Author: Mike Urgo
If you have been living under a rock and are wondering who “Al” is, I am sorry this isn’t referring to Paul Simon and his bodyguard. The purpose of this newsletter is to share some of my high-level thoughts on Artificial Intelligence or “AI” and how it can be practically applied in the workplace today.
To be candid: I haven’t been this annoyed by a topic since collaboration and synergy were the magical buzzwords that were going to solve everyone’s problems. Not because artificial intelligence isn’t going to be effective, spoiler: it’s been used and effective for decades. But because of the common misconceptions surrounding it are starting to cause more harm than good. I am not going to waste time listing all of the misconceptions, except to say that, like synergy and collaboration, without the proper leadership, planning, and change management; it will be about as effective as a pizza party on a Friday on the first week of a return to office mandate.
Based on my experience and observations, at the highest level, we have a perfect storm on our hands right now in the market that can be harmful to organizations if they aren’t strategic in their use of AI. On one hand, there are significant improvements on AI capabilities on almost a weekly basis, which creates this sense of urgency to “not miss out”. On the other hand, we have a very volatile economy which is driving organizations to find areas of cost savings wherever possible. The promise of long-term cost savings from organizations and quick implementations can seem very attractive on a proposal slide. But like anything in life, sometimes it can be too good to be true.
See, the thing is, for an AI solution to be effective, it needs to have clean data to learn from and use. It also requires either an internal or external support team to ensure it grows and scales appropriately for your organization. Now, every situation and tool are unique, and there are countless applications of artificial intelligence right now. The goal of this week’s newsletter is to stay at a high level and give you and your team some food for thought before you start your AI journey.
These are the three steps that I think can lead to an effective engagement with your team and AI. Step one is to work with your internal team(s) to start identifying potential areas where artificial intelligence can help. Think of this step as a brainstorm session. Nothing is off the table and all ideas should be captured. This is important, because depending on the complexity of your business, AI could have many downstream impacts and or applications. It’s important to not focus on just one area and try and paint as broad of a stroke as possible. These should be cross-functional conversations to ensure nothing is missed. These cross-functional conversations can be the most challenging, especially when different areas are fighting for budget, but to create the best comprehensive solution, it’s best practice to work with all areas of the business.
Step two may need to involve a third party to qualify the art of the possible.
The team needs to understand the reality of the current state.
How clean is your data? Truly.
Are your current processes documented and followed?
What is the appetite for change within your organization?
How complex is the problem you are looking to solve?
The most impressive thing with AI right now is that the possibilities of its application can seem endless. The scary thing is, that AI’s endless capabilities can create a false sense of simplicity within your organization, especially leadership. I think it’s easy when trying to get any type of technology approved to over-simplify the solution and downplay the level of effort for implementation, change management and maintenance.
Talk to any implementation leader, project manager, or product owner and they will have plenty of stories of implementations that went “sideways” because there wasn’t enough due diligence at the start. If you’re looking for more detailed thoughts on this practice, check out Episode 8: Measure Twice, Cut Once.
Now that you have identified all the possible areas where AI can help your organization, and you understand current state, the last step is to draft a roadmap. This roadmap needs to be realistic and must at least consider:
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- Organization Impact (Change Management Efforts)
- House cleaning efforts (data cleansing, process documentation, software upgrades, etc.)
- Realistic capabilities and bandwidth of internal resources
- Potential third-party assistance
- Total cost of implementation and total cost of ownership (support, licenses, etc.)
If you only take away one thing from this week’s episode it is to be wary of solutions that can seem too good to be true and timelines that perfectly fit into what you want as an organization. There’s blood in the water right now and there are sharks in the water looking to make a fast buck. The reality is that many organizations will be implementing AI tools over the next few years. It’s my opinion that the organizations that follow these three steps will spend less money in the long term than organizations that are looking to cut costs and time now.
By cutting implementation costs and accelerating timelines with stretched resources, you are opening your organization up to costly rework and delays. Rework and delays to projects only create frustration and thus drive down buy in and compliance. I would also caution your internal leadership the risks of running efforts in parallel. At times, when pressured by executives to accelerate timelines, vendors will suggest cleaning up data and processes in parallel to implementation. I strongly advise against this. Typically, when documenting process, or cleaning data teams will find anomalies that have significant impact on the design and building of the new solution. This would result in the aforementioned delays and additional cost.
So, if you and your organization are starting to look at implementing AI into your technology stack, or improving on some existing capabilities, I highly recommend following this three-step process of identifying all potential opportunities, understanding your current state, and drafting a realistic roadmap.
Have you or your organization run into any of these challenges? Do you have any other recommendations for others who are starting their journey into artificial intelligence?
Let me know in the comments!
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