In the fast-paced world of business, the allure of using generative AI to transform customer service is undeniable. But before taking the plunge, companies face a timeless question: Should they buy or build? This age-old dilemma isn't unique to AI; businesses have long debated whether to develop custom solutions or opt for ready-made software. However, as AI technology advances and the market evolves, the dynamics of this decision are shifting.
While building custom software once offered unparalleled flexibility, purchasing pre-built solutions has become increasingly attractive—particularly for larger enterprises that may not have the specialized expertise in-house. So, what factors should businesses consider when navigating the buy vs. build dilemma for generative AI in customer service?
Insights from the experts: research on the build vs. buy decision
According to a Gartner report, a solid 60% of enterprises favor buying software over building it in-house. The reasons? Faster deployment, lower development costs, and the benefit of vendor support. However, Forrester Research suggests that the desire for customization hasn't entirely faded; 35% of organizations still consider in-house development for highly specialized needs.
The research highlights several key drivers behind the shift towards buying, including the increasing complexity of AI technology, the rapid pace of technological advancements, and the specialized skills required. While building offers the potential for tailored solutions, the challenges of cost, time, and technical expertise make buying a more pragmatic choice for many businesses.
The pros and cons of building in-house
On the one hand, building AI solutions in-house allows for complete customization. Companies can tailor their systems to their unique workflows, data handling requirements, and customer interactions. Plus, they maintain full ownership and control over their solution and its intellectual property.
However, the downsides are significant. Developing AI requires a hefty upfront investment in specialized talent, hardware, and software, not to mention ongoing costs for maintenance and improvement. Building from scratch also means a longer deployment timeline, delaying the realization of AI's benefits. And let's not forget the complexity and risk involved; many companies simply lack the experience to build, scale, and maintain these intricate systems effectively.
The advantages of buying off-the-shelf
On the other hand, purchasing pre-built software offers a quicker path to implementing AI in customer service. Businesses can start reaping the benefits almost immediately, without the lengthy development process. Established vendors bring a wealth of AI expertise to the table, supporting companies in integration, training, and scaling their solutions over time.
Buying also allows businesses to focus on their core competencies, leaving the AI development and maintenance to the experts. While pre-built software may not meet every unique requirement out of the box, the trade-off in customization is often worth the benefits in speed, cost, and reliability.
Choosing the right Gen AI partner
If opting for a pre-built solution, what should businesses look for in a Gen AI partner? Future-proof technology is crucial, with support for integrating various Large Language Models (LLMs) to ensure optimal performance without vendor lock-in. Seamless integration into existing ecosystems is also key, enhancing current operations without disrupting workflows.
A unified, omnichannel customer experience across email, chat, social media, and other platforms is essential. The ideal Gen AI tool should be user-friendly, offering intuitive interfaces accessible to users of all skill levels. And of course, it should enhance the end-user experience, providing clear, concise, and helpful responses to customer interactions.
Comprehensive reporting for data-driven improvements is another must-have, ensuring your AI evolves with your business. And finally, reliability and support are non-negotiable; choose a Gen AI provider known for dependability, quick resolutions, and consistent performance.
The bottom line: balancing buy and build
Ultimately, the decision to buy or build generative AI software for customer service depends on an enterprise's unique goals, technical capacity, and timelines. While building allows for deep customization, it's resource-intensive and risky. Buying, on the other hand, provides a faster, less risky path to leveraging AI's benefits, backed by expert support and proven technology.
The optimal approach? A balanced one. Consider buying technology for non-USP processes like customer service, and building custom solutions for unique use cases. By strategically combining off-the-shelf and in-house development, businesses can harness the power of generative AI while minimizing risk and maximizing results. The key is finding the right Gen AI partner to support your journey—one that offers the perfect blend of innovation, integration, and reliability.
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