How 2018 Learnings Can Inform and Impact 2019
An overview of what we learned from 2018's AIIA Reports
Our Editor-in-Chief, Seth Adler presents our 2018 findings. He reviews what we were hearing at the beginning of the year, shares how things evolved throughout the following 12 months and forecasts expectations from our forthcoming AI2020 Update Report, due out Q1, 2019.Register
The AI & Automation Network’s Editor in Chief, Seth Adler presents how learnings from 2018 can impact and inform 2019.
- Motivations for intelligently automating the enterprise including:
- Staying ahead of the competition.
- Cost cutting
- Streamlining process
- Modernizing business
- Time saving.
- The biggest challenges to implementing intelligent business including:
- Change Management
- Upgrading legacy systems
- Competing priorities
- Grappling with the current results of the intelligent automation to date
- Recruiting the tomorrows talent including the concepts:
- Further enterprise profitability through intelligent automation cannot occur with the currently constituted carbon workforce
- Old skills, content skills, process skills, social skills, resource management skills for manufacturing physical abilities are not what skill sets corporate enterprise practitioners are looking for, for this next generation of talent.
- Cognitive abilities, system skills, complex problem solving- that's whose needed in tomorrow's workforce. Finding that talent cannot be done through traditional channels.
- Scalability as a systems integration issue
- Continuing to optimize structured data
- Taking the next step to leverage your unstructured data
- The definitions of transformation 1.0 through 5.0
- The challenges and opportunities that are in front of us:
- Redefining how your organization thinks
- The talent that's within your organization
- The data that you know and that you don't know from within your enterprise and otherwise
- Answering the question: are you really truly ready to redefine who you are as a global corporate enterprise?