AI Implementation Pitfalls
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."
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.
#FailingCompany.com #SaveMyFailingCompany #ArtificialIntelligence #PitfallsToAvoid #SaveMyBusiness #GetBusinessHelp
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.
#FailingCompany.com #SaveMyFailingCompany #ArtificialIntelligence #PitfallsToAvoid #SaveMyBusiness #GetBusinessHelp