Common Errors to Avoid when using generative AI in your business
Thanks to Anthropic, Google, Microsoft and OpenAI, among others, a new artificial intelligence (AI) era is launching a frenetic scramble to remain competitive. Businesses are racing to deploy innovative related technologies—including generative models that create (or “generate”) content based on the training and prompts received—to improve accuracy and performance, reduce costs and avoid being left behind.
Unfortunately, many AI projects are failing, some with significant errors and others with tragic effects. Although seemingly everyone—from Amazon to Wendy’s—is employing AI in creative and compelling ways to better serve customers and manage expenses, AI initiatives are not cookie-cutter-like endeavors. The initiatives, similar to traditional projects, must be well planned and managed to succeed.
Here are 10 common errors to avoid when deploying a generative AI solution within your business.
1. Failing to understand how AI chatbots work
AI-powered chatbots, such as Bard and ChatGPT, are easily misunderstood. These innovative AI technologies are incredibly powerful tools that can be employed in myriad varying ways. But make no mistake. These chatbot technologies are not applications you download and install to begin using.
Incorporating AI chatbots within new and existing workflows and solutions requires far more than just connecting a few application programming interfaces (APIs) and getting underway. Firms must study and understand the ways these generative AI platforms are constructed, learn and operate. The process can require tapping skillsets new to the organization, which brings us to error number two.
2. Lacking required AI expertise
There will be few candidates submitting resumes and seeking new AI roles who boast decades of generative chatbot experience and expertise. Even technology veterans who have spent years working with AI technologies will have little experience with new AI chatbot tools that are still largely in development and just reaching widespread release.
Subsequently, finding the requisite talent will prove a challenge. Expect fierce competition, too, for those professionals with relevant AI experience. But it is important firms possess the AI and machine learning (ML) expertise necessary to assist in deploying and maintaining successful AI projects.
3. Selecting the wrong use case
While AI chatbots are compelling innovations predicted to significantly change the way organizations operate, the technologies are not automatically effective solutions for every business need. AI projects should be selected based on the business value they generate and only after ensuring the AI solution’s capabilities match well with business needs. Before an AI initiative can generate benefits, organizations must first choose appropriate use cases when considering deploying new AI tools.
In other words, AI solutions are better at some things than others. Before planning an AI deployment, do your homework first. Research how potential AI solutions are programmed, operate and interact with other technologies. Confirm a prospective innovation can actually feed accurate data properly to the people and platforms required. Read case studies and familiarize yourself with a prospective tool’s true capabilities before developing test initiatives.
4. Setting improper expectations
AI can only solve problems for which it is programmed and prepared. If you operate a business in which multiple market forces have been reducing opportunities, suppressing the numbers of clients needing your products or services and tightening margins, rolling out a new AI-powered customer service chatbot is not likely to reverse your company’s slide.
AI project expectations, like other technology endeavors, must be properly considered, set and managed. Place the bar too high and an AI project can be doomed from the start.
5. Employing the wrong success metrics
Firms should ensure they not only identify proper use cases and expectations when selecting AI technologies to power new initiatives, they must also choose and track reasonable, realistic metrics to gauge the success of the project as the solution is deployed, maintained and matures.
This fact may prove particularly frustrating for many project managers accustomed to shepherding a project though its various milestones to completion; AI projects tend to require longer periods to mature. Corresponding success metrics, and deadlines, must accommodate this fact.
The situation is particularly vexing considering the potential time required for machine learning knowledge and expertise to build and accrue and prove useful. Again, this is where knowledge and understanding how generative AI technologies work prove critical. AI initiatives require extensive monitoring and adjustment along the way if they’re to reach their true potential. But we’ll explore that issue more in just a moment.
6. Insufficient testing
In seeking expediency, organizations often speed through a project’s testing phase. As a result, an initiative’s important details are sometimes overlooked. In other cases, the wrong staff or subject matter experts (SME) might be included (or excluded, depending upon the situation) from participating in the project whose input could prove valuable in preventing (or surfacing and recognizing) errors.
Many sources recommend comparing the results of a new AI process against a control group representing standard business procedures, or the existing workflows. Failing to measure an AI effort’s performance against a control group can lead to misleading conclusions giving the AI process credit where credit may not be due (or in which the results are even better than originally anticipated).
Another risk of shortchanging testing, or even subjective testing, is source information, workflows and results cannot be properly validated. Only by dedicating the proper time and expertise to critical observing and studying a new AI initiative’s data, processes and results can an organization truly possess confidence in the new initiative’s potential.
7. Failing to properly manage risk
Multiple risks necessarily arise when preparing and deploying AI-powered projects. Errors can occur due to the fact underlying information is inaccurate or improperly structured, the actual AI analytical processes and information are improperly structured or the results are misconstrued.
For example, lending biases could be perpetuated due to errors in designing, training and managing an AI program’s decision-making processes. Such concerns are sufficiently significant as to have been incorporated within the US White House Office of Science and Technology Policy’s Blueprint for an AI Bill of Rights.
Other issues that can arise include expanding an organization’s attack threat surfaces due to exposing additional systems and data to public access, failing to properly secure potentially sensitive or proprietary information the AI solution uses or generates and improperly confirming corresponding AI datasets and decision-making models are accurate and correct. Building and implementing robust validation steps and prioritizing including the correct SMEs in AI projects are two methods for helping minimize the likelihood such errors arise.
8. Botching deployment
Bungling an AI program’s implementation is another way such ventures can fail. Improperly coordinating the AI solution’s integration with third-party software platforms is one danger, while creating poor customer experiences—such as the inability to resolve an issue or reach a human due to becoming caught within a nonsensical infinite loop—is another. Deployments can also fail due to too little or inappropriate information being fed to the model or a break down occurring with training end users and others upon whom the new AI project’s success proves dependent.
Proper project management emphasizes the importance of the planning phase, in which a comprehensive work breakdown structure is created, dependencies are identified and a requirements list is developed. Proper care and attention must be dedicated to ensuring these steps are completed properly and with as few omissions and mistakes as possible to raise the odds the project’s execution and monitoring phases complete successfully and achieve the AI solution’s desired results.
9. Returning incorrect results
Some AI mistakes are already the stuff of legend. Alphabet, Google’s parent company, famously lost $100 billion of market value in a single day when its new AI chatbot returned an incorrect answer during its launch.
Because AI tools and corresponding machine learning processes take time to collect information and assimilate and leverage new information, organizations must practice patience when planning and preparing such initiatives. Firms must diligently review the development of their AI-powered processes and ensure such initiatives aren’t moved to production stages until they are truly ready to proceed.
10. Forgetting to monitor and improve data
AI tools are only as effective as the information they are fed and the structure and efficiency of the workflows they are programmed to use. Data generated at the beginning of an AI implementation will also, likely, prove quite different from the data powering the AI process several months later.
For this reason, it is particularly important businesses closely monitor their AI solution’s performance, continually adjust the corresponding workflows and test and incorporate changes as circumstances require. Failing to monitor and adjust AI processes is among the easiest mistakes an organization can make when employing AI. Unlike traditional solutions, which are often set and forgotten, AI technologies should be continually monitored, tracked and re-assessed as they operate.
But wait, there’s more!
Other risks include properly understanding and managing corresponding ethical and moral fears sometimes associated with AI projects. Potential job reductions, operations adjustments and business model changes resulting from efficiencies gained by AI initiatives are elements organizations should manage frankly and purposefully, if such innovations are to achieve best results.
The risk of not implementing AI technologies or failing to properly account for the impact AI tools and solutions will have on a business is another danger that only grows the longer businesses wait to conduct an honest assessment. Just ask Chegg, the educational technology firm that lost nearly half (48 percent, or almost $100 billion) of its capitalization in just 24 hours due its admission ChatGPT could pose competitive trouble.
If you’re struggling with AI, or if you have questions regarding the innovative technology, contact Louisville Geek. We’re happy to assist. You can reach us by calling 502-897-7577 or emailing [email protected].
