The post discusses the challenges of building an in-house AI solution for B2B customer experience (CX) teams. While engineers can create impressive demos, CX leaders must decide whether to invest in a long-term internal AI initiative. The main concern is balancing customer-facing AI priorities with internal solutions. Even if an in-house solution were built, ongoing updates would be limited. The estimated cost of such an initiative—leveraging internal data scientists and engineers—would exceed $1M over 2–3 years.
Introduction
Customer experience (CX) leaders everywhere are eager to leverage AI to automate responses, personalize interactions, and reduce costs. Yet when it comes to B2B CX, the question often isn’t whether to use AI, but how: should you build a bespoke in-house solution or adopt a proven vendor platform?
Build vs. Buy: The Real Cost
Building an AI solution internally may look attractive—you control your data, tailor features to your workflows, and avoid vendor lock-in. But it comes at a steep price. According to a Head of AI at a major SaaS platform, simply staffing data scientists and engineers for an MVP and one iteration can exceed $1 million USD over 2–3 years. Beyond initial development, ongoing model maintenance and data updates demand dedicated resources and budget.
Industry surveys show that many CIOs are abandoning in-house proofs of concept in favor of commercial AI: about 50% of companies had internal AI efforts in late 2023, but that number fell to 20% by the end of 2024 (CIOs increasingly dump in-house POCs for commercial AI). And in Asia/Pacific, only 36% of enterprises plan to build GenAI models from scratch, with the majority opting to fine-tune existing platforms or adopt SaaS solutions (Build or Buy? IDC Reveals Asia/Pacific Organizations' Choice in ...).
Time-to-Value and ROI
Even if you overcome cost barriers, delivering business value fast is critical. IDC finds that most AI deployments take under 8 months to implement and organizations begin realizing measurable ROI in 13 months (IDC's 2024 AI opportunity study: Top five AI trends to watch). Generative AI investments return $3.70 for every $1 spent on average (and up to $10.30 for top performers) (IDC's 2024 AI opportunity study: Top five AI trends to watch).
However, Forrester reports that nearly half of AI decision-makers expect ROI within 1–3 years, while 44% anticipate longer payback periods (Rushing for AI ROI? Chances are it will cost you - CIO). In practice, 51% of organizations see top-line revenue gains and 49% see bottom-line improvements from gen-AI, with 41% citing risk avoidance as a key benefit (Areas Of Positive ROI From Generative AI Are Now On Par... | Forrester).
Risks and Failure Rates
The path to AI success is littered with pitfalls. Gartner predicts that at least 30% of GenAI projects will be abandoned after proof-of-concept by the end of 2025 (A third of all generative AI projects will be abandoned, says Gartner). Poor data quality is another killer: up to 85% of AI models fail or underperform due to inadequate or irrelevant data (Why 85% Of Your AI Models May Fail - Forbes). Even projects that clear the pilot stage often struggle to scale—studies show that 75% of AI initiatives stall when moving beyond initial deployment (Why 75% of AI Projects Fail to Scale and How to Fix it? - LTIMindtree).
Key Considerations for CX Leaders
Conclusion
While building an in-house AI can deliver maximum customization, the resource demands, extended time-to-value, and high failure rates make it a risky path for most B2B CX teams. Organizations seeking rapid impact and predictable costs may find that partnering with an AI vendor offers a better balance of innovation, support, and ROI—allowing CX leaders to focus on customer needs, not on infrastructure.
