Reinforcing Community Efforts: Developing Purposeful Customer Interactions through Generative Artificial Intelligence
At AWS, Jeffrey Hammond assists software companies in expediting product delivery, creating new income streams, and decreasing technical debt.
As software companies look for fresh opportunities for profitable growth, generative artificial intelligence (GenAI) is poised to deliver revolutionary customer experiences for next-generation software products. The hype surrounding this rapidly evolving technology is hard to overlook.grandiose statements have been made about its potential to alter how businesses operate and thrill their customers. We may be on the brink of a transformation not seen since the early days of the browser or the mobile paradigm shift.
The choice to be an early adopter or a swift follower of generative AI will be a significant decision that product leaders must make. However, while the seismic potential of this technology is extraordinary, it's crucial to maintain a level head. As with any disruptive technology, adopting a strategic approach to product investments will yield higher returns in the long run and help create intentional experiences that delight both customers and employees.
Why a Well-Defined Use Case is Essential
From conversations with software leaders, it's evident that most companies are approaching generative AI with cautious optimism. Recognizing the potential is balanced by a desire to invest wisely, manage risk, and control costs. This begins with a clearly defined use case.
Most companies I work with are investigating how generative AI can generate business value in two ways:
- Enhancing operational efficiency in software delivery, sales, and customer support processes.
- By integrating generative features directly into products.
The former concentrates on optimization and boosting margins, while the latter focuses on innovation that sets products apart and drives revenue growth.
Integrating generative AI into the products you sell can come with risks, such as managing hallucinations, keeping the cost of goods sold in check, or safeguarding customer data. However, when product teams manage these risks effectively, this approach can yield significant returns. GenAI-integrated software products are already assisting companies in creating market differentiation, generating additional growth, streamlining user experience, enhancing automation, and summarizing operational data. For instance, storage and information management provider Iron Mountain (an AWS partner) is utilizing generative AI to help its customers illuminate "dark data," from physical document digitization to unlocking hidden insights through AI-powered applications.
According to a McKinsey study, surveyed companies attributed more than 10% of earnings before interest and tax (EBIT) to the technology. While the same study indicates that product and/or service development is a frequently cited use case, overall adoption is at only 10% of respondents. As this figure is only going to grow, let's explore how product teams can create valuable, intentional product experiences with generative AI.
It's All About the Customer
It might sound obvious, but simply embedding generative AI features into products won't automatically generate value. How customers react to new AI capabilities will vary from business to business, particularly as the inclusion of those capabilities often necessitates price increases. It's essential to keep the customer at the center of your decision-making process.
The graphic design platform Canva, (an AWS customer), is taking an organizational approach, developing core generative AI capabilities and making them available for product teams. Their Magic Studio features can generate designs, text, edits, and more, helping people reduce the time spent completing tasks by approximately 40%.
Accountancy software provider Xero (also an AWS customer) has concentrated its investments on a new AI business companion. Just Ask Xero is an AI co-pilot designed with the single purpose of reducing customer toil, or time spent on monotonous financial administration tasks. This concept is one that any product team can employ as they look to innovate.
Having hosted a webinar with product leaders from both companies about generative AI’s path to production, I've gained further insight into how these approaches are being executed. Beyond these examples, other companies like Harvey have customers upload and store years (or decades) of dark data, and its product, Vault, delivers legal insights at scale and allows customers to use the data in automated workflows with agentic co-workers using Open AI o1.
The approach businesses should take to embed generative AI into products depends on their organizational structure, customers, and competitive landscape. Will you extend the functionality of existing products, or will you focus your efforts on a new AI companion? If it's not immediately clear which direction to take, work backward from the customer and invest wisely by sowing small seeds that allow for much testing. Observability is essential.
There's a lot of technical considerations to ponder over, ranging from ethical AI use and governance to assessing and selecting the right models. Evaluating the abilities and efficiency of large language models is intricate. It's not just about the speed of response, but also the size of the context window, which determines how much input can be fed to the model and the size of the output. Various factors influence the cost, which in turn affects performance and quality. Opting for model fine-tuning increases cost but enhances output quality. Striking a balance between accuracy, performance, and cost is a delicate task.
Focusing on Purpose
Generative AI is rapidly advancing and has the potential to drive significant growth. Identifying unique product use cases is crucial, along with nurturing in-house skills and constant testing. Tech firms that excel in these areas will be primed to create disruptive products that provide intentional user experiences, rather than being driven by the need to follow technology trends.
The Exclusive Tech Leaders Group, an exclusive network, invites top-tier CIOs, CTOs, and technology executives. Am I eligible?
The Exclusive Tech Leaders Group, which welcomes top-tier CIOs, CTOs, and technology executives, includes Jeffrey Hammond as a member due to his expertise in leveraging generative AI to benefit software companies.
To make the most out of generative AI's potential for businesses, Jeffrey Hammond emphasizes the importance of outlining a clear use case, whether it involves boosting operational efficiency or integrating AI features directly into products for differentiation and revenue growth.
