to IBM

Keeping Counsel – 5 Things keeping AI lawyers up at night

CAIO – What does it take to get the hottest IT job – according to CIO Magazine

How to become a CAIO in 2024?

What is a CAIO – And how do you become one?

Where do CAIO’s go for AI Information and Tools?

#Hashtags: #CAIO #ArtificialIntelligence #BusinessTrends #AIinBusiness #RiseOfTheCAIO
July 1, 2024
What is an Autonomous AI Agent? How is it being used Right Now??
The Advantages, Risks, and Applications of Autonomous AI
Agents

Autonomous AI agents are at the forefront of technological innovation, transforming industries and redefining how we solve complex tasks.

However, like any powerful tool, they come with both benefits and risks. In this article, we’ll explore the advantages and risks of using autonomous AI agents, delve into their diverse applications, and highlight companies that are harnessing their potential.

Advantages of Autonomous AI Agents

Autonomous AI agents are intelligent software programs designed to perform tasks without constant human supervision. They can sense their environment, reason, learn from their experiences, and take actions ¹ ². Here are some of the key advantages:
Companies Using Autonomous AI Agents

Several companies are leveraging the power of autonomous AI agents to drive efficiency and innovation:
Risks of Using Autonomous AI Agents

While autonomous AI agents offer numerous benefits, they also present potential risks:
1. Overlooking Rare Cases: As AI agents are based on statistical models, they might overlook rare cases and abnormalities, potentially leading to operational errors ³.
2. Complex Algorithms: The complex algorithms powering autonomous AI agents can be esoteric, making it challenging for users to understand or predict their behavior. This raises trust and transparency issues ³.
3. Ethical and Societal Considerations: The advent of autonomous AI agents brings to the forefront critical ethical and societal considerations. Issues such as privacy, security, and the impact on employment and social structures are paramount ⁸.
In conclusion, autonomous AI agents are transforming the way we approach complex tasks and decision-making processes. While they offer numerous advantages, it’s crucial to be aware of the potential risks and implement responsible usage strategies. As we continue to innovate and evolve, autonomous AI agents will undoubtedly play a significant role in shaping our future.

Tags: #AI #automonousai #AIAgents #AITrust #aisecurity #GenAI
June 27, 2024
The Essential Guide for Visionary AI Enabled CFOs:

June 26, 2024
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

May 29, 2024
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
May 21, 2024
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.

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May 20, 2024

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.

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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

April 22, 2024