What are the Biggest Challenges for Businesses When Implementing Generative AI Applications?

June 27, 2024
Natalie Thorburn


It’s not a bandwagon. It’s not even a gravy train. The advent of AI really does spell the future for almost all businesses.

There is no doubt that effectively implementing AI systems for your business will set you apart from the competition. It can make you better, faster, and more creative.

The applications are truly myriad, but none are straightforward – even if the internet is awash with tech influencers telling you how easy it is.

It’s not as simple as simply plugging in an AI system, turning it on, and setting it free on your company. For truly effective AI application, a whole host of challenges need to be overcome.


1. Lacking vital knowhow


The idea that AI needs little-to-no expertise – that it is an out of the box solution that anyone can have a go on – causes more issues than many realize.

Any organization that has spent any time really trying to successfully apply AI has quickly come to understand that they need in-house experts. Recent reports on the AI specialism job market shows they are set to be the fastest growing sector over the next four years.

That means investing in training, working with experts in the field, and building up from small AI projects that give your team the time to learn before asking them for big, overarching solutions.

Many companies are also still reliant on outdated systems and fail to understand how badly they will struggle to process large amounts of data at speed.


2. Deciding how it can help


AI is often talked about as if it is the solution to everything. Indeed, it can help with a dazzling array of tasks – from image creation and writing to question answering and complex database searches.

But that doesn’t mean you should expect it to do everything or attempt to apply it indiscriminately. Getting the execution wrong can seriously set you back.

For example, many organizations have been eager to ditch human customer service departments and replace them with AI chat bots. As Forbes’ report on customer chatbot satisfaction shows, 80% of customers who interact with chatbots come away more frustrated, and 78% end up having to interact with a human anyway.

UK parcel delivery company DPD serves as an even more pronounced cautionary tale – earlier this year their new customer service chatbot not only completely failed to help a customer but then wrote a poem about how bad the company is.

Understanding what customers expect from a business, and what tasks could be done more quickly by AI with human oversight without compromising on the customer’s experience, is vital to securing customer acceptance of a new system.


3. Intellectual property ownership


The generative AI solutions provided by MongoDB show just how limitless the potential applications of this technology are.

Having an AI assistant to help with research, writing, coding, or design might seem like a Godsend. Most leading video editing software now also incorporates AI – check them out in our post ‘Video Editing Software: Top Picks and Essential Features’.

But the question of intellectual ownership is a serious challenge.

Remember, generative AI systems generate their output based on the datasets they have access to. They may well be borrowing from content that belongs to someone else when assisting in creative endeavors and this could spell trouble.

The Hollywood Reporter recently detailed record companies’ aggressive stance towards AI companies. They’re not the only ones – more than 25 lawsuits have been filed by artists, authors and publishers.

This infringement can be hard to detect at the point of use but could mean that anything a business produces with the help of AI systems is open to dispute.

AI applications will be transformative for many businesses, but there is likely to be a steep learning curve – and more to think through than perhaps many realize.



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