Common Pitfalls when Implementing Generative AI in Businesses
With a plethora of gen AI tools now available – and new solutions popping up at an astonishing pace – more and more businesses are exploring ways they can use this innovative tech to enhance their operations, offering and output.
And it’s clear to see why. Done well, generative AI offers seemingly endless possibilities to transform a business, from streamlining complex processes and automating repetitive tasks, to boosting creativity and enriching customer experience.
Google’s recently updated list of real-world gen AI use cases is proof in point, showing numerous examples of how effective gen AI can be when businesses get it right – from a conversational tool that lets customers feel as if they are interacting with a knowledgeable travel agent, to a medical solution that helps professionals suggest effective treatment approaches for their patients.
While these examples show the best of gen AI in enterprise, being an early adopter can sometimes feel like a daunting task. Here we look at the common pitfalls to avoid when thinking about transforming your business with the power of AI.
1. Not Asking Yourself if AI Is Right for You
As with any new tech, the FOMO factor with gen AI is strong. Many businesses now feel obliged to jump on the bandwagon without a clear endgame in mind. This is where careful planning is key. Taking the time to properly understand whether AI is right for you – that is, does it align with your business goals and objective – can certainly pay off in the long term.
An effective way of seeing if AI is right for you is by experimenting with an out-of-the box Large Language Model (LLM) solution, such as OpenAI's GPT or GitHub’s Copilot. This approach offers a relatively safe and cost-effective way to gauge the impact and benefit on your business, before rushing out and commissioning a custom – and cost-heavy – solution.
While being at the forefront of a new technology can be empowering, it is important to remember that gen AI is still in its infancy. Ask yourself whether you really have the appetite for being an early adopter – and having to navigate all the limitations and complexities that come with that – or are you better off waiting until the tech is more mature.
2. Not Aligning AI to Your Business Goals
We get it. It’s tempting to see a shiny new technology and want it, without thinking about how best to put it to use to benefit your business. Before adding a brand-new AI tool to your shopping list, it’s good to be clear on your business objectives. In other words, think: what do I ultimately want to get from AI?
The bottom line is that generative AI works best when aligned to your business goals and your customer needs. If it is not yet clear how AI can support these, then it is worthwhile taking a step back and really getting down to the nitty-gritty of how you’ll make gen AI work for you.
3. Not Accounting for the Impact on Systems and Staff
Gen AI can have a big impact on your business – and in ways that are immediately obvious. Integrating AI into existing workflows and processes can fundamentally change the way your business operates. Ignoring this could lead to systems being disrupted and staff being disillusioned.
Before implementing an AI solution, it is key to consider and pre-empt how it will directly impact systems and staff. Ask yourself: how well will AI integrate with existing systems and processes? How will people adapt to working with AI? How is AI going to be maintained – and by who?
It is also important to identify any skill gaps and training needs that may be required, to ensure your workforce are not only fluent in working with the tool, but are confident at maximizing its potential.
4. Overlooking the Importance of Data
To work effectively, generative AI relies on large amounts of high-quality data. So, one of the first questions for any business is: do we have the data available? A lack of high-quality data is likely to result in low-quality output.
Another key consideration when it comes to data is fairness. If your gen AI tool is creating output using biased data, then there is a real risk it can lead to discrimination and equality issues. Data privacy is also key. If your company deals with sensitive personal data, you’ll need to ensure the proper protections are in place to avoid any data leaks in your AI output.
5. Not Having Effective QA in Place
It may sound obvious, but gen AI is not always right. Indeed, the phenomenon of gen AI producing incorrect results – known as “hallucination” – happens far more often than you’d think. The New York Times recently pointed out that analysts believe chatbots hallucinate as much as 27% of the time.
While incorrect data has more serious consequences for higher stakes industries such as medicine and finance, any wrong or inaccurate data your company releases have the potential to cause reputational damage – especially when direct customer interaction is involved. To avoid this, companies must build in an effective testing and QA process, to keep their gen AI output in check.
6. Forgetting the Human Element
Given the social commentary regarding generative AI, implementing AI within a business environment will naturally raise ethical concerns – especially around the notion of AI as a replacement for humans.
As Forbes points out, the most successful examples of gen AI in enterprise are when technology is used to enhance human experience, by making it easier to navigate increasing complexity and enabling people to focus on their domain expertise instead of business processes. Striving for this “human-centric approach” to AI, over a focus on pure automation, will lead to a more effective and sustainable solution in the long term.
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