Generative artificial intelligence (GenAI) is no longer just a buzzword. For companies of all sizes, it has become a critical tool to enhance productivity and drive measurable results. But productivity alone isn’t the end goal—aligning efficiency with positive business outcomes is where GenAI truly shines.

This blog serves as a guide for tech-savvy business directors and professionals who want a deeper understanding of GenAI applications in critical functions. Whether it’s recruiting, marketing, or software development, you'll gain a clearer understanding of how to integrate GenAI strategically into your workflows for maximum impact.

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How GenAI drives productivity—and how to align it with your goals  

GenAI has the power to revolutionize workflows by automating tasks, uncovering actionable insights, and optimizing resource allocation. However, its effectiveness relies on one key factor—the alignment of GenAI-driven productivity with your business’s overarching goals.

The role of control groups

Implementing GenAI is not a “set it and forget it” solution; its success depends on continuous evaluation and refinement. One of the most effective ways to measure GenAI’s impact is by testing it against a control group.

A control group consists of a small audience, typically around 5%, that continues to follow the existing processes without GenAI intervention. By comparing the outcomes of this group to those influenced by GenAI strategies, we can accurately assess the effectiveness and value of our GenAI implementations.  

The monitoring process for the control group is the same as for the entire audience. For example, in a marketing use case if you measure email open rates, you would compare the open rates of the control group against the rest, serving as a benchmark.

The evaluation is typically done by the same team responsible for measuring campaign effectiveness, focusing on comparing results between the GenAI-implemented group and the control group to determine GenAI's impact.

The importance of selecting the right metrics, combined with quality control over outcomes

Metrics are the language of GenAI efficacy. However, as highlighted in the classic book How to Lie with Statistics, choosing the wrong metrics can lead to misguided decisions. For example, in one of our client’s recent marketing projects, a discussion arose about whether to delete content with zero views. One argument held that low views indicated irrelevance, while another suggested poorly targeted marketing efforts were to blame. Without digging deeper into audience segments and supporting data, the wrong content could have been removed.
This illustrates the risk of focusing solely on the value of metrics without questioning the results or analyzing why we are getting those numbers.

In GenAI, we mitigate this risk by ensuring quality control over a small sample of outcomes. By testing and refining based on these results, we can better understand the context behind the metrics. This approach allows us to confirm that our metrics reflect actual success, helping us avoid the drawbacks of surface-level data and make more informed decisions about the strategies we implement.

In the next section we talk about some examples we did to select the right metric.

GenAI in action

GenAI’s utility spans a range of functions. Below, we explore real-business examples of its application in three core areas of business.

Example 1: Recruiting

The hiring process is often time-consuming and error-prone. With GenAI, businesses are transforming candidate selection and evaluation.  

This year, Nearsure developed an internal tool leveraging Azure Services that helped us identify the right talent with the right skill set, significantly improving productivity in our recruiting workflows:

  • Faster Client Response Times: GenAI-driven talent sourcing reduced response times for delivering a viable candidate from 10-12 days to just 5-7 days.  
  • Reduction in Search Times: Specific sub-processes, such as locating suitable profiles, went from taking 1 week to nearly zero time, cutting the overall process by 25-30%.

But we also got to see qualitative improvements in the results of our recruiting initiatives:

  • Candidate Quality Improvement: AI analyzes candidate profiles and identifies those with top alignment to job descriptions, allowing recruiters to focus on high-potential candidates.
  • Automated Screening: GenAI-generated candidate profiles highlight strengths, weaknesses, and technical qualifications—especially helpful for recruiters unfamiliar with certain niche skills.

By focusing efforts on GenAI-curated applicants, we created a streamlined recruiting workflow, with measurable productivity improvements and higher-quality hires.

Example 2: Marketing

We have an ongoing project that uses GenAI to generate emails, landing pages, and other content. This process is automatically triggered through a form, which, when executed, creates a marketing campaign, integrates with a CMS to generate the pages, and utilizes a marketing automation tool to send the emails. This process is called content lifecycle.

With this approach, we improve time-to-market and reduce costs for each campaign without the need to hire a content creator or a creative professional to generate the assets.

However, this is meaningless if we are losing conversions, engagement, or user LTV. This is where a control group helps us compare manually generated content with GenAI-generated content. The challenge lies in selecting the right audiences to avoid biases and drilling down as much as possible to understand the characteristics of each segment. We call this process content management.

Example 3: Software development

Software development is a domain where GenAI not only saves time but also enhances team alignment and project outcomes. However, the challenge lies in selecting meaningful metrics.

Historically, common metrics like developer speed or burndown charts have been problematic. For instance, one client attempted to measure developer productivity by creating a leaderboard ranking developers and labeling them with colors (green for good performance, red for poor). This inadvertently discouraged developers from tackling complex issues, as taking on harder tickets often resulted in being categorized as “low-performing.” Ironically, fixing these tickets could result in a sudden leap to the top of the leaderboard. This misaligned metric did more harm than good, eroding team morale.

Our solution? We adopted several frameworks simultaneously, including DORA metrics for operational health and SPACE for developer experience, tracking variables like team satisfaction and individual progress. In addition, we ran initiatives like GitHub Copilot sessions to benchmark improvements in code quality automation, such as generating unit tests, API documentation, and more.

Recognizing the limitations of code assistants, such as their restricted context window and inability to handle complex tasks, we at Nearsure are developing an IDE extension for IntelliJ and Visual Studio Code. This extension helps us analyze our code base following business rules, automating parts of the process—particularly in a project where the pull request checklist was massive, and many points could be automated.

Combining GenAI with balanced metrics, we streamlined workflows and enhanced productivity while preserving team dynamics, with GitHub Copilot playing a key role in improving code quality and ensuring compliance.

In summary

By combining domain expertise, real-world data, and an efficient GenAI strategy, we can automate processes that improve productivity without compromising results. Since GenAI generally has a confidence score that’s not 100%, we can still achieve impressive results that streamline workflows, improve quality, and drive long-term success.

Join us in Salt Lake City and level up your GenAI strategy!

Are you ready to transform your business with the power of GenAI? This year, we’re excited to host our exclusive event during Utah Tech Week, a pivotal moment for innovation and technology. It’s the perfect opportunity to bring our knowledge of GenAI to the forefront and share it with a community of visionaries.

We’ll take a deeper dive into the three key business areas covered in this article—marketing, recruiting, and software development—and the impact of GenAI on it workflows and business outcomes.

Why attend?

  • Exclusive Event: Gain access to expertly curated case studies and actionable strategies.  
  • Build Connections: Network with forward-thinking business leaders and exchange innovative ideas.  
  • Gain Practical Insights: Learn from experts who will guide you on how to ensure your GenAI investment delivers real, measurable results.  

This isn’t just another event—it’s your opportunity to take your GenAI strategy to the next level while building relationships with professionals who share your vision for GenAI-driven success.

Sign up today!