ChatGPT and other generative artificial intelligence applications have entered the chat – and are poised to affect nearly every facet of your business. If, that is, companies can prepare their own data assets to successfully put these technologies to work.
If businesses didn’t feel compelled to get their data house in order for accommodating advanced analytics applications and data science, then the possibilities of using generative AI combined with their own data assets certainly should now.
These AI applications can write, document and review code. They can generate marketing and sales content and plans, new training procedures, social media posts, and blogs. Evaluate service agreements, conduct data analyses, provide next-level automated customer support? Yes. Scientists have even used ChatGPT to develop new protein sequences, and pharmaceutical companies are using it to accelerate drug discovery.
Executives have every reason to be excited about the opportunities these applications can bring. Yet getting your data ready for generative AI comes with a slew of technical, legal, privacy, and strategic challenges. Here’s where business leaders should start.
Ensuring the effective and ethical and use of generative AI may require many organizations to have a renewed focus and investment on certain data management capabilities. These include:
These new AI applications when given access to your data will be able to provide a variety of benefits. Conceiving these ideas in parallel with an understanding of how well prepared the underlying data is will help prioritize use cases.
For example, AI language models can be used to automate customer service inquiries, such as answering frequently asked questions, handling simple complaints, and resolving issues more quickly and efficiently--even in the form of chatbots. Similarly, these applications will be able to to personalize product recommendations, advertisements, and other content, or analyze customer feedback and sentiment, allowing companies to gain insight into customer attitudes and preferences. But these uses will require access to quality customer, call center, product, and likely third-party data sources you have curated.
Other high-value uses such as content creation, fraud detection, employee or customer training, strategic or operational decision-making, and compliance monitoring will require access to specific and varied data sources related to each.
Organizations have to balance data accessibility and security. Encryption, access controls, firewalls, and regular data backups should be used to keep data secure. At the same time, data should be stored in a centralized location where it can be easily accessed. For additional flexibility, implement cloud-based storage and data management tools.
That said, it’s still early days, and ethical, copyright, and privacy issues have yet to be fully worked out. As such, business leaders should think through what data they need to redact, mask, and/or synthesize before running it through generative AI programs. Customers’ personal information, healthcare data, and anything that would violate client confidentiality agreements should be evaluated before inputting it into a generative AI system.
Business leaders should also prepare for legal challenges and regulatory guidelines when preparing data controls and processes. For instance, the European Commission and National Institute for Standards and Technology have released useful AI deployment frameworks and guidance. Yet we’re far from any binding regulations, and most challenges (at least in the U.S.) will likely play out in the courts.
None of the above will be effective without a clear, transparent, and considered data strategy that goes beyond your current strategy for generating, collecting, managing and using data for traditional operational or analytical purposes. Ask yourself:
Generative AI is here to stay – and evolve exponentially, creating a new competitive battlefield for businesses around the world. As organizations start to leverage these applications and their own data to improve everything from customer support to IT to research and development, they should be enthusiastic about new opportunities, while taking the necessary preparations to drive success.