FailingCompany - Back to the Basics

After spending the last three weeks discussing the ethical & legal considerations for AI as well as evaluating some of the common pitfalls of AI, it became apparent what my next post should cover. Hopefully this week's topic will fend off any thoughts of gloom and doom that might be creeping into you head. Afterall, my goal is to encourage you to take advantage of AI, not scare you off!

So, what's this week's topic? A little thing called AI Governance. Governance is a hot topic these days and can help provide the safety net for some of the risks that you'll face when venturing into the unknown land of AI. It also helps to support a solid approach to advancing AI within your company!

You might be asking yourself questions like: What exactly is AI governance? How do you implement it? Are there other companies using AI governance that I can study? All great questions, so let's dive right in!!

AI governance refers to the policies, frameworks, and procedures that ensure the ethical, responsible, and compliant use of AI in a business setting. Having an AI governance program is essential to mitigate risks and build trust among customers, employees, and stakeholders. To further explain, let's look at why AI governance is vital for small businesses, examine a possible reusable governance framework, and outline some steps for effective AI governance. We'll also look at few companies that have successfully implemented AI governance to enhance their operations.

Why is AI Governance is so Important for Small Businesses?
AI governance provides a structured approach to managing the risks and benefits associated with AI. The key reasons for small businesses to implement AI governance are:

• Ethical Use of AI: As AI systems become more integrated into business operations, concerns around bias, discrimination, and fairness are increasing. An AI governance model ensures that AI algorithms and processes align with ethical standards.


• Regulatory Compliance: Many industries, such as healthcare and finance, are subject to strict regulatory requirements regarding data privacy and the use of AI. Non-compliance can result in heavy fines and reputational damage. Governance frameworks help businesses meet these legal obligations.


• Data Privacy: AI systems often rely on vast amounts of data, including personal and sensitive information. AI governance helps ensure that this data is managed responsibly, adhering to privacy laws like the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).


• Transparency and Accountability: Governance ensures that decisions made by AI systems are explainable, traceable, and can be audited. This transparency builds trust with customers and other stakeholders.


• Risk Management: AI is powerful, but it also introduces risks such as faulty algorithms, security vulnerabilities, and unintended consequences. Governance frameworks help identify and mitigate these risks early in the AI lifecycle.


• Sustainable AI Innovation: A structured governance model facilitates continuous learning and improvement in AI systems, ensuring that the business can scale AI responsibly over time.

Is There a Framework for AI Governance?
To build a strong AI governance model, small businesses can adopt a reusable framework that provides clear guidelines, roles, and processes. Below is a one possible high-level framework that can be adapted based on your company’s specific needs and AI maturity.

1. Governance Policy and Principles
The foundation of AI governance starts with creating governance policies and principles that outline the company’s stance on the ethical use of AI. These should include:

• Fairness: AI systems must be designed to eliminate bias and ensure equal treatment for all users.


• Transparency: Decisions made by AI must be explainable and transparent to stakeholders.


• Privacy: Data used for AI should be collected, processed, and stored in compliance with data privacy regulations.


• Accountability: Businesses must be accountable for the outcomes generated by AI, whether positive or negative.

2. AI Risk Management Framework
Identifying and mitigating risks early in the AI process is critical. The risk management framework should include:

• Risk Identification: Identify potential risks related to algorithmic bias, security vulnerabilities, and unintended consequences.


• Risk Mitigation Strategies: Implement strategies to manage these risks, such as regular audits, bias detection tools, and security reviews.


• Monitoring and Evaluation: Continuously monitor AI systems to detect new risks and evaluate the effectiveness of mitigation measures.

3. Data Governance
Data is the fuel for AI, making it essential to have a governance model for managing data responsibly:

• Data Quality: Ensure that the data used in AI systems is accurate, up-to-date, and relevant.


• Data Privacy: Protect personal and sensitive data by complying with regulations like GDPR and CCPA.


• Data Security: Implement robust security measures to protect data from unauthorized access, breaches, and misuse.

4. AI Ethics Board
A key part of AI governance is having an AI Ethics Board or Committee. This board is responsible for reviewing and approving AI initiatives to ensure they meet the company’s ethical standards. The ethics board should include:

• AI Experts: Technologists who understand the technical aspects of AI.


• Legal Advisors: Lawyers who specialize in data privacy, intellectual property, and regulatory compliance.


• Ethics Experts: Professionals with expertise in ethical frameworks and standards.


• Business Stakeholders: Executives and managers who represent the business side of AI implementations.

5. AI Model Audits and Monitoring
Continuous auditing of AI models is necessary to ensure they remain effective and unbiased over time. Audits should include:

• Bias Audits: Regular checks for algorithmic bias or discrimination.


• Performance Audits: Ensure that AI models are delivering the expected outcomes and are not degrading over time.


• Compliance Audits: Verify that AI models comply with industry regulations and data protection laws.

6. Training and Awareness
For AI governance to be effective, all employees must be educated on the company’s AI policies and principles. Training programs should include:

• Ethical AI Training: Teach employees how to use AI responsibly and ethically.


• Data Handling: Educate staff on proper data collection, storage, and usage practices.


• Regulatory Awareness: Ensure employees are aware of data privacy and AI-related regulations.

7. Continuous Improvement and Feedback Loops
AI governance should be a dynamic process that evolves with changing technologies and regulations. Establish a feedback loop that allows stakeholders to provide input on AI systems and suggest improvements. This continuous improvement process ensures that AI governance remains relevant and effective.


How Do I Implement an AI Governance Model?
Now that we’ve outlined the components of an AI governance framework, let’s take a look as some of the key steps to implement it effectively.

Step 1: Define Governance Objectives
The first step is to define clear objectives for your AI governance model. These objectives should align with your overall business goals and address the ethical, legal, and operational risks of AI. Examples of objectives include ensuring transparency, reducing bias, and achieving regulatory compliance.

Step 2: Establish an AI Governance Committee
Create a cross-functional AI governance committee that includes AI experts, legal advisors, ethics specialists, and business stakeholders. This committee will be responsible for overseeing the implementation and monitoring of AI governance policies.

Step 3: Develop AI Governance Policies and Guidelines
Work with your AI governance committee to develop policies and guidelines that will govern the ethical use of AI in your business. These policies should cover areas such as data privacy, transparency, fairness, and accountability.

Step 4: Implement Risk Management Processes
Establish a risk management process that includes risk identification, mitigation strategies, and continuous monitoring. Ensure that the governance committee is involved in regularly reviewing and updating risk management practices.

Step 5: Conduct Audits and Monitoring
Regularly audit your AI models to ensure compliance with your governance policies. This includes bias audits, performance checks, and legal compliance audits. Monitoring should be continuous, with feedback mechanisms in place to allow stakeholders to report concerns.

Step 6: Train Employees
Conduct training sessions for employees across the business to ensure they understand the AI governance policies and their role in upholding them. Include both technical and non-technical staff in these sessions.

Step 7: Review and Improve
AI governance is not a one-time effort. Schedule regular reviews of your governance policies, risk management processes, and AI model audits. Continuously seek input from stakeholders to refine and improve your governance model.


Are There Other Companies That Have Implemented AI Governance?
Several companies have successfully implemented AI governance models, and we can study them to gain valuable lessons. Let's take a look at a few well-known companies now:

1. Google
Google introduced its AI governance framework after facing public scrutiny for some of its AI projects. The company has an AI Principles framework that focuses on avoiding harmful applications of AI, reducing bias, and ensuring transparency. Google’s AI Ethics Board oversees the implementation of these principles across its product offerings.

Impact: Google’s governance framework has enabled the company to navigate complex ethical challenges, particularly around AI-powered services like facial recognition and autonomous systems.

2. IBM
IBM has a comprehensive AI governance model that includes an AI Ethics Board responsible for reviewing AI projects. IBM emphasizes transparency and accountability, providing explainability tools to ensure AI models are understandable to end-users. The company has also developed open-source AI Fairness 360, a toolkit to help businesses detect and mitigate bias in AI.

Impact: IBM’s governance model has improved trust with clients, enabling the company to expand its AI services in regulated industries like healthcare and finance.

3. Deloitte
Deloitte implemented its AI governance framework to guide the development and deployment of AI across its consulting services. The framework emphasizes risk management, regulatory compliance, and transparency. Deloitte has also invested in training its staff on ethical AI practices.

Impact: Deloitte’s governance model has helped the company develop responsible AI solutions for its clients, strengthening its reputation in the marketplace.


In Summary
For small businesses, AI governance is no longer a luxury—it’s a necessity. As AI becomes increasingly integrated into operations, governance frameworks ensure that AI is used ethically, transparently, and responsibly. By following a structured governance model, businesses can not only mitigate risks but also unlock the full potential of AI in a sustainable manner.

Establishing a clear governance policy, creating risk management strategies, forming an AI Ethics Board, and regularly auditing AI models are all steps toward effective AI governance. Real-world examples like Google, IBM, and Deloitte show that AI governance is key to building trust and long-term success in the AI space. By prioritizing governance, small businesses can confidently harness the power of AI to drive innovation while protecting their stakeholders.

What do you think? Was this governance overview helpful? I know it was a lot, so you may need to read through it a few times before it all sinks in. Also, remember that you don't have to implement everything in one pass. Implement the most important pieces for your specific situation and add the rest as needed.

Did you have a major fumble with AI and realize that you need help implementing AI Governance? Perhaps you'd like to be proactive and implement governance before your first major AI initiative? Check out FailingCompany.com to find the help that you need. Go sign up for an account or log in to your existing account and start working with someone today.

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Our focus was on execution last week. We took a look at some ways that businesses used AI to achieve better business results. Executing and leveraging AI really advanced those businesses. However, we all know that implementing something new isn't always an instant success. There are always setbacks and lessons learned. That brings us to today's topic.

Pitfalls can occur in every major project, whether it's AI or otherwise. We've already covered both Ethical and Legal considerations when implementing AI. Pitfalls can definitely be found in both of these areas. However, pitfalls can easily occur in a way that stops you short of your goals while still being both ethical and legal.

Let's turn to a different AI engine today to get some thoughts on potential pitfalls. Microsoft Copilot is a competitor to ChatGPT. Let's test it out today and see if it will give us some insight into major pitfalls that can occur when implementing AI. Below is the prompt that I gave Copilot:

"Act as an AI implementation expert for small businesses with 500 employees or less. In 1,500 words, describe the major pitfalls of implementing AI for the first time in a small business. Provide specific examples with real companies where available."

"Implementing AI in a small business can be transformative, but it comes with its own set of challenges and pitfalls. Here are some of the major pitfalls to be aware of, along with specific examples from real companies where available:

1. Lack of Proper Training and Education
One of the most common pitfalls is the lack of proper training and education on AI technology. Small businesses often underestimate the complexity of AI and fail to invest in adequate training for their employees. This can lead to misuse or underutilization of AI tools.

Example: A small retail company implemented an AI-based inventory management system but did not provide sufficient training to its staff. As a result, employees struggled to use the system effectively, leading to stockouts and overstock situations that hurt the business.

2. Failure to Identify Appropriate AI Solutions
Choosing the right AI solution that aligns with the specific needs and objectives of the business is crucial. Many small businesses fall into the trap of adopting AI technologies that are either too advanced or not suitable for their operations.

Example: A small marketing firm adopted a sophisticated AI-driven customer relationship management (CRM) system. However, the system was too complex for their needs, leading to frustration among employees and ultimately, the abandonment of the system.

3. Insufficient Data for Effective Implementation
AI systems rely heavily on data to function effectively. Small businesses often lack the volume and quality of data needed to train AI models, which can result in poor performance and inaccurate predictions.

Example: A small e-commerce business tried to implement an AI-based recommendation engine. However, due to insufficient historical sales data, the recommendations were often irrelevant, leading to a poor customer experience and decreased sales.

4. High Costs and Resource Constraints
Implementing AI can be expensive, and small businesses often operate with limited budgets. The costs associated with AI implementation, including software, hardware, and skilled personnel, can be prohibitive.

Example: A small manufacturing company invested heavily in AI-driven predictive maintenance tools. However, the high costs of implementation and ongoing maintenance strained their budget, leading to financial difficulties.

5. Integration Issues
Integrating AI systems with existing business processes and technologies can be challenging. Small businesses may face difficulties in ensuring seamless integration, which can disrupt operations and reduce efficiency.

Example: A small logistics company implemented an AI-based route optimization tool. However, the tool did not integrate well with their existing fleet management system, leading to operational inefficiencies and increased delivery times.

6. Ethical and Bias Concerns
AI systems can inadvertently perpetuate biases present in the training data, leading to unfair or unethical outcomes. Small businesses must be vigilant in ensuring that their AI systems are fair and unbiased.

Example: A small recruitment agency used an AI-driven hiring tool to screen candidates. However, the tool was found to be biased against certain demographic groups, leading to discriminatory hiring practices and legal challenges.

7. Over-Reliance on AI
While AI can automate many tasks, over-reliance on AI can be detrimental. Small businesses should strike a balance between automation and human oversight to ensure optimal outcomes.

Example: A small customer service company implemented an AI chatbot to handle customer inquiries. However, the chatbot struggled with complex queries, leading to customer dissatisfaction. The company had to reintroduce human agents to handle more complicated issues.

8. Lack of Clear Objectives and KPIs
Without clear objectives and key performance indicators (KPIs), it is difficult to measure the success of AI implementation. Small businesses must define their goals and metrics to evaluate the effectiveness of AI initiatives.

Example: A small healthcare provider implemented an AI-based patient management system but did not set clear objectives or KPIs. As a result, they struggled to assess the system’s impact on patient care and operational efficiency.

9. Security and Privacy Concerns
AI systems often handle sensitive data, making them a target for cyberattacks. Small businesses must ensure robust security measures to protect their data and maintain customer trust.

Example: A small financial services firm implemented an AI-driven fraud detection system. However, inadequate security measures led to a data breach, compromising sensitive customer information and damaging the firm’s reputation.

10. Resistance to Change
Employees may resist adopting new AI technologies due to fear of job displacement or discomfort with new tools. Small businesses must manage change effectively to ensure smooth adoption.

Example: A small accounting firm introduced an AI-based bookkeeping tool. However, employees were resistant to using the new system, fearing it would replace their jobs. The firm had to invest in change management initiatives to address these concerns and facilitate adoption.

Conclusion
Implementing AI in a small business can offer significant benefits, but it is essential to be aware of the potential pitfalls. By addressing these challenges proactively, small businesses can harness the power of AI to drive growth and innovation."

What is your initial response to Copilot's response? I personally had two initial thoughts. The first is that Copilot doesn't seem to be quite as detailed as ChatGPT in describing each of the ten pitfalls. That shows that you may have to do some prompt engineering to get an optimal response for whichever Large Language Model (LLM) you are using OR you may want to test multiple models. More specifically to the subject of today's post, we've hit on several of these in previous posts when diving into specific business functions and how to leverage AI.

Hopefully lack of proper training and education is an obvious pitfall at this point. You can have the best AI solution in place, but it's of little use if your employees have no idea have to used it. Given that, let's move on to item number two on the list.

It's really easy to read about another company implementing an impressive AI solution and begin thinking to yourself that you need that same solution in your company. We must always remember that AI is just a tool in the toolbox. A very powerful tool, but a tool, nevertheless. You must always choose the right tool for the job!

Always start with your specific business problem or opportunity in mind. Make sure it's well defined and aligned to your strategic objectives. Then, and only then, should you evaluate potential AI solutions to determine the most appropriate solution for your needs. This is fundamental and absolutely critical. Think of it as a commandment that shall not be broken!!

I believe that items four and five above, high costs / resource constraints and integration issues, are really a symptom of item two, failure to identify appropriate AI solutions. Implementing an AI system that costs too much to implement and/or run means that it was never a good fit for your business and specific business needs. Likewise, choosing an AI solution that won't integrate with your existing systems is a significant failure because you're left with a fragmented ecosystem that now has some AI engine sitting off to the side running in a vacuum. Be diligent in fully laying out what you expect from an AI solutions before shopping around!

I've said before that data is the fuel for your AI machine. It's very common to hear conversations, even at very large corporations, about implementing new AI capabilities with absolutely no discussion about the data needed to feed the engine. Data can be messy, confusing and a little boring to talk about, so most people want to dismiss it as a trivial task that they will address when they implement the AI solution. This is simply a recipe for disaster. You must ensure that you have the necessary data, in the right structure and of the appropriate data quality level before implementing anything relating to AI. Think of this as the second commandment that shall not be broken!

We've hit on ethical issues and bias a lot recently, so let's skip that and jump to over reliance on AI. A lot of people think they can implement an AI solution, and their business will be on autopilot. They can spend their days traveling to exotic locations while money magically hits their bank account. However, AI is not like a thermostat. You can't just set it and forget it. You must have proper oversight to make sure AI isn't generating false result, hallucinations, or otherwise going off the rails. Be willing to put in the work to learn how to run the AI solution before it mistakenly runs your business into the ground!

To me, lack of clear objectives and KPIs is the sibling or close cousin to the failure to identify appropriate solutions. You must have a clearly defined problem or business need identified before picking an AI solution. This means that you have a clear picture of what the end state looks like. This is your destination on the map.

Progress towards this end state can then be tracked by goals & objectives, KPIs OKRs, etc. These are the places that you'll stop at on the way to your final destination. As such, they are critical measures to monitor if your new AI solution is actually moving you closer to that final destination. If not, then it's time to course correct. If these aren't defined ahead of time, then it's like setting sail from the US to Ireland without plotting a course first. You'll likely wind up lost at sea or someplace far from Ireland!

Security and privacy issues can always be pitfalls, and we discussed these in depth during the legal and ethical considerations posts. No need to belabor the point here. Make them a top priority and keep yourself out of hot water!

We've also covered resistance to change in previous posts. However, this one shouldn't be underestimated. Many leaders have the attitude that their employees will simply adopt the AI technology because they said so. If they don't like it, then they can find another job.

Needless to say, this is a terrible approach to running a company, much less implementing AI. You may wind up with nearly 100% turnover and find yourself training brand new staff on your business AND the new AI solution. Talk about a huge setback! Be sure to keep your staff in lockstep with you on this new AI journey!

What do you think? Did Copilot give good suggestions of pitfalls to avoid when implementing AI? What would you add to the list if you were making it? I think they are good pointers but, like anything in business, I wouldn't blindly accept them without verifying first.

As a double check, I ran my same prompt through ChatGPT and through Claude, another LLM. There were definitely some similarities in all three, but it's interesting to look at the suggestions that were unique to each model. It helps you to get a more comprehensive view of the pitfalls to avoid than if you just depended on one model. Copy my prompt above and go test the models for yourself!

Have you fallen victim to one or more of these pitfalls on your AI journey? Maybe this article helped you to narrowly avoid a major pitfall, and you now think you need a helping hand? Check out FailingCompany.com to find the help that you need. Go sign up for an account or log in to your existing account and start working with someone today.

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What separates a successful business owner from a failed business owner? Depending on your past experience and area of expertise, you may have a lot of ideas. Perhaps it's better marketing? A better sales team? More responsive customer service? Pure luck? There are lots of possible answers, but I believe they can all be distilled down into one common theme.

Execution is the key. A successful business owner executes well. They act on good ideas and bring them to market. They then work to build streamlined processes that execute consistently. The sole focus is to put useful products and services into the hands of their customers as quickly, efficiently and as often as possible.

Failed business owners don't focus on the basics. They may have a multi-million-dollar idea but never act on it. They may have ways to streamline their processes but keep using the manual and cumbersome processes instead. They may even have ways to make their existing products and services more useful to the customer but decided to leave good enough alone.

Yes, I believe execution is the key. This doesn't change with AI, either. It doesn't matter how many ideas you have to implement AI in your business. It only matters when you actually implement them. Likewise, implementing AI in an area of your business that doesn't improve execution, either directly or indirectly, won't matter. It'll be a waste of time and money. Focus on improving execution!

Given that, let's take a look at some mini case studies on businesses that successfully improved execution with AI. I've provided examples throughout this AI miniseries, but this time the focus will be on the business more than the way the AI was implemented. I provided ChatGPT the following prompt:

"Act as a researcher who is writing a case study on the use of AI in small businesses to improve business execution. Write a 1,500 word case study on real businesses who have successfully implemented AI that resulted in improved business performance. Focus on businesses with 500 or less employees. The businesses can be in any industry."

"Case Study: The Successful Implementation of AI in Small Businesses to Improve Business Execution

Introduction
In today’s rapidly evolving business landscape, small and medium-sized enterprises (SMEs) are turning to artificial intelligence (AI) to streamline operations, enhance customer experiences, and drive growth. With increasing accessibility to AI tools and platforms, even businesses with 500 or fewer employees can leverage AI solutions to gain a competitive edge. This case study examines the real-world implementation of AI by small businesses across various industries and explores the tangible improvements in business performance. The case study highlights the successes of three companies: Blue River Technology, Cognoa, and Levity AI, showcasing how AI has been a transformative force in business execution.

Case 1: Blue River Technology (Agriculture)

Company Overview: Blue River Technology, an agricultural technology company based in California, was founded with the aim of integrating AI to solve the challenges of large-scale farming. With fewer than 200 employees, the company specializes in smart farming solutions to reduce pesticide usage and improve crop management. By leveraging AI and machine learning, Blue River created the “See & Spray” technology, which automates the identification and spraying of weeds.

AI Implementation: Blue River developed a machine vision platform that uses deep learning algorithms to identify weeds and selectively spray herbicide, reducing the use of chemicals on farms. The AI system processes real-time data captured by cameras attached to farm equipment, recognizing the difference between crops and weeds. The solution is fully integrated with precision agriculture systems, where the AI processes vast amounts of data to make more informed decisions about planting and cultivation.

Impact on Business Execution: The implementation of AI reduced herbicide use by up to 90%, significantly lowering the cost of chemicals and reducing the environmental impact. Farmers experienced better yields due to improved crop health, as less chemical intervention meant fewer harmful side effects on plants. Additionally, the AI solution reduced labor costs by automating manual weed-pulling tasks, allowing workers to focus on more skilled agricultural tasks.

For Blue River Technology, integrating AI not only positioned the company as an industry innovator but also drove substantial business growth. The company was later acquired by John Deere in 2017 for $305 million, signifying the success of their AI-driven product in improving efficiency in agriculture.

Lessons Learned:

• Strategic Alignment: By focusing on a real pain point in agriculture—excessive herbicide use—Blue River effectively deployed AI to solve a targeted issue.


• Operational Efficiency: Automating previously manual processes led to measurable improvements in operational efficiency and cost reduction.


• Scalability: The AI solution was scalable and could be adapted to various types of crops, enabling growth beyond initial deployments.

Case 2: Cognoa (Healthcare)

Company Overview: Cognoa is a small healthcare technology company specializing in AI-driven solutions for early childhood development. The company, with around 100 employees, focuses on using AI to help parents and pediatricians detect developmental delays and autism spectrum disorder in children earlier than traditional methods allow.

AI Implementation: Cognoa developed an AI-powered diagnostic tool that uses machine learning algorithms to analyze behavioral data from children. This data comes from both parent reports and clinical observations. The AI system compares a child’s behavior with large datasets of children with known developmental patterns, allowing the system to flag potential developmental disorders.

The AI tool is FDA-approved and has the ability to assist in the diagnostic process, acting as a decision support system for pediatricians. Additionally, the system continuously learns from new data to improve its accuracy.

Impact on Business Execution: The introduction of AI drastically improved Cognoa's ability to offer quick, accurate diagnoses at a lower cost compared to traditional methods. For pediatricians, it reduced the time spent evaluating patients and eliminated some of the subjectivity in diagnosing developmental delays. As a result, Cognoa became a preferred partner for pediatricians, increasing its market share.

The healthcare industry’s rigorous regulatory standards present challenges for businesses integrating AI, but Cognoa’s tool met these standards, enabling the company to grow while maintaining high compliance levels. In addition to operational efficiency, AI helped Cognoa extend its services to more patients through remote consultations, expanding its reach beyond local medical practices.

Lessons Learned:

• Regulatory Compliance: AI can be successfully integrated into heavily regulated industries, provided the company adheres to industry-specific guidelines.


• Improved Accuracy: AI algorithms can reduce human error and variability in decision-making, offering more consistent and accurate outcomes.


• Remote Access: AI-enabled solutions that work remotely can help businesses scale their operations to new markets and customer segments.

Case 3: Levity AI (Retail and Customer Support)

Company Overview: Levity AI is a small German-based company with fewer than 50 employees, focusing on automating repetitive tasks like customer support, email management, and document classification. Levity’s AI platform allows businesses to build custom workflows that automatically process and categorize large volumes of data without requiring advanced coding skills.

AI Implementation: Levity AI uses machine learning models that help businesses automate routine tasks such as sorting emails, categorizing customer support tickets, and analyzing customer feedback. For example, retail companies using Levity can automatically classify thousands of customer service emails and forward them to the appropriate team. The system learns from each interaction, improving its classification accuracy over time.

The platform provides no-code tools, which means businesses without technical expertise can easily create AI models suited to their specific workflows. This democratizes AI usage and allows smaller companies to leverage machine learning without needing in-house data science teams.

Impact on Business Execution: Levity AI’s clients, including small retail businesses, have reported significant time savings by automating routine tasks such as processing customer support queries and handling product returns. By reducing the need for manual intervention, Levity’s AI tools helped companies cut operational costs and improve response times.

One notable example is a mid-sized e-commerce company that reduced its customer support response time by 30%, improving customer satisfaction. The business also reallocated staff previously handling mundane tasks to focus on more strategic work, such as improving customer experience and launching new product lines.

Lessons Learned:

• No-Code Solutions: Offering no-code AI platforms opens doors for small businesses that lack in-house technical expertise to adopt AI solutions.


• Time Savings: Automating repetitive tasks allows businesses to focus on more value-added activities, enhancing overall productivity.


• Customer Experience: AI’s ability to improve response times and service delivery can have a significant impact on customer satisfaction and loyalty.

Common Themes Across Case Studies
While each of the companies discussed in this case study operates in a different industry, several common themes emerged regarding the successful implementation of AI to improve business execution:

Targeting Specific Pain Points: All companies effectively identified and addressed specific operational inefficiencies or business challenges that could be solved with AI. Whether it was reducing chemical usage in agriculture, improving diagnostic accuracy in healthcare, or automating routine customer service tasks, each solution was targeted to create measurable improvements.

Operational Efficiency and Cost Savings: Across the board, AI solutions improved operational efficiency, leading to significant cost savings. Blue River Technology reduced herbicide costs by 90%, while Levity AI’s clients saw substantial reductions in labor costs related to customer support. This operational efficiency translates directly to business performance, allowing companies to reinvest resources into growth areas.

Scalability: Each company developed AI solutions that were scalable. Blue River’s technology could be applied to different types of crops, while Cognoa’s tool scaled to meet the needs of a broader patient population through remote diagnosis. Scalability is crucial for small businesses looking to grow without significantly increasing operational costs.

Improved Customer/Client Experience: Whether it’s the farmers using Blue River’s technology or customers of the e-commerce company working with Levity AI, the adoption of AI led to improved service delivery. Customers benefitted from faster response times, better product outcomes, and enhanced service quality, increasing customer satisfaction and loyalty.

No Need for Technical Expertise: Companies like Levity AI that provide no-code solutions make it possible for smaller businesses with limited technical resources to take advantage of AI. This accessibility allows even non-technical business owners to streamline operations using AI-powered workflows.

Conclusion
AI has proven to be a transformative tool for small businesses across various industries, helping to improve business execution, reduce costs, and drive growth. As seen in the case studies of Blue River Technology, Cognoa, and Levity AI, the key to successful AI implementation lies in targeting specific business challenges, ensuring scalability, and focusing on customer-centric improvements. By adopting AI, small businesses can enhance their operational efficiency, offer better customer experiences, and scale more effectively—ultimately leading to better business performance.

For small and medium-sized enterprises, AI is no longer a futuristic technology reserved for large corporations. Instead, it is an accessible and practical tool that can lead to meaningful improvements, even for companies with fewer than 500 employees. The growing availability of no-code AI platforms, such as those offered by Levity, combined with the potential for automation and efficiency gains, makes AI an attractive investment for SMEs looking to stay competitive in an increasingly digital marketplace."

What did you think of the ChatGPT response? Not that long ago, an article like this would have required hours of research and multiple drafts to get ready to publish! That observation aside, I found it fascinating to see how three very different industries (agriculture, healthcare and retail) were able to leverage the power of AI. Though vastly different businesses, there were certainly some common themes.

Each business focused on addressing some unmet need. Blue River Technology was able to greatly reduce the use of pesticides, which saves farmers significant money and may result in healthier crops. Cognoa was able to help parents and their pediatricians discover possible developmental issues earlier in life, thus allowing them to initiate proper treatment as soon as possible. Finally, Levity was able to help its customers streamline low value tasks and allow employees to be redeployed to more strategic work. All three businesses filled a need and delivered tangible value.

These businesses were a prime example of the importance of execution. None of these companies sat on their ideas. They brought them to market and turned them into thriving products and services. That's execution at it's finest. For each of the companies above, there are probably 10s or 100s of companies just like them that sat on the same or similar idea and chose to do nothing with it. Who was the winner and who was the loser?

The last thing that I liked about this article is that all three of the businesses were small businesses. These weren't multibillion dollar mega corporations. They weren't found on the Fortune 500 list. The combined total of all three companies was only around 350 employees. Yet their impact was HUGE. The size of your company no longer matters. Combine the proper application of AI with strong and consistent execution and you can move mountains!

Did one of these businesses inspire you to act on an AI-based idea that you've been sitting on? Perhaps you did execute and need some help managing your newfound exponential business growth? Check out FailingCompany.com to find the help that you need. Go sign up for an account or log in to your existing account and start working with someone today.

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We dug into some important ethical considerations of AI last week. This is a real concern for businesses when implementing AI. Have you heard before that something might be legal, but not ethical? Well, there are also times when something may be considered ethical, but not necessarily legal (such as slightly speeding to get someone dying of a heart attack to the hospital). Then there are the times when things are both unethical and illegal. What this really tells us, unsurprisingly, is that legal considerations are right up there with ethical consideration. Slightly higher in most cases.

Handing over the reins to AI can be risky if you don't do your homework. You need to know what the AI model is actually doing, and you need to know the implications of using it. This includes understanding the legal considerations. The last thing you want to do is end you up in a lawsuit or, worse yet, being investigated by government agency. Either way, you can be drug through the mud on social media as well. To that end, let's turn to ChatGPT to discover some of the major legal considerations that we should know about:

"Legal Considerations for Implementing AI in Small Businesses

Artificial Intelligence (AI) is transforming the way businesses operate, offering enhanced efficiency, better customer insights, and automation of mundane tasks. However, the adoption of AI comes with a set of legal considerations that small business owners must carefully navigate to avoid potential pitfalls. This article will explore the key legal issues small businesses should consider when implementing AI, including data privacy, intellectual property, liability, transparency, discrimination, and regulatory compliance. We will also provide specific examples to illustrate these points.

1. Data Privacy and Security
One of the most critical legal considerations when implementing AI is data privacy. AI systems often rely on vast amounts of data to function effectively, including personal data from customers, employees, and other stakeholders. This reliance raises significant concerns about data protection and privacy.

Key Considerations:

• Compliance with Data Protection Laws: Small businesses must ensure that their use of AI complies with relevant data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States, or other local privacy laws. These laws require businesses to obtain consent before collecting personal data, inform individuals about how their data will be used, and provide rights for individuals to access, correct, and delete their data.


• Data Minimization: Businesses should adopt data minimization principles, collecting only the data necessary for the AI system to function. Excessive data collection can increase the risk of data breaches and non-compliance with privacy laws.


• Data Security: Implementing robust data security measures is essential to protect sensitive information from unauthorized access, breaches, and cyberattacks. Businesses should consider encryption, secure data storage, and regular security audits.

Example:
A small online retail business uses AI to analyze customer purchasing behavior to offer personalized recommendations. To comply with data protection laws, the business must obtain customer consent before collecting and analyzing their data, provide a clear privacy policy, and implement security measures to protect the data.

2. Intellectual Property (IP) Issues
AI can create new content, processes, and innovations, leading to questions about intellectual property rights. Small businesses must consider how to protect their AI-related intellectual property and avoid infringing on others' rights.

Key Considerations:

• Ownership of AI-Created Works: Determining who owns the intellectual property rights to creations generated by AI can be complex. If a small business uses an AI tool to generate content, it must clarify ownership rights with the AI provider. The business should also establish whether the AI-generated content can be copyrighted.


• Licensing Agreements: When using third-party AI tools, businesses should review the licensing agreements to understand the scope of use, ownership rights, and any restrictions. This ensures that the business does not inadvertently violate the terms of use.


• Patentability of AI Innovations: If a small business develops a new AI algorithm or application, it may seek patent protection. However, obtaining a patent for AI-related inventions can be challenging due to issues such as the novelty requirement and the abstract nature of algorithms.

Example:
A small marketing agency uses an AI tool to generate unique social media content for clients. To protect its intellectual property, the agency should ensure that the AI-generated content is original and does not infringe on existing copyrights. The agency should also clarify with the AI provider whether the agency or the provider owns the rights to the generated content.

3. Liability and Accountability
AI systems can make autonomous decisions, leading to questions about liability and accountability when things go wrong. Small businesses must consider who is responsible if an AI system causes harm or makes a mistake.

Key Considerations:

• Liability for AI Decisions: Determining liability can be complex, especially when AI systems make autonomous decisions without human intervention. Businesses must establish clear guidelines for liability and accountability, especially if the AI system causes harm to individuals or property.


• Product Liability: If a small business sells or deploys AI-based products, it must consider product liability issues. The business could be held liable if the AI product malfunctions or causes harm due to defects, lack of proper warnings, or inadequate user instructions.


• Contractual Liability: Businesses should include liability clauses in contracts with AI vendors or service providers to define responsibility for errors or failures. This may include indemnification clauses to protect the business from third-party claims.

Example:
A small healthcare provider uses an AI-powered diagnostic tool to assist doctors in diagnosing patients. If the AI system provides an incorrect diagnosis, resulting in harm to a patient, the healthcare provider could face liability issues. The provider must ensure that the AI system is reliable, thoroughly tested, and that clear protocols are in place for human oversight and verification of AI-generated recommendations.

4. Transparency and Explainability
AI systems can often function as "black boxes," making decisions based on complex algorithms that are difficult to understand. Small businesses must consider the importance of transparency and explainability in their AI applications, especially when those decisions impact customers or employees.

Key Considerations:

• Explainability Requirements: Some industries and jurisdictions require businesses to provide explanations for AI-generated decisions, especially when those decisions have legal or significant personal effects. For example, financial institutions using AI for credit scoring may need to explain the factors influencing credit decisions.


• Building Trust: Transparency and explainability are essential for building trust with customers and stakeholders. Businesses should strive to make their AI processes understandable and provide clear information about how decisions are made.


• Auditing and Accountability: Businesses should implement mechanisms for auditing AI systems to ensure they operate as intended. This includes regular reviews, documentation, and the ability to trace decision-making processes.

Example:
A small insurance company uses AI to assess risk and set insurance premiums for customers. To comply with transparency requirements, the company should provide customers with clear explanations of how their data is used, the factors that influence premium calculations, and how they can contest or appeal decisions.

5. Discrimination and Bias
AI systems can inadvertently perpetuate or amplify biases present in the training data, leading to discriminatory outcomes. Small businesses must be aware of the potential for bias and take steps to mitigate it.

Key Considerations:

• Bias in Training Data: AI systems learn from the data they are trained on. If the training data contains biases, the AI system may produce biased or discriminatory results. Businesses must carefully select and preprocess training data to minimize bias.


• Testing and Monitoring: Regular testing and monitoring of AI systems are essential to identify and address bias. Businesses should implement procedures to detect biased outcomes and adjust the AI models as needed.


• Non-Discrimination Policies: Businesses should establish non-discrimination policies for AI applications, ensuring that decisions are fair and do not disproportionately impact protected groups based on race, gender, age, or other characteristics.

Example:
A small HR firm uses AI to screen job applicants. If the AI system is trained on historical hiring data that contains biases (e.g., favoring certain genders or ethnicities), it may produce biased hiring recommendations. The firm must ensure that the AI system is trained on diverse and representative data, and regularly audited for bias, to promote fair hiring practices.

6. Regulatory Compliance
As AI adoption increases, so does regulatory scrutiny. Small businesses must stay informed about current and emerging regulations that impact AI use in their industry and jurisdiction.

Key Considerations:

• Industry-Specific Regulations: Different industries may have specific regulations governing AI use. For example, the healthcare, financial services, and automotive industries may have stricter regulatory requirements for AI due to the potential impact on safety and public welfare.


• Emerging Legislation: Governments worldwide are considering new regulations to address AI-related issues. Businesses should stay updated on legislative developments and prepare to comply with new rules as they emerge.


• Cross-Border Considerations: Small businesses operating internationally must be aware of regulatory differences between jurisdictions and ensure compliance with local laws where they operate.

Example:
A small fintech startup uses AI to provide automated financial advice. The company must comply with financial regulations that govern the use of AI in providing financial services, including regulations on data protection, transparency, and consumer protection. The startup should work with legal experts to navigate the complex regulatory landscape.

7. Ethical Considerations
In addition to legal considerations, ethical considerations play a crucial role in AI implementation. Small businesses should strive to implement AI ethically to build trust with customers, employees, and stakeholders.

Key Considerations:

• Fairness: Businesses should ensure that AI systems make fair and unbiased decisions, avoiding discrimination and promoting inclusivity.


• Privacy: Respecting individuals' privacy rights is essential, even beyond legal requirements. Businesses should prioritize data privacy and give customers control over their personal information.


• Accountability: Businesses should take responsibility for the outcomes of their AI systems, ensuring that there are mechanisms for redress if things go wrong.


• Transparency: Clear communication about AI use, data collection, and decision-making processes helps build trust and transparency with stakeholders.


Example:
A small e-commerce company uses AI to personalize marketing campaigns. To implement AI ethically, the company should ensure that its marketing practices do not exploit customer vulnerabilities or engage in manipulative tactics. It should also be transparent about how customer data is used for personalization.

Conclusion
Implementing AI can offer small businesses significant advantages, including improved efficiency, customer insights, and competitive edge. However, it also comes with legal and ethical responsibilities that cannot be overlooked. By understanding and addressing the legal considerations of data privacy, intellectual property, liability, transparency, bias, regulatory compliance, and ethics, small businesses can implement AI in a way that is both legally compliant and socially responsible.

Small businesses should work closely with legal professionals to navigate these complexities, stay updated on evolving regulations, and ensure that their AI initiatives align with both legal requirements and ethical standards. In doing so, they can harness the power of AI while minimizing risks and fostering trust with their customers and stakeholders."

We tackled ethical considerations last week, so we'll put that aside this week. With that out of the way, let's take a look at some of the others. Did you know that there are data privacy laws? People usually think of HIPAA laws in healthcare but there are others too. You need to be aware the state and local laws of where your customers reside. This is easy if you run a brick-and-mortar business but can get incredible difficult if you're an online business. Adding to the complexity are laws from other countries! Going global is great but be aware of the laws in those countries!

Intellectual property rights are nothing new. In the past, these were easy to protect by getting patents, copyrights, trademarks, etc. These issues haven't been fully vetted in the new world of AI. If you're not careful, a competitor may be able to replicate your AI capabilities, and you'll have little to no recourse. Worse yet, you may blindly rely on the AI solution and discover that you inadvertently infringed on someone else's intellectual property rights. This may land you in a nasty lawsuit!!

Liability should really be an underlying theme to all of the other items on the list but is definitely worthy of evaluating on its own as well. Making a wrong decision or taking a wrong action because of an AI model can be dangerous. Using an AI model doesn't transfer risk to the model developer or eliminate liability in some other way. You're still liable, so you better know what the model is doing! That leads us to explainability.

Transparency and explainability is a hot topic in the world of machine learning. Companies must be able to explain what their models do. This is particularly important in heavily regulated industries or during an audit. More broadly, though, is being able to defend a decision when challenged. The example of using AI for credit scoring is a great one. Someone may challenge the decision, and you don't want to find yourself in a position where you can't articulate why the model gave the answer.

I'm sure you'd agree that avoiding real or perceived discrimination and bias is always a top priority, whether AI is used or not. You probably work very hard to keep your business practices on the up and up. Fairness and equality are the only way to do that. Be careful to ensure that the AI model that you implement doesn't end up ruining that stellar reputation that you worked so hard to build. You must always know what your model is doing!!

Were any of these a surprise to you? None of these should scare you off from implementing AI. Rather, it should encourage you to educate yourself on what you're implementing. The goal is to keep you out of legal hot water and the associated negative press. Knowledge is power, so go build your AI knowledge and then implement that amazing AI solution!

Did you get a little too excited about AI and find yourself cleaning up a mess after a legal dispute? Perhaps you're trying to understand all of the legal risks of an AI solution that you're considering implementing? Check out FailingCompany.com to find the help that you need. Go sign up for an account or log in to your existing account and start working with someone today.

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