"Why" Generative AI can drive Business Outcomes in Corporate Settings

For a year, I've been sporadically writing blog posts exploring why Generative AI is poised to drive academic and business outcomes. In my previous posts, I focused on building trust, safety and transparency in Generative AI applications, and before that, wrote about career readiness preparation, and how organizations can leverage the unique properties of Large Language Models (LLMs) to provide personalized feedback for their end users, at scale, on a 24/7, 365, on-demand basis. In this blog, I will continue to build on that chain of thought (pun intended) and explore the world of continuous learning and development (L&D) in corporate settings. There are parts of this blog that may require a deeper dive into some technical bits, references to which I have provided in the blog. For those of you that want to skip past these bits, but still want to tap into the power of Generative AI for L&D, please feel free to reach out to me directly and I will do my best to guide you or help.

Optimizing your workforce: An important topic of discussion.
In today’s fast-paced corporate landscape, the ability to harness data effectively can make a world of difference in driving business outcomes. Among the tools available, conversational analytics, powered by Generative AI Models, emerges as a game-changer, providing insights that are often overlooked. By analyzing the nuances of employee interactions, organizations can uncover patterns, preferences, and areas for improvement, ultimately fostering a culture of continuous learning and adaptability. As companies strive to enhance engagement and productivity, leveraging the power of conversational analytics equips leaders with the knowledge necessary to tailor training initiatives and optimize workforce capabilities. This data-driven approach emphasizes an important aspect of talent development: the need to map learning and development (L&D) efforts to business outcomes, ensuring that skills acquired during training translate directly into enhanced organizational performance.

The Need to Map L&D to Business Outcomes

A study commissioned by Middlesex University for Work Based Learning found that 74% of workers from a sample of 4,300 felt they weren't achieving their full potential at work due to lack of development opportunities.

This article from the HR Magazine highlights research and several interesting statistics that show how hungry people are for opportunities to learn on the job. And this translates directly to a burden on the organization’s Learning & Development (L&D) departments, who are often short-staffed, resource constrained, or both. I have seen and experienced too often, the misconception that learning and development is primarily associated with content creation, and serving up tutorials, lessons, regulatory sessions, or similar modules, at discrete instants in time, to check a box within an organization. This highlights a pervasive misconception that learning and development (L&D) is primarily associated with content creation and compliance tasks done only at specific intervals. Often, there is the exercise of “needing to map” the learning and development function within the organization to the organization’s objectives or goals. While there are healthy debates and multiple viewpoints on this aspect of L&D (and there is no right or wrong approach, since all organizational cultures and workforces are nuanced and unique), there is also a seismic shift with Generative AI that is poised to flip the “need for mapping” on its head. By tapping into the underlying architecture of Large Language Models, we can do things that weren’t quite possible before. For example, by having a data strategy in place, Generative AI can help automatically identify and map the learning & development functions based on business outcomes. This is where we start to get into the interesting world of Predictive AI. If this topic is still holding your attention, read on, because I will try to explain this idea further with some real examples.
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Applications for Sales & Manager Training
Predictive analytics offers organizations the ability to forecast training needs and outcomes by analyzing patterns from historical real-world data. The staggering statistic below that I computed using some back-of-the-envelope math should give anyone doubting if we have any available data, food for second thought:

The daily meeting time across Microsoft Teams and Zoom combined is roughly ~362 million hours ! [1, 2]

So, what are organizations doing with all this data? More importantly, what can organizations do with this data, particularly if they were to begin using advanced Conversational Analytics? The possibilities are endless, but what matters most is making sure that the choice is well-aligned with business outcomes.

​As an example, in the realm of sales training, organizations can utilize predictive analytics to identify the characteristics of top performers and tailor programs that replicate their success traits. By understanding which training modules yield the best results, such as increased sales conversions or improved customer interactions, companies can refine their approaches to training and ensure maximum impact.

Similarly, for manager training, predictive analytics can help assess the current competencies of managers and forecast the skills required for future challenges. By examining employee feedback, performance assessments, and other data sources, organizations can identify gaps in managerial skills and create targeted development plans. This proactive approach ensures that training is not just reactive to current needs but anticipates future demands, ultimately equipping leaders with the tools necessary to drive team performance and business success.
And this brings me to the foundation for all of this to occur. It is imperative that organizations invest in a good data strategy that can support their workforce and L&D departments.

​The Data Strategy
Until now, we didn’t have the ability to look at vast volumes of data and comb through them to extract insights that mattered to our academic or business context. This also meant that we didn’t really have a data strategy in place that would directly impact our future business outcomes. Sure, we all collected and saved data that mattered to us, but this data was unstructured at best, and more likely, unusable at worst. Large Language Models (LLMs) have changed that. We now know exactly how data can be used to improve the performance of these models. We also know how we can alter the final layers of these incredible neural networks with our own data to achieve novel results. In a nutshell, this data can be broken up into the following categories:

• Pre-training corpora or foundational datasets used to train LLMs on large amounts of text, that allows them to perform next word prediction, grammar, patterns, or even (apparent) reasoning abilities.
• Datasets that are used to adapt the functionality of LLMs in order to perform very specific tasks such as classification or instruction following through the process of fine-tuning.
• Evaluation datasets to check whether the LLMs that were built from scratch or fine-tuned are performing to the standards that you require in your academic or business context.
• Ad-hoc datasets that serve as “training wheels”, when used in conjunction with traditional LLM usage e.g. prompt-engineering with one-shot or few-shot learning.

A comprehensive overview of these datasets for LLMs can be found here on arXiv. Also, if you’re interested in getting some hands-on experience with LLMs, I could not more highly recommend the book “Build a Large Language Model (From Scratch)” by Sebastian Raschka, and his associated GitHub repository.

The Connection between L&D, Data Strategy, and Business Outcomes
At this point, you’re probably thinking: "How does this help me with L&D and the need to map the function to business outcomes?" Well, from the previous section, we know that we can transform any data that we have into insights using the power of Large Language Models. So, the questions that we should be asking ourselves could be something along these lines, in the following order:

• What are the biggest challenges or problems that our business is facing?
• What kind of data (quality, quantity, and category from the previous section) do we have, that is related to those challenges?
• What other data can we be collecting that is directly related to those challenges that we’re facing?
• What kind of insights do we think we could gain, if we had an infinite number of resources to comb through that data?

If you haven’t already, you will soon be coming up with ideas in your mind on what you’d like to do with LLMs. I’d like to help kickstart that process by focusing on one of the most accumulated datasets that organizations will likely have access to: Recorded virtual meetings or calls, involving human conversation.

Applying Conversational Analytics for Insight Extraction
The potential of conversational analytics, powered by Generative AI, in corporate settings is vast, particularly when it comes to extracting actionable insights from recorded meetings and calls. By leveraging the transformer architecture of LLMs, organizations can analyze conversations to identify trends, sentiment, and key topics that are impacting business performance.

• Identifying Pain Points and Opportunities: Analyzing conversations allows organizations to pinpoint recurring themes related to customer feedback or employee issues. For instance, topics such as product defects or recurring customer service complaints can be automatically flagged for further investigation. This insight grants L&D teams the ability to directly address these concerns through tailored training programs.
• Enhancing Collaboration and Communication: By evaluating the dynamics of team discussions, companies can gain a better understanding of collaboration patterns. Are there communication gaps that need addressing? Do certain teams struggle with alignment on business goals? These insights can guide targeted training sessions aimed at improving teamwork and enhancing overall productivity.
• Feedback Loops for Continuous Improvement: Implementing conversational analytics creates a framework for continuous feedback and improvement. L&D initiatives can be adapted based on real-time insights gathered from employee discussions, ensuring that training remains relevant and effective in meeting evolving business needs.

​Measuring the Impact of Durable Skills on Business Outcomes
Once conversational analytics have been integrated into L&D strategies, measuring their impact, becomes essential for long-term business success. We can do this particularly well by leveraging the durable skills framework. Durable skills, alternatively referred to as "soft skills", such as communication, empathy, persuasion, negotiation, problem-solving, critical thinking, adaptability, and teamwork, play a critical role in enhancing employee performance and driving business outcomes. And they lend themselves very well for measurement in conjunction with conversational analytics.

To effectively evaluate success, I’d argue that organizations who are serious about positive change through training should consider the following aspects:

• Setting Clear KPIs for Durable Skills: Key Performance Indicators (KPIs) must be established to assess the effectiveness of training programs that focus on developing durable skills. I have seen one too many organizations shy away from this initial hurdle. Metrics such as employee collaboration levels, conflict resolution success rates, and customer interaction improvements need to be defined for a training program to work. For example, a surge in team cohesion after a collaboration-based training initiative can serve as a strong indicator of success.
• Linking Skill Development to Business Metrics: Organizations should actively connect the development of durable skills to broader business performance metrics. For instance, if a training intervention aimed at enhancing communication skills leads to improved sales conversations and higher closing rates, this serves as a clear proof point. Analyzing the correlation between training programs and these key outcomes demonstrates the value of focusing on durable skills.
• Utilizing Dashboards to Visualize Skill Impact: By creating comprehensive reporting tools that assess the growth of durable skills alongside business performance, stakeholders can gain insights into the effectiveness of L&D efforts. Dashboards that track progress in areas like communication and teamwork will help sustain executive buy-in and guide resource allocation for future skill development initiatives.

​Conclusion: The Future of L&D through Conversational Analytics
In conclusion, the integration of conversational analytics within Corporate Learning and Development strategies presents a profound opportunity for organizations to drive meaningful business outcomes. By harnessing the power of data derived from conversations, companies can gain insights that inform training initiatives, address key business challenges, and foster a culture of continuous improvement. As the workplace landscape continues to shift towards hybrid and remote settings, the relevance of conversational analytics will only increase. Organizations that leverage these insights will not only optimize their workforce but also remain agile in navigating the complexities of the modern business environment. The future of work lies in data-driven strategies where informed decision-making leads to impactful learning experiences, ultimately translating into enhanced performance and growth.
At Relativ, we help organizations experiment with their own data and understand how LLMs work with different contextual information, so they can expand these capabilities and begin to measure various skills that individuals exhibit during their conversational exchanges. We have developed advanced AI models that accurately measure essential durable skills relevant across multiple industries. The consistent performance of these models across diverse contexts reinforces the essence of “durable skills”, highlighting their ability to seamlessly transfer and apply across various work environments. These models enable us to create simulated environments that facilitate high-stakes conversations, where individuals can practice and refine their durable skills. Through personalized feedback, we empower users to enhance their performance in these critical interactions. To learn more, and get a demo of our proprietary conversational analytics engine, head over to our website or reach out to us to learn how we can help you deploy your own AI models, infused with psychology, and linguistics, to empower your organization and end users with the durable skills they require to meet the challenges of the future of work.