Learning and Leading in the Era of Artificial Intelligence and Machine Learning, Part 2

Julianna DeLua
Oct 1, 2018 · 4 min read
Wikimedia Commons

In the previous post, I discussed the golden age of knowledge and learning that can make significant impact. In part 2, I’ll address the importance of negotiating conflicts, priorities and gaps, and how we can all rethink leadership in our lives with networks of human connections and machine-generated insight.

Negotiating conflicts, ambiguities and gaps in insight

Collaboration on the surface is now easier than ever before. But having quality engagement and meaningful outcome with your team members or partners can be much harder because there is so much noise. You have to constantly assess:

  • Is this something that I should research? Can I trust this report?
  • Do I want to ask someone? What happens if they send me the wrong information or biased opinions?
  • What if something unanticipated happens?
  • Are these correlations or causations?

In many cases, knowledge is locked in specific departments or small groups. Such silos can create conflicts, confusions or even serious delays in resolving a simple matter. IBM Watson Knowledge Catalog powers intelligent, self-service discovery of data, models and more, activating them for artificial intelligence. You can access, curate, categorize and share data, knowledge assets and their relationships, wherever they reside, and see frequencies in a graphically understandable format.

Figure 1: Watson Knowledge Catalog and Data Refinery

With more transparency and visibility of data, relationships and people, you have a greater chance to tackle issues and discover new opportunities. You can:

  • Find ways to increase productivity across the team
  • Look for processes that can be reengineered or streamlined
  • Identify and fill gaps in expertise, data and resources
  • Continuously negotiate better ways of running the business

Putting leadership at the intersection of AI and business

We covered some significant ground related to acquiring more skills and filling knowledge gaps. Will it be daunting? Yes, you bet. Do you have to do all by yourself? A resounding no. This is where I see another learning opportunity for all of us: Leadership.

For the sake of discussion, let’s define leadership as an influence that can help orient and empower others to execute a shared vision.

Now, let’s imagine a typical ML production environment where people are working together using both human and machine generated insight in the Watson Studio.

  • Business leaders set the vision and objectives, with emphasis on customer experiences, operational efficiency and new business creation.
  • Data science and analytics leaders help set the directions of AI / ML projects to meet the business goals.
  • Data scientists define and run experiments, build and train models and collaborate with developers and operations to put them into production.
  • Domain experts and analysts translate business objectives into rules and metrics and build catalogs.
  • Data engineers build prepare, aggregate and manage data sets.
  • App developers build apps and work with AI/ML ops to operationalize the pipeline using predictions provided by Watson Machine Learning via a REST API.
Figure 2: Watson Machine Learning with Watson Knowledge Catalog

You’re constantly collaborating with people with a diverse set of skills and talent internally and externally. So what are the key lessons that can help you win at the team sport of AI?

  • Build your expertise and constantly improve it so that others can trust you to do your job.
  • Stay open and flexible to learn new areas; learn from sample projects and assets.
  • Be generous — share assets and teach your colleagues and friends; it’s easy to do so in a single environment.
  • Put yourself in the shoes of your customers and especially how your customers experience your business end-to-end.

Next Steps

To learn more, join us for our October 4 Watson Studio Web seminar where IBM will provide a tour of Watson Studio and introduce Watson Studio Desktop. We’ll provide a product demonstration centering on personalized medicine where we showcase visual modeling, visual recognition, deep learning and model deployment capabilities end-to-end.

We also invite you to join us for IBM Analytics University in Miami on October 2–5, 2018. oAnd read The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning Solutions which names IBM a proven leader, or start a Watson Studio trial now.

To learn more:

Finally, stay connected on Twitter: IBM Data Science

Inside Machine learning

Deep-dive articles about machine learning and data. Curated by IBM Data and AI.

Julianna DeLua

Written by

Expert on the use of artificial intelligence and analytics to drive growth and reinvent business. Original, independent thinker with relentless execution

Inside Machine learning

Deep-dive articles about machine learning and data. Curated by IBM Data and AI.

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