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We conducted a survey about a week ago with a hundred CIOs and CTOs who said that AI is going to be crucial during the next two years. But, they also said in the same survey that they don’t think their teams will be able to drive that change. What’s your perspective on how IT Executives can better prepare for it?

Scarcity for talent is something which will continue. AI is very crucial because as companies grow or transform or try to acquire new customers and compete, they need to be able to do this at scale. There’s going to be a lot of work, whether it is based on the customer experience or internal operations that need to be driven in a much more orbited fashion. It can’t be solved by hiring lots and lots of people. The compute power is available, the endless possibilities of the outcomes is available, the data sets are available for you to think about how you take the company to the next level. In terms of building teams, as I said earlier, it’s going to be a challenge. Hiring data scientists or machine learning engineers is hard enough. So, my advice is to look at companies who have done this, won customers, have case studies, have a vision about building out these capabilities as entire platforms and are focusing their resources and intellect in doing this.

Anonymous Author
Scarcity for talent is something which will continue. AI is very crucial because as companies grow or transform or try to acquire new customers and compete, they need to be able to do this at scale. There’s going to be a lot of work, whether it is based on the customer experience or internal operations that need to be driven in a much more orbited fashion. It can’t be solved by hiring lots and lots of people. The compute power is available, the endless possibilities of the outcomes is available, the data sets are available for you to think about how you take the company to the next level. In terms of building teams, as I said earlier, it’s going to be a challenge. Hiring data scientists or machine learning engineers is hard enough. So, my advice is to look at companies who have done this, won customers, have case studies, have a vision about building out these capabilities as entire platforms and are focusing their resources and intellect in doing this.
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Anonymous Author
Two things: 1) Realize that AI and ML are a symptom of broader trend, *becoming data driven*. Models are a part of that, but not the only part. Successful teams will invest in training both on the technical side (AI), and on the culture side. The quest to make data-driven decisions starts with the culture. Does your company make decisions with the aid of models, visualizations, notebooks, and hard data? Or do decisions fall from the sky on stone tablets? Marissa Mayer famously said that she discourages people from saying "I like X" in meetings--and rather encourages them to bring data that support or refute specific points of view. 2) Invest in data infrastructure that allows *the entire team* to participate in the process of interpreting and making decisions based on shared data. Too often data are siloed across systems and there is no single location where analysts, developers, and business leaders can get on the same page, reason about the world, and document their decisions.
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Anonymous Author
There is a need for cross skilling existing teams and executives with the new age technologies and possibilities Offered by the same. There are programs by the universities as well as online courses that could be undertaken by the executive teams. What works best is KPI driven approach for cross training of executives and team. Often bringing in an external expertise including consulting teams can also help.
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Anonymous Author
Finding the talent to do the work is going to be a big issues. Data Scientist and big data resources are getting top dollar these days. The other area is companies have to be able to understand what is a good project for AI and what is just a straight Data Analytics problem. I have seen companies say I need AI when the reality is the do not. Lastly, do they have enough data to do the training of the models. 
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Anonymous Author
Hiring qualified and affordable resources is a challenge, especially here in the bay area.  Some consulting companies marketing AI are just using predictive analytics/algorithm that already existed 15 years ago but that's not true AI to me.   Finding implementations of successful use cases of true AI is still a challenge although I expect this will improve rapidly with increased adoption.
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Anonymous Author
 We need to start training some people internally, and leveraging tools that we may be using elsewhere (e.g. Microsoft Q&A maker) to begin some initial steps with existing resources. 
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Anonymous Author
 AI is the end result of well implemented BI and ML programs. however, the technology change from BI to ML to AI are step changes from one another and require different levels of investment in talent, skill set, technology, infrastructure and operations. AI and ML will also drive changes to core business operations at the company based on the application of AI. Legacy IT teams will most probably be hesitant to implement AI as most leadership teams treat AI as a buzz word and view it as the \"next shiny toy\" to show off rather than view it as a serious restructuring tool. Implementation of AI requires transformation thinking and necessitate some business model changes. Once leaders realize how difficult, expensive and time consuming it is to put an enterprise grade AI program in place, some structure tends to fall in place and use cases will be rolled out slowly.  IT executives can prepare for the change by taking a long, hard look at their company's business model and identifying key focus areas where AI may delivery reasonable results with existing resources. They may then expand their data programs to augment implementation of a sustainable AI program.
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Anonymous Author
Find a real need rather than inflating the hype of adding AI unnecessarily into the environment, as it just simply creates a bubble of unstable frameworks that will not lead to a proper development of its true benefits.
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Anonymous Author
AI and RPA AUTO can help the business drive positive business outcomes with various business processes and other tasks, both strategic and tactical, but first the business have a clearly mapped out problem to solve for. Otherwise it may result in developing solutions for problems/situations the business may not have just yet. 
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Anonymous Author
CIO’s need to begin to prototype and pilot AI and ML projects. When possible, they need to look at partnerships. When it comes to internal skills and team building, they need to anchor around a dedicated leader who has experience.
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Anonymous Author
At Royal we started by fomenting the AI-Data Analytics knowledge across the entire enterprise. Everyone in the company should be involved and understand the capabilities at a macro level. That way you will have more people with ideas on how to translate data to create business value. We also created a channel where people can come up with such ideas. We are building our Data Analytics team and changing our business models to be more data driven. As for talent, it is a real challenge indeed but instead of looking only outside our organization we are looking inside and finding potential data scientists, data engineers and data translators that just need training and exposure. It is imperative that the CEO embarks in the journey and supports the culture. Also, count with experience vendors and have a great leader in that arena.
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