Why tapping into LLMs isn’t enough for data analytics
The transformative power of AI is revolutionizing the insights industry – but be sure you’re using the right technology for your team’s needs. It’s tempting to turn to a free and readily available tool like ChatGPT when the demands on your work as an insights professional are always “more” and “faster”. However, is this really the most effective and productive choice for meeting your needs?
Generative AI has the potential to significantly enhance enterprise-level insights work by boosting efficiency, rapidly analyzing data, and providing accurate insights. Like humans, these technologies are not perfect and are subject to limitations and risks. One of the most well-known examples of this technology is ChatGPT, a type of Large Language Model (LLM).
Insights teams at enterprise companies need to drive data-backed decision-making across their organization by staying on top of consumer preferences to stay ahead of the competition.
While generative AI may appear to be the most straightforward option for boosting productivity, it isn’t always the most impactful choice.
Here’s what you and your team should consider to make sure you’re using the technology that will deliver the results you need:
Why enterprise teams are tapping into AI technology, including ChatGPT
If you’re familiar with the State of AI in the Insights Industry, this section will serve as a quick summary. For those who aren’t familiar, here’s a quick run-down of the tech, how it works – and how it doesn’t.
Understanding generative AI ( including ChatGPT )
Generative AI models, mostly LLMs, are essentially probability machines trained on large data sets to quickly comb through and combine data in response to a prompt given by a user. They can’t think the way a human does, but they can process very large amounts of data very quickly – helping humans dig deeper and scale faster than they can on their own.
What types of data any given generative AI model is good at processing depends on what it was trained on and also how skilled the user is at prompting the technology to get the results they want. The more basic the model – like the free version of ChatGPT – the more skilled at prompting and refining the results the user needs to be.
At this stage, basic generative AI should be thought of as a means of support similar to a research assistant. They require guidance to deliver results and fact-checking to ensure those results are correct.
Since enterprise companies need advanced insights that are timely, scalable, and reliable, this creates a mismatch between what this technology can do and what enterprise insights teams need.
The need for advanced insights
As we touched on in the introduction, there’s a need for enterprise organizations to stay ahead of the competition at the same time they’re keeping pace with constantly changing consumer preferences. It’s not enough to understand the surface level of trends; teams need to understand why consumers are making the choices they are to communicate why their brand meets those consumers’ needs.
Enterprise insights teams have the resources to collect large amounts of data and are met with the challenge of extracting actionable insights from that data quickly and in a way that’s scalable. There’s pressure both internally and externally around this; teams across an enterprise want to make decisions based on consumer insights, not vibes, and consumers want brands to understand and anticipate their needs.
Insights from this data also need to be relayed across the organization in a way that’s easy for every team to understand and take action on. Things that seem obvious to the team immersed in the data all the time might not be to other teams focused on their deadlines and goals.
A truly useful generative AI research assistant would be able to help with the entire end-to-end process. That includes everything from creating and refining a survey or questionnaire to collecting and processing data, analyzing and interpreting that data (including sentiment analysis), and then making that data easily shareable as personalized consumer insights.
The free version of ChatGPT would be able to help with some aspects of this, but not without risks. However, an Enterprise Version with larger capabilities is available that teams can evaluate to see if it meets their needs and budget.
Risks of Using Generative AI Tools
As with any technology, generative AI has benefits and risks. Enterprise-level companies have a lot of the same concerns as smaller companies do around data privacy, security, and compliance, but they have to worry about all of these problems at scale.
Here are the risks to weigh before you tap into this technology:
The technology itself
All emerging technologies have their share of issues. Generative AI models, such as ChatGPT, include some type of bias due to incorporating the data it was trained on from all around the internet (which is notoriously biased in many corners).
The technology is also built to provide an answer in response to a prompt with few exceptions – things it has been trained not to answer, like recipes for explosive devices – so it’s prone to “hallucinating” data instead of returning no result. According to Scientific American, some amount of hallucination is inevitable, but there are ways to minimize it. Even if the impact is minimized, that still means time spent fact-checking the results the AI delivered instead of doing deeper, more strategic work.
Generative AI also struggles to handle very large datasets, something enterprise companies work with routinely, and any unstructured data. Most of the models are also chat-based models, so they require sophisticated prompt engineering and understanding of how to continually refine results to get what the user wants.
Users have to manually upload their files to the generative AI which poses several security and data privacy issues around proprietary data. The assumption should be that everything fed into an LLM is used as training data which means any proprietary data would then be available to anyone using the right prompt to retrieve it, on purpose or accidentally. That would be great news for your competitors and not-so-great news for your organization.
Enterprise-level teams need a solution that’s custom to how they run research, is easy to use, and gives the user direct control over their study from end to end.
Outside Entities
Aside from potential risks to proprietary data, there are regulation and compliance issues for enterprise-level organizations to consider. Especially if they are processing sensitive data in specific industries and/or for clients in specific locations, a basic generative AI model might not be able to account for regulations around:
- GDPR
- CCPA/CPRA
- HIPPA
- GLBA
It’s possible to account for that with sophisticated prompt engineering, but then it’s more likely you’re working with proprietary data you wouldn’t want to feed into an open model in the first place.
Specific Tools
There are always hiccups with new technologies, but some examples get held up for years because of how detrimental they were or could have been – to a particular company.
Engineers at Samsung were banned from using ChatGPT after using it to troubleshoot proprietary code and a lawyer learned the hard way that data hallucinations are a big problem when ChatGPT cited previous cases that didn’t exist and he presented them in court.
Teams need to be sure they’re using the technology that meets their needs across the board, including when it comes to data privacy, security, and compliance.
Knit’s Approach to AI
At Knit, we firmly believe that researchers belong in the driver’s seat and AI should act as a co-pilot for the entire end-to-end research process. Our technology is built to help researchers do their best work, not do their work for them. Offloading tasks that can be automated to an AI research assistant leaves room for deeper analysis and storytelling that brings your work to life across your organization and makes data-driven decision-making possible.
We understand the work that you do
The job of an insights team is to communicate what they learn to other teams across an organization so they can act on it, and those other teams need to understand those insights without in-depth training or extended time to spend with the data.
We’ve built our AI to help with every step of the process:
- Study Design: With your research objectives and prior examples of how you uniquely run research, our AI assists researchers in crafting surveys and questionnaires.
- Data Analysis: We’ve built our system to intelligently analyze both quantitative and qualitative data, including complex open-ended text and video feedback, helping uncover meaningful insights in minutes.
- Quality Checks: Responses are quality ensured by the AI-driven algorithms employed to scrutinize the data for bad actors, fraud, accuracy, and consistency, with a Knit Researcher giving a final review of all output before it meets your eyes.
- Report Generation: Communicating insights is covered with Knit’s AI synthesizing analyzed data into comprehensive reports, creating summaries that are insightful and easy to comprehend, and helping the researcher build out their narrative.
We take data privacy, security, and compliance seriously
Our mission is to build secure and responsible market research solutions that don’t compromise on innovation. Our commitment to data security and the ethical use of AI are at the core of our organization.
While our training processes are proprietary, we can share that we use our high-quality internal research to improve our model.
Our environment is compliant with ISO 27001 standards, leveraging AWS’s certified infrastructure and tools to ensure our security practices meet rigorous industry standards. We also recently became COPPA compliant and can accommodate the requirements of anyone researching medical and healthcare topics.
If you’re interested in learning more about our policies and approach to AI, reach out to our team to ask any questions you have!
Final thoughts
New technology, especially those that promise to make our workloads easier, always comes with its share of risks and benefits. Teams working at the enterprise level can’t afford to tap into tech that’s below what they need to keep up with their work, including from a data privacy, security, and compliance perspective.
Find the AI research assistant that’s right for the work your team does and you’ll unlock meaningful insights that drive your organization’s understanding of your audience forward and help meet your goals.
