TMG recently had the pleasure of chatting with leaders in commerce and technology about pain points in their businesses, and on this occasion, focused around the topic de jour – AI. We felt we couldn’t keep these thoughts and ideas to ourselves, so we’re presenting these insights for broader use. Are you encountering the same issues? Have you wondered how other leaders are approaching these major advancements in technology? Here’s your chance to find out.
Balancing AI Hype and Reality
Pressure to incorporate AI is coming from the top down, and often the core of AI discussions are happening at senior levels in companies, but we’ve found that there’s little disbursement of responsibility down the chain, where employees are often doing the actual work of figuring out the use cases and protocol for AI.
Similarly, there is tremendous pressure to show ROI for new tools like this, necessitating a deep understanding of the potential benefits and thorough accounting for how data is extracted, stored, used, governed, analyzed, etc. Because of the intricacies of an all-encompassing AI strategy, leaders find themselves picking around the edges – using chatbots, search functions, and predictive product suggestions (which are great ways to begin as AI journey!) – instead of tackling the larger task of breaking down current tech stacks and starting anew with AI at the center.
The hype is now…but turning AI and data capture and processing into actionable output is a 18-24 month process. The challenge now becomes creating stakeholders, outlines, and goals to keep this project on track to deliver in this long time frame. Committing to an AI (and data) strategy makes the work tangible, with questions of governance and who should ‘own’ data. Turning AI into reality also means bringing data out of silos and into a centralized place of importance within a company. This is a shift for many, and one that might reorganize the entire company. The hype is strong now, but with a long runway to achieve results, organizations have to remain committed to creating a strong data foundation. The leaders we talked to are preparing for this change.
AI and Data Ground Rules
Regardless of the process, all execs were in agreement that the focus should be on deciding what business outcomes are ideal and where they can be augmented with AI, instead of the goal being only ‘let’s start using AI’. In a similar vein of looking inward, another leader questioned whether the net cast over AI was too large, and whether we are calling ‘AI’ things that are merely automation. That might be so. Despite encouraging the uptake of AI in business processes, this group agreed that top leaders and board members don’t really understand what AI and Generative AI is – and isn’t.
When the more tactical details of implementing AI were discussed, it became clear that no one was looking to build their own internal AI, but rather are looking for an out-of-the-box solution – from big providers or from smaller, niche vendors that they can customize.
Next Steps
In short, every executive in our discussion agreed that AI is indeed worth evaluating and planning for. What we discovered was that most companies – from varied industries and market cap, want to optimize their data collection, storage, and analysis to make way for AI, but are still working on creating the teams and plans to shift to this work, process, and organizational change. But make no mistake – it’s coming.
Have you started AI discussions within your company? TMG and our technology experts are well-versed in the questions and possible solutions that surround this space.
