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The Pitfalls of AI – Getting Lost, Stuck or Burnt Out
Emerging AI tools generate new challenges as well as new opportunities
3 Min Read
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Executives are faced with generating value quickly from deploying AI across their enterprises. The urgency to show they’re ahead of the competition is seen in the wave of press releases, proclaiming the rollout of new AI tools that will move the needle for the organization.
JPMorgan Chase rolled out “LLM Suite” for its asset and wealth management employees to provide them with a digital “research analyst”. Morgan Stanley announced the rollout of Morgan Stanley Debrief to its wealth advisors, as a “digital assistant” for notetaking, email drafting and other daily tasks. Royal Bank of Canada (RBC) has also announced that it is piloting AI tools for its financial advisors. These announcements are consistent with the 2024 Broadridge Digital Transformation study that found 45% of organizations allow staff to use GenAI tools for work purposes.
These emerging AI tools not only generate new opportunities but also new challenges. A shortlist of common pitfalls is emerging as more enterprises explore AI:
Data management quagmire: Underestimating the complexity of preparing data for AI, constraining the ability to generate working AI models that deliver their intended value.
Adoption/change management challenges: Unprepared employees being tasked with using AI tools in their “day-to-day” without the needed support, leading to unused costly tools and no productivity gains.
Use case selection paralysis: Endless cycles of assessing use case value without follow-through solutions create mountains of slides without a working MVP.
Proof of concept burnout: Ill-formed AI experiments that do not generate useable learnings and drain investments.
Data privacy and security concerns: Not meeting data protection and security standards, resulting in unviable solutions and wasted development costs.
AI safety concerns: Neglecting safety considerations, leading to reputational damage and regulatory issues.
Each of these pitfalls leads to getting lost, stuck, or burnt out on your AI journey. Where does this leave the executives tasked with guiding their enterprises past these pitfalls? The executive's role is to deliver on the following:
Set a focused vision for AI in your organization. Are we prioritizing employee productivity? Client business value? Strengthening strategic partnerships through deliberate co-creation?
It’s buy and build not buy v.s build. What’s the right mix of market-leading LLMs and in-house models? How does that mix solve the problems that matter most to you? Who are the right partners to make it happen?
Cultivate a hands-on AI culture. What safe and secure environment do my people need for their AI experiments to flourish?
Success in AI isn't just about technology—it's about reimagining a “day in the life…” of your people while maintaining the highest standards of security, compliance and customer trust. With deliberate planning, strategic investment and a commitment to continuous learning, you can avoid getting lost, stuck, or burnt out and instead lead your organization to thrive in the AI era.
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