INSIGHT
Pace and precision: How software companies can maximize generative AI’s value
by Ben Schreiner, Head of Business Innovation and Go-to-Market Strategy, AWS | 16 Apr 2025 | Thought Leadership
Overview
When you move too fast, it’s easy to miss important details. Mistakes are made. Opportunities pass you by in a blur. Often, it’s only when you pause and take note of what’s happening around you that it’s possible to create a more sustainable path to success. In my experience, this is a phenomenon that’s also true when it comes to adopting new technologies.
Recent research by Forrester shows that 63 percent of software companies surveyed launched at least one generative AI product in the past six months. But the findings also indicate that urgency and a lack of strategic planning could be impacting prospects of long-term business value. In fact, the study found that software leaders’ top generative AI challenges highlighted difficulties differentiating products, predicting market needs, and operating profitably.
Despite enthusiasm to experiment with proof of concepts, many software companies struggle to get them into production. In chasing generative AI opportunistically, they can bypass viable business models. It’s only when companies unite employees, business processes, and customers throughout the journey that they can drive lasting value.
Recent research by Forrester shows that 63 percent of software companies surveyed launched at least one generative AI product in the past six months. But the findings also indicate that urgency and a lack of strategic planning could be impacting prospects of long-term business value. In fact, the study found that software leaders’ top generative AI challenges highlighted difficulties differentiating products, predicting market needs, and operating profitably.
Despite enthusiasm to experiment with proof of concepts, many software companies struggle to get them into production. In chasing generative AI opportunistically, they can bypass viable business models. It’s only when companies unite employees, business processes, and customers throughout the journey that they can drive lasting value.
Make talent and tech a tag team
Finding and retaining the right people to develop AI isn’t the only hurdle for software companies. 39 percent of software leaders see change management as a key challenge in developing generative AI. Ultimately, to maximize value from new technologies, people across the organization need to feel empowered to both embrace them and continually improve their outputs. If not, adoption rates and results are likely to be limited at best.
Of course, preparing teams for the evolving world of work doesn’t happen overnight. But by evaluating generative AI’s impact on them and supporting with ongoing training throughout the transition, it’s possible to augment workforces. Although some functions will be automated, most people can level up in their roles—gaining time and unlocking insights to help them work more strategically. By encouraging people to see generative AI as an ally, rather than a replacement, software companies can expect to see a significant productivity uplift.
When embedding this cultural change, championing a human-in-the-loop approach is also critical for validating outcomes. Upskilling individuals who are developing and utilizing AI systems can foster greater trust in outputs and prevent hallucinations—especially given that AI systems are predictive, not deterministic. With effective human oversight, people and technology can work in harmony and generate more business value.
Of course, preparing teams for the evolving world of work doesn’t happen overnight. But by evaluating generative AI’s impact on them and supporting with ongoing training throughout the transition, it’s possible to augment workforces. Although some functions will be automated, most people can level up in their roles—gaining time and unlocking insights to help them work more strategically. By encouraging people to see generative AI as an ally, rather than a replacement, software companies can expect to see a significant productivity uplift.
When embedding this cultural change, championing a human-in-the-loop approach is also critical for validating outcomes. Upskilling individuals who are developing and utilizing AI systems can foster greater trust in outputs and prevent hallucinations—especially given that AI systems are predictive, not deterministic. With effective human oversight, people and technology can work in harmony and generate more business value.
Let data lead your processes
Data is the guiding light for building more profitable workflows and more powerful user experiences. Many software companies are aware of this, with Forrester’s study also revealing that 63 percent want to leverage data and analytics to inform their decision-making. However, before they can unlock its value, there needs to be a holistic understanding of that data. This means knowing where data comes from, what it’s used for, and how it should be protected. If not, there’s a real risk of automating bad workflows or failing to address user challenges.
A case in point is when companies overlook customer needs—especially when working backwards from users is what drives high value products. Yet responses from the Forrester survey suggest that under a quarter of software leaders see superior customer support (24 percent) and enhanced user experience (23 percent) as a top priority. Added to this, over two-thirds of respondents underestimated the importance of data security and privacy to their customers. Aside from leaving them open to risks and fines, this could create a drop in user satisfaction and retention rates, significantly impacting profitability.
To understand and act on user needs, departments across the organization should be aligned every step of the way. IT teams need to be closely connected to the business executives who know the questions that need to be answered by data. Once they have that data, they can figure out how to be purposeful with solutions. Setting clear expectations for generative AI responses—including creating guardrails and defining narrow measurements of success—will help companies stay in control of data and get the most relevant outputs for their users.
A case in point is when companies overlook customer needs—especially when working backwards from users is what drives high value products. Yet responses from the Forrester survey suggest that under a quarter of software leaders see superior customer support (24 percent) and enhanced user experience (23 percent) as a top priority. Added to this, over two-thirds of respondents underestimated the importance of data security and privacy to their customers. Aside from leaving them open to risks and fines, this could create a drop in user satisfaction and retention rates, significantly impacting profitability.
To understand and act on user needs, departments across the organization should be aligned every step of the way. IT teams need to be closely connected to the business executives who know the questions that need to be answered by data. Once they have that data, they can figure out how to be purposeful with solutions. Setting clear expectations for generative AI responses—including creating guardrails and defining narrow measurements of success—will help companies stay in control of data and get the most relevant outputs for their users.
Reflect, but don’t stop moving forward
Generic use cases like image generation and multilingual support can only take you so far. Yet only 36 percent of software leaders expressed interest in building generative AI infrastructure in house over the next 12 months, suggesting that many are opting for ready-made solutions to quickly harness the benefits.
As the landscape becomes even more crowded, novel capabilities and specialized solutions will be increasingly critical to carving out a competitive differentiator. Using data, you can track changing user needs to inform products, experiences, and business models over time. By regularly analyzing how users are interacting with the technology, offerings can be continually enhanced with features and capabilities that add real value-per-customer and boost sales.
As software companies reinvent their generative AI solutions, choosing those that solve a real challenge and can be feasibly executed will create the greatest return on investment. Currently, 35 percent of software leaders are investing in scalable infrastructure to efficiently handle growing data volumes. This suggests that the majority could run into technical roadblocks as they ramp up their generative AI initiatives. By prioritizing enterprise-grade security, compute, scalability, and integration at the beginning, these companies will be able to ensure a smooth flow of data between systems and take user feedback on board quickly.
Building the right infrastructure is just one factor for successful execution in the long run. You might have the best AI tools, but results can pass you by if you fail to look both outside and within your organization. Together with strategic prioritization, taking the time to foster organizational alignment and change management will help grow the value of solutions for employees, customers, and the business. See how AWS can help your software or technology company maximize its investment in generative AI.
As the landscape becomes even more crowded, novel capabilities and specialized solutions will be increasingly critical to carving out a competitive differentiator. Using data, you can track changing user needs to inform products, experiences, and business models over time. By regularly analyzing how users are interacting with the technology, offerings can be continually enhanced with features and capabilities that add real value-per-customer and boost sales.
As software companies reinvent their generative AI solutions, choosing those that solve a real challenge and can be feasibly executed will create the greatest return on investment. Currently, 35 percent of software leaders are investing in scalable infrastructure to efficiently handle growing data volumes. This suggests that the majority could run into technical roadblocks as they ramp up their generative AI initiatives. By prioritizing enterprise-grade security, compute, scalability, and integration at the beginning, these companies will be able to ensure a smooth flow of data between systems and take user feedback on board quickly.
Building the right infrastructure is just one factor for successful execution in the long run. You might have the best AI tools, but results can pass you by if you fail to look both outside and within your organization. Together with strategic prioritization, taking the time to foster organizational alignment and change management will help grow the value of solutions for employees, customers, and the business. See how AWS can help your software or technology company maximize its investment in generative AI.
About the author
Ben Schreiner, Head of Business Innovation and Go-to-Market Strategy, AWS
Ben Schreiner is Head of AI and Modern Data Strategy BD for AWS North America. He helps customers rapidly innovate, build new businesses, and grow faster than ever before by leveraging the industry's leading cloud platform. He has a blend of global Fortune 500 and tech startup experience, both as a technology customer and provider. He has been advising CIOs and business leaders for nearly 25 years and is based in Texas (US).
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