Tasks Versus Skills Part 4: A Task Intelligence Control Room
A 7-part series on AI, the state of tasks versus skills, and some provocative what-if concepts for Learning and Development, HR and Talent teams.
This is Part 4 of a series called Tasks Versus Skills: Part 1: Introduction, Part 2: Playbook, Part 3: Let Learning Breathe, Part 4: Task Intelligence Control Room, Part 5: Tasks as EX Product, Part 6: IQA Prototype, Part 7: Talent Is Not a Commodity; Google Books “Tasks vs Skills” free ebook (Parts 1-6 only).
What-If: A Task Intelligence Control Room
A ‘real-time’ view of tasks obtained, accomplished, and their immediate business impact
(Photos by Adriano A. Biondo)
Many companies have a Learning Management System (LMS) or similar that helps them track the training progress of their employees and partners. This typically includes course enrollments and completions, engagement, compliance, quiz scores, certifications and more. Given the importance of skill-building and targeted training academies, a significant measurement focus also includes tracking skill gaps, proficiency gains, specific skill enrollments, and more.
A related goal for learning analytics and insights for many companies, especially large, multinational enterprises, is to move beyond basic consumption or Descriptive measures to higher-value analytics to help forecast future gaps and needs. This spectrum typically evolves from leveraging Hindsight data to higher, more realized data providing greater Foresight for the business.
(https://www.researchgate.net/publication/339672162_Analytics_Maturity_Models_An_Overview)
For our 2nd What-If scenario… What if AI and other mechanisms like graph-based GenAI, retrieval augmented generation, task-driven autonomous agents, etc., could track task-level activities and report their status in real-time or close to real-time? What would this tell us? What would we gain or benefit? What could we mitigate if there was a known gap or risk previously identified?
While working at Novartis’ main campus in Basel, Switzerland, I was so impressed by the caliber of people dedicated to developing and testing medicines and the operations and technologies required to achieve these goals. “To reach twice as many patients, twice as fast” was a commonly heard mantra. One building on the Basel campus was a regular magnet for me. This was the Novartis Study Operations Center. In particular, I was consistently magnetized by a high-tech fishbowl known as the SENSE Control Room.
The SENSE Control Room is an air traffic control or network operations center (NOC) providing an end-to-end view of the hundreds of clinical trials occurring in over a thousand locations around the globe. Moreover, the team–operating with full transparency for anyone at Novartis to observe online–is specifically conducting Risk-Based Monitoring for early detection of safety issues, site performance modeling, supply chain management, predictive modeling for FDA reporting, and more.
This practice of a centralized NOC for other companies has many of the same attributes, particularly from a performance, capacity utilization, asset lifecycle, and, of course, risk perspective. This has been a fairly common practice for healthcare management, manufacturing, disaster relief requirements, and more.
Some tenets I shared earlier in this series:
Task-level focus defines and measures work more accurately: Work is most precisely defined, conducted, and accomplished at the task level. This approach optimizes real problem-solving by focusing on tangible outcomes and enabling immediate impact and feedback.
A task-centric approach optimizes problem-solving and impact: Tasks focus on specific outcomes, enabling immediate impact, removing ambiguity, and capturing complexity in a way that is directly measurable.
Performance is best judged through task measurement: Tasks are binary in nature—either completed correctly or not—making performance easier to measure objectively compared to skills, which require assessing potential or capability.
The term Task Intelligence first came to us in 2017 via robotics research intending to help machines “perform work on behalf of a person or to provide convenient service to a person.” [28] More recently, ServiceNow has released its Task Intelligence offering that uses machine learning to allow agents, that is, real human agents working in call centers, for example, to automate and triage customer tickets and improve workflows. One goal is to optimize an agent’s work to lower a call center’s mean time to resolve or MTTR. A powerful benefit--similar to our SENSE and sibling control centers--is to predict the best customer solutions with faster MTTR and prescribe or deploy new support models:
With What-If #2, A Task Intelligence Control Room, I think we’re getting closer to making this happen for L&D. Can this be a reality, ideally a) in real-time, b) aligned with business and personal growth, and c) specific to quantifiable tasks?
Let's try this out.
Our use case example
We are a large software company rolling out a new product worldwide. All eyes are on our Australian region, which will sell this product first as it was tested locally. The regional sales team has been trained on the product, its new features, value proposition, and related objection-handling skills. The devil is in the details, and the details sales execs track is a fairly predictable sales methodology geared to monitor revenue attainment, with cojoined systems like Salesforce monitoring sales operations and providing reports.
For our Australian reps and all global Sales, our sales methodology looks like this, with this use case emphasizing the third phase in the process, or the DEVELOP phase:
The DEVELOP phase contains several categories: a) the specific Sales Tasks that a rep or teammate should complete; b) a set of “Verifiable Outcome” tasks that, if achieved with the prospect, provides validation that the deal is proceeding; c) related, the probability of the deal closing in the form of a percentage, and; d) the available tools and resources supporting reps and teams.
In general, this is a process- and task-heavy set of criteria to show probability and confidence, as well as potential coaching and training opportunities and a means to identify risks.
However, our use case includes a huge challenge with this new product deployment. That is, as we assume we have a future state Task Intelligence platform tracking the dozens of leads and sales activities occurring throughout the Australian market, we see gaps in skills not being applied, tasks not being executed, and an unfortunate High Threat potential of not being able to accomplish this quarter’s revenue goal.
What this Task Intelligence Heatmap isolates regarding tasks and skills Not accomplished during this DEVELOP phase:
A Task Intelligence Close
With this sales-specific use case, our Task Intelligence approach is in real-time, aligning vital skills and tasks to sales productivity, and leaning toward Predictive and Prescriptive responses. This helps management, support teams, and sales reps be more specific about the necessary skills that must still be applied on the job (versus just completing the course) and supporting tasks to accomplish.
More specifically, there is a variety of Task Intelligence benefits to consider beyond selling one product in one region during one phase in the methodology. Broader benefits may affect onboarding and time-to-productivity, manufacturing, customer service, clinical trial training, and more. (See Appendix H for current AI approaches already enable Task Intelligence)
In closing for What-If #2, here are several examples highlighting additional uses and benefits. Also included are a few technologies currently being deployed in this What-If reality.
In preparation for What-If #3, we’ll investigate how a task-centric approach, combined with AI, can accelerate the adoption and effectiveness of modern Employee Experience or EX programs.
For now…
Broader Task Intelligence uses and benefits
Links: Part 1: Introduction, Part 2: Playbook, Part 3: Let Learning Breathe, Part 4: Task Intelligence Control Room, Part 5: Tasks as EX Product, Part 6: IQA Prototype, Part 7: Talent Is Not a Commodity; Google Books “Tasks vs Skills” free ebook (Parts 1-6 only).
Special thanks to colleagues who guided me as contributors and reviewers, and to so many who have inspired my thinking and curiosity on this subject.
Contributors: Cathy Moore, Clark Quinn, Felipe Hessel, Gianni Giacomelli, Giri Coneti, Jon Fletcher, Julie Dirksen, Megan Torrance, Nick Shackleton-Jones, Nina Bressler, Will Thalheimer, Ross Dawson
Inspirations: Allie K. Miller, Amanda Nolen, Andrew Kable (MAHRI), Bhaskar Deka, Brandon Carson, Brian Murphy, Chara Balasubrmanian, Dani Johnson, Darren Galvin, Dave Buglass Chartered FCIPD, MBA, Dave Ulrich, David Green 🇺🇦, David Wilson, Deborah Quazzo, Dennis Yang, Detlef Hold, Donald Clark, Donald H Taylor, Dr Markus Bernhardt, Dushyant Pandey, Egle Vinauskaite, Emma Mercer (Assoc CIPD, MLPI), Ethan Mollick, Gordon Trujillo, Guy Dickinson, Harish Pillay, Hitesh Dholakia, Isabelle Bichler-Eliasaf, Isabelle Hau, Joel Hellermark, Joel Podolny, Johann Laville, Jon Lexa, Josh Bersin, Josh Cavalier, Joshua Wöhle, Julian Stodd, Karen Clay, Karie Willyerd, Kate Graham, Kathi Enderes, Marga Biller, Marc Zao-Sanders, Meredith Wellard, Mikaël Wornoo🐺, Nico Orie, Noah G. Rabinowitz, Nuno Gonçalves, Oliver Hauser, Orsolya Hein, Patrick Hull, Peter Meerman, Peter Sheppard, Dr Philippa Hardman, Raffaella Sadun, Ravin Jesuthasan, CFA, FRSA, René Gessenich, Ross Dawson, Ross Garner, Sandra Loughlin, PhD, Simon Brown, Stacia Sherman Garr, Stefaan van Hooydonk, Stella Collins, Trish Uhl, PMP 👋🏻, Tony Seale, Zara Zaman
Acknowledging leveraging Perplexity, Gemini, ChatGPT, and Claude for research, formatting, testing links, challenging assumptions and aiding the creative process.
LICENSING
Unless otherwise noted, the contents of this series are licensed under the Creative Commons Attribution 4.0 International license.
Should you choose to exercise any of the 5R permissions granted you under the Creative Commons Attribution 4.0 license, please attribute me in accordance with CC's best practices for attribution.
If you would like to attribute me differently or use my work under different terms, contact me at https://www.linkedin.com/in/marcsramos/.
APPENDICES
Appendix H
Table 8: Current AI approaches already enable Task Intelligence
NOTES
A Task Intelligence Control Room
[28] Task Intelligence first came to us in 2017: Deok-Hwa Kim, Gyeong-Moon Park, Yong-Ho Yoo, Si-Jung Ryu, In-Bae Jeong, Jong-Hwan Kim, (2017), “Realization of task intelligence for service robots in an unstructured environment”, Annual Reviews in Control, Volume 44, Pages 9-18,ISSN 1367-5788, https://doi.org/10.1016/j.arcontrol.2017.09.013
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