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583 AI Enabled Leadership In Japan
- 2024/10/30
- 再生時間： 12 分
- ポッドキャスト
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サマリー
あらすじ・解説
We know that AI has gone from the domain of geeky people in white lab coats to the mainstream of business in a nanosecond. Such speed is difficult to keep up with and the roll out of new options continues unabated. As the leader how do we surf this tech wave and prepare our people for this AI enabled future/ Making data backed decisions is always preferred in leadership and AI has the power to crunch large amounts of data and provide answers very quickly. As long as it isn’t lying to us with so-called hallucinations about the results, then it is a big help. Direction on using AI in our businesses is not going to bubble up from down below and we leaders need to get to work to harness this beast. 1. Audit We can start with an audit of where we think AI can bring savings in terms of time, money, effort and quality. Doing this process with the team is required because we want them to own the process and the results. There may be fears that certain jobs will disappear because of AI and we need to face that reality head on. It doesn't necessarily mean the person leaves the firm because finding staff in Japan is at a premium, but it may mean their job content changes. There will be flow on effects about required retraining and thought has to be put into the feasibility of doing that with the resources we have available. 2. Strategy & Innovation Having completed the audit we now have some insight into the opportunities and difficulties working with AI will bring, rather than relying on our imaginings of the future. Where is the intersection of AI capabilities and the goals we have set for the firm? The goals are usually revenue related and these won’t change much, but the way we deliver the results could. People will have to work with AI, there is no escaping that fact, so what is the strategy to determine how this happens? We don’t want to leave everyone to their own devices to wander off and somehow work it out by themselves. Which AI platforms do we need, how much should we budget for them and who will take care of what, are leading questions we need to find answers for? For some staff, AI may never be an immediate part of their world at this point, although that may also change. We need to do an analysis of who needs it the most and who needs it first. Which jobs will benefit the most from applying AI’s capabilities to the work? That simple question may be difficult to answer because we have to explore the possibilities AI introduces. We may need to appoint champions to drive the usage of AI inside the company, so that we can break the task up into smaller pieces. The scale of AI can be overwhelming. How can we find ways of having AI help us with becoming more innovative or at least set out some frameworks for us to explore by ourselves? 3. Staff Training A lot of the training for the use of AI will be internal with people dedicating time to play with it. If we think of AI as external to our work, then we won’t nominate the time for people to experiment and learn on the job. The explosion of AI means that no one can keep up with the latest developments as functionalities are superseded by new alternatives. There is also the issue of the broad range of platform variations and upgrades which are emerging every month. How can we navigate this breadth and speed? We can’t but we shouldn’t be so overwhelmed we don’t start. We should select a few platforms which seem to have the greatest application for what we do and start there, realising we may need to jump on to the back of faster racehorse, once the gun has sounded and we are off barrelling down the track. We should block out a certain number of hours per week for our team members to play with AI and see where they can apply its power to the business. If the leader nominates 4 hours a week, for example, then that gives people permission and time from within their work day to experiment. 4. Reporting Naturally, we want to have reports and updates on the progress and learnings these hours experimenting are yielding. This requires some time scheduling changes for everyone and for the boss too. These ideas are all difficult in an already busy life, but we have to grant AI the priority or it will all just be hot air from the boss and there will be no follow through. We are all touching different parts of the machine, so getting together to share makes a lot of sense and the boss can nominate a couple hours in a month to make sure that happens. 5. Data We will unearth and collect a host of data, but what do we do with it? This seeking data for data’s sake is tremendous fun for some, but it all has to connect back to driving the firm forward. There will be financial data we can use to try and pick up trends or patterns which will aid us in trying to set budgets and allocations for spending. ...