?? Is AI adoption in B2B heading in the wrong direction?
Too often, organizations implement AI tools before identifying the real problem. Our latest B2B Virtual Coffee participants shared firsthand experiences of costly AI and digital projects that failed to deliver value.
?? Top-down AI decisions: A common pitfall?
One participant raised concerns about AI-driven pricing tools being introduced at the top level—without input from end users. The result? A system that didn't address real business challenges. Others recognized the pattern across different departments: leadership making technology decisions without involving those who will use the tools daily.
?? Why do software projects struggle?
Discussions also highlighted software project failures, especially in global organizations with multiple business units. A key takeaway: success depends on truly understanding user needs—not just technical capabilities. Examples of outsourced projects that lacked clear objectives were shared, leading to wasted investments. One expert emphasized the need to engage top management early to clarify the expected value behind tool purchases.
?? Challenging AI hype: Lessons learned
In another case, a business unit pushed for AI adoption after a seemingly successful pilot. However, when challenged on long-term strategy and real-world impact, they couldn't provide answers, and the project stalled. Others recalled similar situations, where digital initiatives were greenlighted without a solid business case. A key takeaway is that validating assumptions and challenging AI hype can save companies from costly mistakes.
?? Solving real problems before adopting AI
The group agreed that AI adoption should start with a clearly defined problem. One participant shared how a company invested heavily in an AI-powered sales forecasting tool only to realize the data quality wasn't good enough. The conclusion is that no AI model can fix bad data. A solid foundation is essential before layering on technology.
?? CPQ, ChatGPT, and the cost of misalignment
The team discussed how a CPQ system at a global company was misused as a pricing tool—despite not being designed for that purpose. With Multi M€ already spent, abandoning the project was not an option. This mirrors past ERP challenges, where companies struggled with all-in-one solutions instead of specialized tools.
Similarly, ChatGPT adoption raised concerns about data security, privacy, and the need for clear objectives. The conversation emphasized that AI is only as valuable as the strategy behind it.
?? AI, trust, and the future of decision-making
Participants explored AI's role in daily operations, from structuring tasks to data analysis. While AI tools are evolving, trust remains a key issue. They agreed that training and expertise in asking the right questions are critical for getting reliable results. As AI systems improve, adoption will increase—but the human element will remain essential.
?? What's your experience with AI-driven projects?
? Have you seen AI implementations that didn't solve real problems?
? What's the biggest challenge in aligning AI tools with business needs?
Drop your thoughts in the comments! ??
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