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Data Science and ML

Tips for Responsible Use of Generative AI in Enterprise

It’s no secret that the promise of generative AI in the enterprise is great, offering unparalleled opportunities to streamline operations by transforming efficiency and productivity. Particularly in the IT and data insights industry, generative AI can drastically reduce the time spent on tasks like research and analysis, among many other benefits. 

However, employers are right to approach its implementation with caution. Whether using a third-party’s generative AI product, or designing your own, harnessing generative AI requires responsible and methodical product development. But when implemented responsibly, the nascent technology can save workers significant time, enhancing both efficiency and productivity. The key is understanding how to do so.

Apply Innovation to Pain Points

It’s paramount that businesses are strategic in how they adopt generative AI. Blindly implementing AI without a clear purpose or business case can lead to inefficient resource allocation and limited returns on investment. First and foremost, organizations should focus on identifying tangible pain points within their businesses that generative AI tools can effectively address. The goal is to pinpoint where and how generative AI applications can be used most optimally to streamline efficiency. 

Employers and product designers should consider questions like, where do employees spend the most time that they no longer need to? And, how can generative AI eliminate time spent on tedious tasks, and elevate employee workload to focus more on higher level thinking and creativity? The answers to these questions will help determine where Generative AI will be most useful. 

Outline a Purpose by Understanding Generative AI’s Benefits & Pitfalls

A clear purpose and goal are necessary when incorporating generative AI in the workforce. Without a foundational purpose for implementation, organizations risk confusion, frustration, and inefficient resource allocation. 

In addition to understanding workflows within the organization, a great way to zero in on a goal is for leaders to educate themselves so they have an intimate understanding of how generative AI functions. What are its benefits and what are its pitfalls? Combining these two factors – workflow goals and understanding – will enable leaders to clearly outline how the technology should be applied within the organization (when it can and should be used, versus when it shouldn’t be used)  to ensure responsible and efficient use.

Instill Trust with Verifiable Data

Understanding pitfalls – and mitigating against them – is essential. ChatGPT is known both for its innovation but also for its ability to derive false insights by pulling data from unverified sources, which will simply not work for enterprise businesses. The risk is too great for global organizations if they disseminate or make decisions based on incorrect information. Therefore, utilizing only trustworthy and traceable data is a pivotal step toward successful implementation – especially for businesses in the IT and consumer insights industry that rely on strong data. 

When assessing a product’s ability to pull verifiable data using generative AI, transparency is key. Users should be provided not only with answers but also with the sources from which those answers were derived. By relying solely on internal data, the risk of inaccuracies stemming from unreliable external sources is mitigated. In cases of conflicting information, acknowledging and verifying sources further builds confidence in the AI-generated output.

Incorporate Human-Centric Design 

Once a purpose is laid out, and benefits and pitfalls are accounted for, the interface becomes key to encourage consistent use. Despite working with powerfully automated technology, a successful generative AI product must be at its core: human-centric.

It should be user-friendly and offer seamless integration with existing platforms and workflows. It should naturally aid in human thinking, not run contradictory to it. Striving for a human-centric experience fosters symbiosis between humans and AI, leading to substantial productivity gains. 

Much like how the iPhone revolutionized how we interacted with technology despite leveraging components that we already were familiar with (a touch display, the internet, the ability to text, etc.), generative AI’s potential hinges on not the technology itself, but an innovative interface that will streamline how humans interface with it. The true catalyst for transformation relies on if humans enjoy using it or not. If a process or function is too complex, or does not feel natural to use, mass implementation is likely to be unsuccessful.

Envision Future Impact

It’s also important to remember that Generative AI’s impact extends beyond immediate gains, fundamentally altering the pace of organizational tasks, which will have a long term impact on business operations in years to come. At the beginning of the internet, we called companies that used it “internet companies.” Now, every company is an internet company. Someday in the future, the same will be true of Generative AI. 

But before that happens, the journey towards successful and responsible generative AI integration in enterprise demands meticulous planning, a clear sense of purpose, responsible education, and verifiable outputs. As the landscape of enterprise evolves, understanding its strengths – and utilizing them effectively – will unlock unprecedented efficiency and productivity gains.

About the Author

Thor Philogéne is the CEO and Founder of Stravito, an AI-powered enterprise insights platform that allows employees at Fortune 2000 organizations to store, discover, share and integrate consumer insights, in seconds. Today the business counts world-leading brands such as McDonalds, Comcast, Burberry, Electrolux and Danone as customers. Prior to Stravito, Thor was Chief Revenue Officer and VP of Growth at fintech company iZettle (now Zettle by Paypal following a $2.2bn acquisition in 2018). Thor holds a M.Sc. in Business Administration from the Stockholm School of Economics.

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