AIIM - The Global Community of Information Professionals

Clearing the Complication - Making Machine Learning and Artificial Intelligence Accessible and Useful

May 17, 2018 10:32:00 AM by Alan Pelz-Sharpe

These days it feels almost impossible to have a conversation with anyone involved in the world of Information Management without bringing up machine learning and artificial intelligence. Everyone is talking about it, everyone has an opinion, everyone claims to be doing it — or at least thinking about doing it. AI is part of the popular zeitgeist, entering our lives in the form of autonomous vehicles on our roads, Alexa in our homes, and a burgeoning AI arms race between rival world powers. Yet despite all the talk, no one really seems to agree on what its real impact will be. In fact, everyone seems to have a different definition for what ‘it’ even is. There are those telling us it’s nothing new and its potential is over-hyped, while others tell us that AI and Machine Learning will make us all redundant.

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Topics: training, machine learning, Artificial Intelligence

Surviving Disruption -- Strategies for the Era of AI and Machine Learning

Apr 18, 2018 9:00:00 AM by John F. Mancini

Truly disruptive moments occur only once in a generation. They occur when the cumulative inertia of technology innovation combines to disrupt business models. And the heart of disruption is ultimately when radical technology changes are standardized and then incorporated into new business models.

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Topics: machine learning, content mangement, intelligent information management, Artificial Intelligence

Going Down the Rabbit Hole

Feb 15, 2018 10:44:00 AM by Theresa M. Resek, CIP

When I was a young child, in an era when school papers had to be researched in libraries with card catalogs and shelves upon shelves of books, my father had the bright idea to purchase a complete set of Encyclopedia Britannica. Now, we lived on a tight budget and my mother wasn’t too thrilled with this splurge when a perfectly good library was so close to home. Dad wouldn’t hear of it! “Our girls need the best education we can offer,” so of course, mom relented.

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Topics: ecm, capture, digital transformation,, machine learning, intelligent information management

Who's Watching Your Operations?

Feb 1, 2018 10:28:00 AM by Bob Larrivee




You manage a large facility with multiple buildings across the campus. Each building while somewhat unique, is equipped with plumbing, electrical, security, HVAC, and other elements that must be monitored, maintained and repaired at some point. Ideally, you would have an army of staff available to monitor and check everything constantly to provide feedback and predict what will happen before catastrophe strikes, but you don’t. This is where machine learning and industrial operations can play a significant role.

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Topics: content analytics, machine learning, data analytics

Guest Post -- Automation Is Changing the Way We Work

Jan 19, 2018 11:07:00 AM by Tanya Brooks

Artificial intelligence is changing the way we do everything. As we look ahead, self-driving cars, hyper-intelligent predictive software, and self-improving robots seem possible.

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Topics: automation, machine learning, Artificial Intelligence

4 Information Capture Challenges to Prepare for the Era of AI and Move to the Next Level

Jul 27, 2017 12:13:55 PM by John Mancini

 

In a recent AIIM survey we investigated the question of what information capture “leadership” looks like in user organizations. What does information capture look like in leading organizations that want to position this competency not only as a source of immediate competitive advantage, but also as a long-term competency critical to the coming era of machine learning?

What are the problems that organizations are experiencing with their capture implementations as they consider this evolution? Here 4 key problem areas that surfaced in our survey; we’ll also be discussing this survey and its implications in an AIIM webinar on September 19.

4 Key Information Capture Challenges in Moving to the Next Level

  1. Multiple document types -- Most organizations are struggling with capture complexity that is driven by the sheer volume of document types that must be managed.
  2. Uncertainty about data accuracy -- Data is at the heart of the Digital Revolution. And data quality is at the heart of the challenge facing organizations as they attempt to make their data fit for purpose and fit for use.
  3. Poor usability -- 62 percent of respondents in our survey rate their capture software “very difficult” or “somewhat difficult” to configure.
  4. True information capture is more complicated than simple imaging -- Capture is often assumed to be synonymous with scanning. The reality is that most organizations need to do far more than just process images.

Interested in learning more? Download my new free Tip Sheet!

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Topics: information capture, machine learning, intelligent capture, Artificial Intelligence, parascript

The convergence of analytics, cognitive computing and machine learning

Jan 18, 2017 4:38:24 PM by John Mancini

AIIM’s resident Podcaster Extraordinaire, Kevin Craine, recently sat down with Andrea Chiappe, Director of Innovation and Strategy at Systemware to discuss opportunities in the convergence of analytics, cognitive computing and machine learning. The following is a short synopsis of the interview.  You can find the full podcast interview HERE.

Part two of the interview was a discussion with Claudia Kieran, Corporate Senior Accountant at Wildman Business Group, about their efforts at Wildman to become more paper free, and how they did it.  I’ll be following up this post with a second post to tell Claudia’s story.

Kevin:  Why do you feel that the scope and idea of information governance needs to more than just records management?

Andrea:  When I look at the words “records management,” I have to admit that even I think it sounds a little bit boring. It implies a singular objective and a singular solution. I look at information governance as not so singular. Traditional records management is linear in nature – we classify records, we maintain and retain records, and after their retention period is past, we get rid of records.  Information governance is more of an ecosystem. Although records management absolutely is still a legitimate endeavor, I think we need to think in terms of an overall umbrella or ecosystem of governance. 

Kevin:  When you say that content management is critical enabling technology for digital transformation but not in its traditional form, what do you mean by that?

Andrea:  First generation content management systems look at information control as king – “I better hold my information tightly and never let it go.” I see the future as our industry as an absolutely open information ecosystem where yes, compliance and security are key, but the emphasis in on allowing for curated information to get out and be put to use.  I don't think that the traditional way we define content management will be the dominant definition in 2020.

Kevin:  We hear a lot about analytics, cognitive computing, machine learning, and how these technologies can be leveraged to improve things like customer experiences.  What are the things that we should consider now as we map our strategies with respect to analytics and machine learning?

Andrea:  First and foremost, this is not an overnight deal rather it is a journey. Your road map and strategies must align with the objectives and use cases we identify as benefiting from these technologies and know that they will evolve. Benefiting from cognitive technologies require that you measure outcomes and continue to tune and train your systems and users. In fact, ensure that you plan to govern the tuning mechanisms and training sets closely whether human built or system generated. The competitive advantages that businesses stand to gain are undeniable and as these technologies continue to evolve they will take on a life of their own.

Kevin:  You say that we can no longer just put on band-aids onto our infrastructures as we think about moving forward. How can we adjust our focus to include a more transformed governance approach as part of our strategy and not as an afterthought?

Andrea:  It is important to take a step back and consider if the technologies that we leverage in our organizations are providing a foundation and environment to move forward. The current pace of innovation demands that we identify those things that are helping versus hindering our road map initiatives in order to remain competitive. Keep in mind that testing our business plans and technologies against the vision of where we want to be seven to ten years from now should not be a happening it should be an ongoing endeavor.

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Download this free white paper -- Process Improvement and Automation 2016
Research shows many business leaders understand now more than ever before, that information and process form an integrated component of business operations as a whole. This report from AIIM Market Intelligence and underwritten in part by Systemware, takes a look at the current state of BPM.

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You might also be interested in this.  27% of organizations see content analytics (CA) as essential now, with 59% citing they see it as essential within the next 5 years. Beyond “big data” style business intelligence, analytics is driving auto-classification, content remediation, security correction, adaptive case management, and process monitoring and modeling.  Get a copy of the executive summary of AIIM's new market research study -- Using Analytics: Automating Processes and Extracting Knowledge -- to find out more.

DOWNLOAD YOUR EXECUTIVE SUMMARY

 

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Topics: ecm, analytics, machine learning, cognitive computing

Information overload: It affects MACHINES as well as people

Sep 19, 2016 3:01:31 PM by Paul Cleverley

This is a guest post by Paul Cleverley, a geoscientist and practitioner by background and is now an information scientist and researcher in the Department of Information Management with Robert Gordon University in Aberdeen.

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Topics: enterprise content management, business process, enterprise search, machine learning

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