Everyone’s feeling the pressure to adopt AI at work, but not every business leader is investing in proper AI procurement and training.
In order to navigate this dissonance, we’ve seen the rise of a “Bring Your Own AI" (BYOAI) approach, in which teams are furtively implementing their own AI solutions or adopting consumer-grade tools like ChatGPT.
At first glance, this seems like a win-win—teams can use their preferred AI platforms to optimize workflows!
However, BYOAI (also referred to as “shadow AI”) introduces serious risks, especially when it comes to data security, regulatory compliance, and even efficiency.
Even if the results aren’t as dire as they once were—remember the lawyer who turned in an entire brief with fictional court cases that ChatGPT hallucinated?—a policy of “Bring Your Own AI” spells danger for any organization.
Let’s take a look at why BYOAI is bad for business, and why purpose-built AI solutions are the way to go.
While most AI tools and Large Language Models (LLMs) are intuitive and easy to use, this doesn’t mean that everyone will be successful in leveraging their potentials. An AI tool’s outputs are only as good as the inputs—which doesn’t bode well for unskilled workers blindly using unverified AI applications.
Prompt engineering isn’t a throwaway skill; there’s actually a lot of nuance to it. Tailoring AI inputs to achieve specific outputs requires experience (even if you’ve committed to watching hours of YouTube tutorials when your boss isn’t looking).
When employees cherry-pick their own AI tools, they’ll likely turn to consumer-grade applications, which often don’t have robust security protocols. Signing up for an enterprise account with ChatGPT is relatively safe. But many of these other AI tools are developed by small teams or individuals who aren’t focused on stringent data policies.
Anytime someone gives you something for free, there’s generally a reason: They want your data.
Allowing unrestricted access to unverified AI applications can result in disaster for a company’s data integrity and privacy—especially if employees are blindly dumping proprietary or confidential info into these tools, without reading the fine print about how such data can be used.
The whole idea of AI tools is to save time. But using these applications incorrectly, or without a cohesive, company-wide plan, can actually introduce fresh roadblocks and inefficiencies. For instance, the consumer version of ChatGPT may be cheap (or even free), and it might be helpful for routine tasks like drafting emails or summarizing meeting transcripts.
But for more intricate operations that demand consistency and accuracy, these tools will likely fall short. You’ll be doing a lot of unnecessary copy-pasting into ChatGPT and similar models. Trying to jerry-rig an off-the-shelf AI tool into automating essential parts of your job will likely require more time than you end up saving—and can introduce errors into vital processes.
A homegrown or off-the-shelf AI model might work well when analyzing small data sets but would cave under the pressure of a massive marketing campaign involving millions of data points.
A custom AI solution often has to be integrated with your existing tech stack—CRM systems, email marketing tools, and analytics platforms. These integrations can become complex and require significant IT support, slowing down your operations and leading to delays.
First of all, what do we mean by “purpose-built AI”?
These are specialized apps tailored for specific tasks or challenges. While general-purpose AI like ChatGPT is a sort of Swiss army knife of multi-functionality, purpose-built tools are optimized for narrower use cases.
The products within Stagwell Marketing Cloud, for instance, are purpose-built for marketers. While they may be powered by various LLMs underneath the hood, from Anthropic to Gemini, these tools are integrated into an application that has been expertly designed for specific use cases.
Purpose-built AI applications are meant to scale easily, integrate with your existing tech stack, and uphold high data-security standards.
Here’s a few other advantages:
With the right applications, you don’t have to turn yourself into an expert-level prompt engineer.
Purpose-built AI solutions are designed with user experience in mind. They come equipped with built-in guidance, training resources, and user-friendly interfaces that lets marketers and comms pros get in the driving seat without needing to know everything about the underlying tech.
AI technology shifts on from day to day, and it can feel impossible to keep up with current developments, let alone which models are best suited for specific tasks.
Purpose-built solutions come with dedicated teams that actively monitor AI model performance and update their platforms accordingly. This means the organizations using these tools can work comfortably on the cutting edge, without doing the periodic re-evaluations themselves.
One underlying worry about the future of AI models is that they’ll begin to degrade over time. Since there’s now such a glut of sloppy AI content online, newer AI models end up “digesting” it—and the results aren’t pretty.
Purpose-built AI solutions are less vulnerable to this. And in the case of Stagwell Marketing Cloud’s suite of products, they’re constantly being fed and informed by real, pertinent marketing data from within the larger Stagwell network of agencies.
