Driving double-digit growth for the past five years in the AI transformation space is no mean feat. This makes our interviewee, Stephanie Stockin, a bonafide ‘pundit’ when it comes to the latest trends in AI.

Our interviewer Kenny Small comes with over a decade of experience in both software development and quality assurance. He currently leads the Qualitest Enterprise practice globally apart from advising Fortune 500 clients across all levels on their SAP transformations. 

With both SAP and AI taking center stage in our latest offering Coco, this one-on-one chat between two seasoned Qualitest veterans is both fortuitous and timely. So, prepare yourselves for a riveting dialogue that sheds light on:

  • the challenges associated with adopting AI
  • how organizations can ready themselves for an AI transformation
  • how organizations can transition towards that AI-first status
  • the primary reason why we should leverage AI at all

Let’s fire up those neurons!

Kenny’s tête-à-tête with Stephanie: here’s the full rundown

Kenny: Hey Stephanie, it’s been a month now since we met at the Americas leadership kickoff!

Stephanie: Hey, Kenny! It’s nice connecting with you again. As we learned at that conference, SAP Sapphire 2025 is one of Qualitest’s top priorities for this year.

Kenny: Yes indeed, it is. With the event only a month away, it’s vital that we communicate what AI can do for SAP. With it being all the rage these days, our social media strategy and marketing efforts must cover this angle.

Stephanie: Yes, that makes sense.

Kenny: With your deep expertise on the subject, I thought I could pick your brain with a few questions today, but keeping the world of SAP and our customers in perspective.

Stephanie: Sounds good!

Kenny: Great! Let’s get cracking! Here’s the first question: Are organizations ready to leverage AI effectively? If they’re not, then why?

Stephanie: Businesses desperately want to be AI-ready even if Big Tech continues to set the bar. It’s the very reason why enterprises use their [Big Tech] models in making that proverbial leap to adopting AI. However, there are other factors that also serve as a hindrance, and which are worth addressing here.

First, there’s a lack of skilled talent. Particularly a dearth of AI thought leaders who can drive strategy within their organization, keeping both the engineering and product standpoint in mind. So, with no access to model-making talent, teams have no choice but to upskill, primarily by attending conferences. This is also why enterprises fall behind if building their own Copilots and AI Agents is a priority. Of course, this is where Qualitest can bridge the gap, provided these companies can invest in our AI capabilities.

This brings us to another problem enterprises face today. High implementation costs. Adopting AI really depends on whether the enterprise is forward thinking. This becomes evident if they’re clear about what AI can do for their customer base and their return-on-investment (ROI). Again, making such a decision rests in the hands of leadership, their thoughts and priorities. Other initiatives may have been planned and remain urgent but in this race to the top, AI is clearly something enterprises need to really look into adopting.

Often, it’s just the high implementation costs that inhibit enterprises from boldly taking the leap, even if other constraints such as security and data are deciding factors. The sensitivity surrounding the use of Personally Identifiable Information (PII) – specifically customer data – comes to mind here.

That said, and most importantly, if an organization has not thought about how AI can transform their business from an operational efficiency standpoint, they need to start their evaluation there. Internal operations are often the easiest to let AI help automate and free up employee time for more complex tasks. I really like where AI is taking expense reporting these days.

Kenny: Thank you, that was very insightful. In fact, I’ve made notes. Alright, on to the next question then: let’s say, you were to advise a CEO or AI teams on AI strategy. What would be the #1 area to focus on?

Stephanie: If you’re looking for the easiest and quickest way to drive AI strategy, finding a good partner [like Qualitest] makes all the difference. That is if the enterprise does not have plans to build out an internal AI team and infrastructure and even then, there are certain partners that need to be relied on for flawless execution.

Kenny: Interesting. So, how do you measure that you’re AI-ready? What are the signals?

Stephanie: Most companies measure their progress with meeting quarterly and annual KPIs as a yardstick for success. These goals matter for company progression and growth. One of the biggest key performance indicators to measure your success on is asking, is my data ready? Many organizations have tons of unstructured data that needs to be organized to efficiently start any AI initiative. Other questions to ask include, are your data sources integrated? Or are they siloed in different departments and systems? Structuring data and organizing it is a first best practice to set your organization up for success. If you need help in doing this, feel free to talk to one of our experts as we want to help.

What you also need, apart from organized data, is a company-wide commitment to AI transformation. Companies have individuals building AI skills as opposed to investing in AI transformation organization wide. If you expect a select one or two individuals to carry an AI strategy forward, the main fact is they can’t. Individual readiness without the commitment to systematic change will not only cause turnover but barriers in breakthrough. AI operationalization can be adopted and built. Some best practices include hiring, investing in upskilling, incentivizing AI adoption, creating an AI taskforce and mission, encouraging usage of AI experimentation through secure sandboxes and the use of opensource apps.

Kenny: You spoke about the possibility of AI adoption in industries and the unique challenges faced? Which industry is most primed for disruption? Which industry has the most opportunity?

Stephanie: Retail appears to be the industry most ripe for disruption, given the vast opportunities to capitalize on. For one, it offers the potential for immediate returns on investment, as boosting the point of sale and increasing transaction values are key focuses. With minimal investment, businesses can see quick results. In contrast, industries like legal and healthcare involve handling sensitive personal data, which requires significant investment in security to protect customer privacy when introducing new generative AI technologies. Retail, on the other hand, offers greater flexibility in personalizing the customer experience, which can directly drive profits and revenue, all while facing fewer regulatory constraints.

Generative AI has revolutionized the personalized shopping experience, and this user experience is directly tied to an increase in retail profit. A great example is looking at Amazon. Amazon recently released an AI tool that uses Gen AI, ML and NLP technologies to optimize product listing and search results. Amazon Rufus helps tailor recommendations based on browsing history, trends and preferences to increase conversion rates. It helps define products by adding keywords and features to help with features that help me in the vast decision making of what product to buy on Amazon.

Using open-source models to build Gen AI applications is a smart approach, as the performance gap with closed-source models is rapidly closing. Developers also gain valuable ‘playground time’ to experiment and iterate. After reviewing the results, they can determine which model best meets their needs. From there, it’s crucial to evaluate the necessary investment. At Qualitest, we guide companies in building Gen AI apps by offering expert recommendations on model selection and design, ensuring they make the most informed decisions.

Kenny: Someone said to me: Closed source models is like building out the twentieth floor of the penthouse while open source means you’ve only got floor three. You’ve got to decide what you’re comfortable with as a company or team. Both have their opportunities. Please share your thoughts.

Stephanie: A lot of the open-source models that I’ve played around with are from Meta, LLaMa models are efficient and easy to customize. I have seen a lot of advancements on the closed source side, and I like Anthropic’s approach to model safety. I got to hear Kevin Weil, CPO from Open AI speak in an intimate Q&A session recently and he highlighted how ChatGPT is going to be doing things for us and how many agents we can have working at one time solving problems for us. Who wouldn’t want a true virtual assistant beyond what the definition is today, one who is capable of completing tasks? This is going to heavily optimize our workdays in one to two years. With all that we’re learning, the cost of building models will continue to decrease. What this will do is make room for more enterprises to drive AI across their organization.

Kenny: You mentioned that we’re going to be heavily optimized in two years’ time. What does an AI-first organization look like? What does AI-first mean to you?

Stephanie: It must start with the leadership. They’re the ones who must adopt the mindset of efficiency and optimization along with the desire to take safe risks. Currently, several enterprises are not implementing generative AI as they are in wait-and-see mode or are limited by budgetary constraints. This is even though their employees are using AI for work, thanks to a ton of available apps. So yes, while they are using AI internally, they’re not thinking about offering AI capabilities for their customers.

Now, when I think of what defines an AI-first organization, the leadership should get onboard with the idea, but the culture must be data-centric. This is particularly true if you aim to be an AI-first organization. Once you’ve identified the biggest opportunity for growth, you can leverage AI towards that end.

Another aspect involves the entire company focusing on digital transformation to the point where employees know which AI tools are at their disposal. So, if all employees are used to working with AI tools, its adoption as part of a product for customers will come naturally. Cross-functional integration of AI across departments will only spur this change further.

Once we have these things in place, it’s only then that organizations will ask important questions such as: Where are we going? What products do we want to power up with AI? How will we enhance AI-powered products? What model improvements are we making? Which will require them to engage with partners [like Qualitest] to drive AI-focused innovation.

Kenny: What is the most exciting thing that you’re seeing in AI today?

Stephanie: From a personal perspective, I find the advancements in robotics to be incredibly exciting, especially as they’re poised to make a significant impact on industries like healthcare and supply chain management. Additionally, Artificial General Intelligence (AGI) holds the potential to revolutionize virtually every aspect of human life. I believe AGI will greatly enhance human cognition, leading to groundbreaking breakthroughs across various fields. One area I’m particularly hopeful about is AGI’s role in helping to uncover cures for diseases that have long seemed out of reach.

Even though AI agents and other work-related AI capabilities simplify responsibilities already, there’s a lot more in the works. So, despite taking 20 years to get this far, the world will turn and work faster than you can imagine. Without a doubt, AI will be a technology that we cannot live without.

Introducing Coco – our Copilot for SAP – at SAP Sapphire 2025

As you might have discovered, this interview offered a glimpse into the mind of a seasoned leader whose passion for Generative AI is only matched by her ability to fuel growth opportunities in her current role.

We, at Qualitest, also believe in the ROI that AI transformation offers. This is reflected in the way AI plays a significant role in the services that we offer, with the latest being Coco – our Copilot for SAP. If you are looking for a toolchain that drives quality using intelligent automation throughout the lifecycle, we can help.

Find us at Booth #454 during the upcoming SAP Sapphire Orlando 2025 event to see what Coco can do for you.

Book a meeting now.