Artificial intelligence is on the train track. The wave has begun in the arena. It cannot be stopped!
A Hockey Game versus A Strategy of Process and Individual Intelligence
We are in TD Garden and watching the wave! A hockey club parallels many characteristics in business. OK … what are they?
- Recruiting and On-boarding: Finding the open positions as well as the talents, experience and skills needed.
- Business Strategy: Defining the problems and opportunities by analyzing the marketing, finance, and operations. Maybe a SWOT or Gap Analysis?
- Operational Strategy: Monitoring and assessing business operations through definitive quantitative and qualitative measures (forward looking). Assessing those measures using operational data (backward looking) using data analytics.
Ultimately, a lot of thinking! Oh no … I will integrate hockey as a metaphor. 😊
The Pre-Game of AI Hockey!
The goal is to compile and implement a strategy to improve (notice I did not say reduce) costs and increase the winning percentage. Therefore, hockey teams should just integrate the player’s game (and opponent’s statistics) into an AI application. To WIN!
Team personnel will use the various buzzwords being stored by technology gurus such as … deep-learning, natural language generative, reactive machines, limited memory, theory of mind. Great! The current mindset is to save operating costs and reduce labor. Integrate artificial intelligence into the current process.
The ability to gather, summarize, and report data as well as a strategy narrative will be completed in less than the time it takes to brew your favorite coffee.
Then, send the results electronically to the coaching staff. Then, before putting the sugar in the coffee, send the results and narrative electronically to each individual players for the next game with the following specifics:
- Assigned a position they will play and line they are assigned to,
- Specific game tactics for them that requires their focus and mistakes to avoid, and
- Specific play-making tactics for their opponents and teammates.
So easy! The coaches spend less time analyzing the videos. Less time needed to compile feedback from assistant coaches and players to formulate a plan for the next game. Heck, they do not need as much hockey and coaching experience! Why?
AI can use its technology, data storage, and immense computing ability to replicate the team’s coaching process. AI technology uses its comprehensive data points (including game videos) to replicate the coaching strategy process that has existed for decades. The customized, individual player strategy generated by AI reduces the time needed for meetings with players.
Now … focus on execution during the actual game. Easy! More effective and efficient (former students, remember the difference?!) 😊
However, can AI accurately define a “slump” in the previous game’s activity and statistics? Is a one-game “slump” a slump? Is a negative change to a player’s statistics a slump?
Sure, AI and data analytics can find outliers. However, could it be transactional based on the last opponent? The lines in the game? Individual opponents that the player confronted? But wait, why didn’t the AI output predict the game? OK, being a bit sarcastic here, but please read on.
So, What's Missing?
Was it off-ice data? Nagging injury. Psychological issues on or off-ice? Trade rumors? Unknown small health issues that we all deal with (sniffles, cold, allergy, digestive, etc.). None of these are “data points” were stored in and tracked by the AI data vat.
The humans associated with the team … coaches, medical staff, players, captain, or management need to be integrated into the "equation." More importantly, into a process.
To build "knowledge" through interaction and chatting between the human stakeholders, especially with the player.
Even the informal interaction between a coach and player in the hallway is data. How does that “data” become part of the AI generative model?
If someone knows, please let me know!
So Much Discussion, So Little Blog Space
Or so little ice time! I have more discussion on this subject. I have never been asked to share my thoughts about AI. So, it is here. I have two discussions to add to this popular (and complex) topic. More to come soon! Already have the points ready.
If you wish to print a copy of this article, AI-OpportunitiesAndChallenges_2024Jul-Part1.
If you wish to read Part II of this article, click here.