Digital Transformation drives today’s Workforce Automation and Business Intelligence (BI) initiatives where nimbler agile teams undertake tasks and jobs of unprecedented scale, scope and speed.
Digitalization also involves “Self-Service” business models which are based on the direct involvement of end-users and a frictionless customer journey, all relaying on seemingly instantaneous and automated mass-personalization.
Given that digitalization has become pervasive and that ‘making tech human’ has become a critical success factor, the new field of Genuine Intelligence (GI,) addresses holistic Human-Machine-Systems (HMS) leveraging collaborative environments comprised of networked insights, tools and processes. GI’s signature deliverable is Digital Decision Support Systems involving Integrated Workspaces.
This construct adheres to LeanOps and Quality by Design (QbD) principles for emerging technologies and, therefore, optimizes for (a) quality outcomes as gauged by consumer and operational experiences performed under (b) highly efficient operations and (c) advantageous resource utilization and effort levels.
Both value generation and productivity gains are constantly audited and iteratively improved throughout event lifecycles and over the lifespan of the system.
Jose de Francisco is a Senior Design Director at Nokia Software Group. His 20+ year experience encompasses multi-disciplinary leadership responsibilities in strategy, product & portfolio management, research & development, marketing, partnerships and project & program management. Jose is a Distinguished Member of Technical Staff (DMTS) and has worked with Bell Labs on next generation platforms. He is a Member of the Advisory Board at MIT’s Institute for Data Systems and Society (IDSS) and is the recipient of an MBA in International Marketing and Finance (MBA/IMF) from Chicago’s DePaul University as a Honeywell Europe Be Brilliant Scholar. Jose also holds a postgraduate degree in Human Factors Engineering from BarcelonaTech (UPC) and can be followed on innovarista.org.
Following up on my last post about IEEE ERT 2018, here are a couple of charts for my “discussion brief,” which include a Human-Machine-System Capability Mapping chart (above) and concept illustrations of the Experiential Decision Support System (below.) The charts’ text conveys context setting remarks, which I am also providing here.
The goal of furthering machine intelligence is to make humans more able and smarter: the opposite engineering approach typically becomes a source of self-defeating technical myopia waiting to happen and missed opportunities. This simple mapping exercise can be customized to assess and roadmap capability levels.
The more sophisticated automation becomes, the more obvious the criticality of the human factor in both consumer and enterprise environments… rather than less. And, in any case, customer acceptance and adoption criteria remain Quality’s litmus test for emerging technologies.
Digitalization is fostering (a) XaaS, (b) Self-Service, (c) the Shared Economy and the (d) Maker Movement. All elevate human involvement and drive the push for opening and democratizing technologies. These make (e) citizen science and citizen developers shape the next generation prosumers at mass market scale.
Digital Transformation initiatives embracing the above allow (f) nimbler enterprise teams to operate at far greater scale, scope and speed, and shift focus from routine operations to dynamic value creation coupled with extreme efficiencies.
This entails (g) interdisciplinary workstyles and collaborative organizational behaviors that include (h) customer co-creation models. In this new context, humans remain (i) the ultimate critical element in system reliability and safety. Left shifting Quality by Design (QbD) prioritizes Human-Centered-Design tools and processes to deliver high performance workforce automation systems.
Cost-effective Lean Ops systems intertwine analytics, automation, programmability and flexible systems integration. All optimized for dynamic behaviors given Soft System’s perpetual motion. This means designing “for-ever” rapid and seamless reconfigurability instead of just engineering “day 1” implementations.
Operational Excellence dictates system-wide as well as subsystem level visualization, and a combination of centralized & distributed closed loop controls under user friendly operational modes. Cognitive models involve Situational Awareness (SA,) Sense Making (SM,) Root Cause Analysis (RCA,) Scenario Planning (SP,) and ROA (Real Options Analysis.)
The Experiential element is not just about programming known rules and policies but, most importantly, it grows by assimilating iterative learning in the context of cyclical automation: routine decisions and manual operations can be streamlined and collapsed, then switching to “exception” based management for that particular event.
Productivity calls for streamlining operations so that (a) waste can be eliminated & prevented, and (b) value based tasks can be performed effortlessly, in less steps, at speed & without error. High performance behaviors and sustainable competitiveness also call for the ability to (c) experiment and create new capabilities, as well as leveraging (d) process mining for customer journeys & value stream mapping (CJM & VSM) to continuously optimize them and guarantee service levels.
Service Operations Centers (SOC) should be equipped with Experiential Decision Support Systems (DSS) featuring (d) collaborative filtering, (e) actionable data stories conveying hindsight, insight & foresight and (f) adaptive cybernetics. Advanced visualization for both (f) intuitive & highly abstracted infographics and (g) scientific views is of the essence.
Quality is best addressed as a human experience, which determines (d) meaning and, therefore, the degree to which a system is lean vs. over-engineered or subpar (both being defective and carrying obvious and hidden costs.) A new take on QbD for Soft Systems, which are inherently fluid by definition, emphasizes acceptance testing probing for: usefulness & utility, usability & affectivity, consumability & serviceability and safety thru use cases and lifecycle events.
ETR turned out to be a very productive undertaking and I would like to thank IEEE’s Spilios Markis, Chi-Ming Chen and Chris Mayer for all the help provided prior and during workshop.
My contribution focusing on addressing the unprecedented flexibility of advanced software defined systems and artificial intelligence. That intersection defines game changing technologies leading to zero-touch automation and, therefore, fostering self-service opportunities at both operational and service consumption levels.
“Zero touch” implies extreme automation to its fullest while self-service reveals that this new order elevates the criticality of HMS (Human Machine Systems.) More touch points surface compared to what legacy technologies allowed given their constraint and restricted nature. That prompts a new take on HCI (Human Computer Interaction) and QbD (Quality by Design) to best deliver service quality throughout: concept exploration and service definition, fulfilment and adaptation, assurance and security… across multi-domain, highly decomposed, re-configurable and exceptionally dynamic end-to-end systems involving integration and service delivery in continuous motion.
These are thought out to (a) dramatically optimize support personnel ratios and (b) shift staff’s attention and efforts to value based activities and innovation. These are small agile teams and new talent tasked with jobs involving (c) far greater scale with (d) a wider interdisciplinary scope, and all to be performed at (e) digital speed. In this next-level productivity and more demanding and challenging context, success relies on new tools embracing Design Thinking’s HCD (Human-Centered-Design.)
That is applied to capability models and subsequent modes of operation for (f) HITL (Human “IN” The Loop) Computing largely devoted to deep domain expertise supported by Science Visualization, as well as (g) HOTL (Human “ON” the Loop) for system-wide supervisory responsibilities and ease of service creation and onboarding. HOTL draws from highly abstracted Visualization techniques and Low Code Development revealing the behavior of end-to-end systems and subsystems and adequate flow control.
These are coupled with effective Cybernetics gearing up for context aware 360-closed-loop-control, zooming in and out between distributed and central levels. Last but not least, effective and efficient tools that are characterized by ease of use and consumability do attract many more new users from many more different domains to interact with these systems in a self-service fashion and create new business opportunities as a result.