Unlocking A.I. Potential: Insights from Stax’s Consulting Expertise

Stax, a global strategy consulting firm serving private equity and investment banking clients, brings relevant and recent experience working with a variety of clients on A.I.-related projects and diligence. Working on 300+ client engagements annually, we’ve observed companies eager to integrate A.I. into their strategies but hesitant to act.

A recent case revealed surprising insight, where management of a DevOps company had begun A.I. integration efforts, specifically for customer-facing use cases. During our diligence process, we uncovered how their competitors were taking similar strides and, in some areas, outpacing our client's integration strategy. Despite thinking they would be ahead of the curve, they ultimately found themselves not as far ahead of the market as they had anticipated.

Similar to the aforementioned case, conducting a competitor analysis may uncover that you are not as ahead of the A.I. curve as once thought. This realization, along with our experience with similar projects, has underscored the significance of thorough planning prior to launching a platform or tool. Rushing into implementation without establishing a solid foundation may create vulnerabilities or incompetencies, leading to delays or the need for extensive revisions, ultimately wasting valuable time in the continuous pursuit of innovation. 

Related: Prioritizing Efforts in Private Equity as Gen AI Impacts Broaden

We’ve observed several key areas where A.I. integration may aid SaaS companies:

Customer Acquisition

Goal: Improve engagement and close rates

Customer Success & Retention

• Customer Onboarding: Personalizes approaches to maximize successful implementations 
• Customer Support: Offers proactive and on-demand assistance 
• Account Management/Benchmarking: Utilizes real-time sentiment analysis to drive customer satisfaction 
• Retention: Implements automated churn prediction and enhances renewal management efficiency to enhance Net and Gross Revenue Retention. 

Product Development 

Goal: Improve product value and differentiation

• Roadmaps: Streamlines feature prioritization 
• Engineering: Provides support with coding and testing 
• QA and Testing: Enhances quality assurance to accelerate testing cycles 
• Rollout: Ensures more efficient management of product updates and lifecycles 
  

Operations 

Goal: Streamline operational efficiency and decision-making

• Finance: Accelerates financial analysis and dynamic scenario forecasting 
• HR: Streamlines hiring and onboarding processes while enhancing employee retention strategies 
• IT: Decreases ticket submissions by deploying chatbots and allocates resources to more complex tasks 
  

Taking this into consideration, our consultants ultimately decide where to focus a company's A.I. efforts through a few questions:

1. What is the company’s strategy, and how can it be best reinforced or advanced through GenAI? 
2. Where would the integration of GenAI have the greatest impact? For example, is there stagnation in customer awareness? Are sales throughput low or declining? Is retention an issue?

Related: Early Implications of Generative AI for Private Equity Investors

Once these questions are addressed, companies then need to understand how to tailor their approach to maintain GenAI utilization.

This is primarily accomplished by reframing it not as a completely new process but rather as an augmentation of previous methods. This includes educating existing staff and leadership on the capabilities of AI with the intention of establishing an “A.I. Council.”

The formation of an A.I. Council could prove advantageous given the complexities associated with utilizing GenAI. The need for swift decision making can be overwhelming for an individual to handle alone, which is why we see companies beginning to form these councils as a response to this challenge.

While overseeing decisions, the council will also help to ensure that only high-quality data is being gathered and utilized to train models. Many companies struggle to capture the necessary data to train these models effectively, emphasizing the importance of implementing quality standards to avoid any gaps or deficiencies.

One way to gather high-quality data is by incentivizing employees to regularly utilize in-house A.I. tools. This not only enriches the algorithm’s learning and comprehension of processes, but also ensures the continuous addition and update of new, high-quality data, thus maintaining the effectiveness of the current model.

Lastly, it’s important to focus A.I. efforts on immediate practical applications. By utilizing A.I. for simpler and more immediate tasks, the information gathered—and the relative time saved on those tasks—will enable the next implementation of A.I. tools to be better equipped in serving and addressing user needs. In short, these factors compound together to create a more cohesive and built-out platform which will ultimately perform better as more data is collected. 

Conclusion 

When it comes to understanding the viability of a market for investment, Stax is where value is created. Our approach, centered around providing data-driven and actionable insights, enables clients to make informed decisions that lead to providing the most competitive returns. To learn more, visit our website www.stax.com or contact us here.