How to Assess GenAI’s Impact on Your Business

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Read more about author Madhukar Kumar.

Since the beginning of 2023, generative AI (GenAI) has quickly made a significant impact across an expanding range of industries and applications. In just over a year since its groundbreaking debut, there’s much to celebrate about GenAI – and even more to still uncover and understand.

Today, 79% of employees report at least some exposure to AI, with 22% regularly using the tool at work. Leaders have bought into GenAI too. Nearly all business owners believe ChatGPT is poised to help their companies, meaning there’s enthusiasm about GenAI solutions across all rungs of organizations.

Despite GenAI’s growing popularity, the technology’s rapid adoption has left a major gap in organizational strategy – namely, a mechanism for determining impact, as well as a roadmap for resolving weak points when intended outcomes fall short.

Understanding the true business value of AI, specifically GenAI, is a top goal for leaders in 2024. However, due to lack of time and resources – as well as the task’s complexity – this goal is often backburnered on leaders’ to-do lists.

The GenAI Proliferation Problem

The potential for employees to quickly tap into GenAI tools creates a clear path for organizations to boost efficiency and broaden impact. But the technology’s proliferation is accompanied by a mix of advantages and disadvantages.

Operational tasks that were already simple for organizations have quickly become automated, difficult tasks have grown easier, and tasks that were once deemed impossible are now increasingly feasible. GenAI has shown new possibilities for employers and employees – from small-scale improvements to rich insights and business opportunities. Thanks to GenAI, there’s a renewed excitement among workers to solve problems, to learn, and to grow.

That said, much of this progress is happening in a freeform and unregulated manner. Without standardization, growing enthusiasm toward GenAI is easily undermined by operational inconsistencies and an inability to measure and amplify progress. This is especially the case when it comes to the data inputs required to power GenAI experiences.

An ongoing lack of specific guardrails and guidelines heightens risks and introduces confusion around the tool’s best use cases. No one wants to be the lawyer citing fake legal cases or the programmer behind chatbots offering bad financial advice. Still, it’s easy to see how these types of AI-powered blunders slip through given minimal oversight or quality assurance. 

It is ultimately up to leadership teams to encourage independent and creative GenAI use cases, while also offering employees a protected, clear, and measurable environment in which to experiment. This process starts with reflecting on applications of GenAI over the past 12 months, as well as the considerations required to better assess if the technology is delivering value to your business.

Three Factors to Inform Your GenAI Audit

Like any business investment, there are several strategies your organization can employ to more thoroughly evaluate the impact of GenAI. It is important that this measurement process starts now and is informed by any current and future GenAI use cases.

For each of these three prompts, consider if your organization is positioned to monitor, measure, and act:

1. How do you manage access and ensure security?

When measuring GenAI’s impact, diving into the data is the most promising place to start. Your current data capabilities and guardrails are powerful evaluation metrics of your system’s overall structure, security, and potential.

You might ask: How big is our data set? Is it mostly structured or unstructured? How are we managing access to data in real time so we can contextualize insights for language learning models (LLMs)? What mechanisms do we have in place to maintain data privacy, accuracy, and governance? How organized is our data, and do we have a clear understanding – and offer an equally clear explanation –of our data pipelines? Is it easy to connect our GenAI tools with other platforms and plug-ins? Can our GenAI solution run anywhere (e.g., on-premises, hybrid, and cloud)?

Answering these practical questions reveals immediate areas of improvement because security and access are core tenets of effective GenAI models. This line of thinking also assesses whether your GenAI efforts are intentional and structured, or overly freeform and too risky. If you find your answers to these questions are less than satisfactory or even nonexistent, this is a strong indication that your organization needs greater AI strategy and oversight.

2. Has GenAI led to additional revenue or cost savings?

Next, explore GenAI’s impact on your organization’s bottom line. This is an area in which you may want to coordinate with leaders across departments to more fully understand how GenAI is helping to accelerate specific business goals.

You might ask: How are our teams using gen AI in a daily capacity? What types of efficiencies is it driving – and are these improvements reflected in the way we scope and sell our products/services? How are we tracking these wins and crediting GenAI tools for their involvement? Are our employees trained to use these tools effectively and report back on progress? Are the insights surfaced by each department being disseminated to other teams?

Exploring GenAI’s impact from a metric perspective identifies the location of the technology’s desired effect. From there, you can model these successes elsewhere and make a strong case for expanding your network of GenAI tools when the time is right.

3. Has GenAI inspired quantifiable improvements in customer or employee experience?

Similar to revenue and cost savings, it’s worth exploring where GenAI is having a meaningful impact on your customer and employee experiences. This is also an opportunity to source feedback from others and ensure you have a process for logging, evaluating, and following through on recommendations. 

You might ask: What parts of our customer and employee experience have we automated? Are we being creative enough with our applications? How are other organizations in our space using GenAI tools, and what might we learn from their existing applications? Is there a clear process in place for employees to share new ideas for gen AI tools and seek feedback and/or approval? Are our solutions easy to use?

GenAI tools are intended to improve the lives of our customers and employees. So, measuring progress against these key constituencies is an indicator of whether GenAI is delivering the intended impact. Not only does hearing directly from these sources offer an honest assessment of the tool’s current performance, but the process can also surface new applications for GenAI solutions of which those in leadership positions may be less aware. 

Don’t Make GenAI Audits an Afterthought

We’re not starting from scratch. The way you audit the impact of your GenAI investments should align with the evaluation methods used for other software services at your organization.

We can monitor access, privacy, and quality, and achieve incremental improvements over time. By treating GenAI like any other technology investment, we can ensure these tools generate maximum value for our organizations – whether that involves offering a simpler yet more powerful vector database within a unified platform, or surfacing real-time insights with full context and rich queries.

However, the speed of operations is one key difference between GenAI and other technologies. Given the rapid evolution of GenAI, it’s crucial to develop strategies to evaluate and enhance the impact of your investments. Otherwise, the technology’s pace of change may become your undoing rather than your superpower.

Your auditing process should match the level of customization GenAI tools offer. The most crucial aspects for your organization to assess and improve are most effectively realized when aligned to the specific areas where you’re using GenAI to meet your business needs and goals.