ChatGPT and AI Solutions: The importance of data integrity

ChatGPT is a popular tool that simplifies tasks and provides accurate responses, saving time and potentially driving cost savings for businesses.

ChatGPT is making waves in the news and across the business community due to its ability to streamline diverse tasks by providing responses that are accurate and nuanced. The excitement is warranted: ChatGPT is a time-saver and drives potential cost savings for organizations — two critical elements that impact the allocation of resources in a business.

What cannot be overlooked is that ChatGPT, like any Artificial Intelligence (AI) solution, is built on a significant amount of data. When a user asks for a unique response from their favorite celebrity, for example, the tool’s response is based on voluminous amounts of available data on the celebrity which informs the tone, direction, and prose of the response. But what happens if the data was largely inaccurate? The response would not be an accurate reflection of how the celebrity would respond to the question or prompt posed by the user.

For recreational purposes, like the celebrity use case, an AI solution yielding an inaccurate response is innocuous. However, as companies rave about the possibilities that AI provides, the pitfalls of leveraging the solution on top of inaccurate data could be detrimental. Consider an AI solution that interacts with customers via a chat function and is built on erroneous data. The solution’s dialogue with customers could be confusing, misleading, and overall unhelpful — risking the integrity of the company’s customer service function which could result in the loss of customers.

Simply put, bad data yields bad responses. AI is a growing tool in the business landscape and its usage will only continue to grow. Before implementing such a powerful piece of technology, which many organizations strive to make core to their business, careful considerations should be made regarding how to deploy the solution and ensure its accuracy.

Implementing AI in Your Organization

New AI use cases are emerging rapidly. The opportunities are seemingly endless and business leaders are justified in being eager to introduce AI to their organization’s critical functions. Before doing so, they should perform analyses that focus on repetitive tasks that are time-consuming, including:

  • Gatekeeping: Customer-facing chat solutions that document challenges and enable customers to resolve straightforward requests
  • Issue Diagnostics: Review of manufacturing or system issues for triage or initial attempt at resolution
  • Copywriting and/or Documentation: Creating or editing written drafts for publication

There are also more robust and intricate tasks that organizations are looking to tackle with AI and can take prolonged periods of time to complete. Leaders should review these processes and the amount of time and personnel dedicated to completing them — shining a light on what tasks are best suited for an AI solution. These include:

  • Decision Making: Applications for employment, loans, or other critical business needs
  • Organizational Risk Determination: Applications that automate business process controls
  • Alerts and Trends: Review of market conditions and inputs that may drive a business to respond with a new investment or spending strategy

Ensuring the Success of Your AI Solution

AI’s capabilities, while touting the ability to complete manual tasks, in some respects differ from human capabilities. For example, humans possess the ability to communicate emotion and provide helpful context that is often not considered by an AI solution. As context outside of what is “fed” into the AI solution is overlooked, the need for correlated and accurate data is paramount.

An AI solution operates within a defined criterion — rendering it only as good as what it knows.

This puts forward an element of risk in implementing an AI solution, as there’s risk present in introducing any new system or process into an organization. Therefore, leaders must take a risk-based, highly tactical approach that focuses on the user and enables both a positive customer experience and efficiency on the backend. These risks are manageable by performing recurring risk assessments that put the organization in its customers’ shoes to help highlight potential tripwires, mitigate risk, and position the team to respond to any incidents efficiently.

By determining what problems are to be solved or what tasks will be completed by an AI solution, ensuring the integrity of the data it’s built on, and remaining cognizant of risk and diligently mitigating those on the horizon, organizations will be positioned to get the most out of enticing AI solutions, like ChatGPT, that can be a game-changer for their business.

Altum’s unique service offerings help organizations navigate the complexities of an AI solution deployment. The Altum Crew surveys the data environment, unearths risk, and takes a hands-on approach to deployment that contribute to a scalable and comprehensive solution.

  • Date June 30, 2023
  • Tags Financial Services Insights, Insights, Intelligence, Data & Technology, Intelligence, Data & Technology Insights, Resilience, Risk & Governance, Resilience, Risk & Governance Insights, Technology, Technology Insights