IEEE CQR 2018 – AI CoC Session


IEEE CQR-ETR 2018: “Discuss and identify the RAS (Reliability, Availability and Serviceability) challenges, requirements and methodologies in the emerging technology areas like the Cloud Computing, Wireless/Mobility (with focus on 5G technologies), NFV (Network Functions Virtualization), SDN (Software Defined Networking), or similar large-scale distributed and virtualization systems.”

“Discuss the RAS requirements and technologies for mission-critical industries (e.g., airborne systems, railway communication systems, the banking and financial communication systems, etc.), with the goal to promote the interindustry
sharing of related ideas and experiences. Identify potential directions for resolving identified issues and propose possible solution.”


Session Title: A Programmatic Approach for an Artificial Intelligence Code of Conduct.

Today’s DX, Digital Transformation, programs are all the rage, but it takes a fair amount of double clicking and inquisitive questioning to separate facts from vaporware. DX typically involves a wide variety of game changing initiatives intersecting analytics, automation, programmability, software-defined systems, end-to-end integration, service-level composition and controls… all coming together to optimize for Quality as a differentiated and value-based Human Experience. Therefore, Customer Delight metrics (rather than outmoded customer satisfaction ones) are set to redefine the “Q” in CQR, Communications Quality & Reliability in 5G.

While the Telecoms industry rallies toward a zero touch paradigms, which some happen to position as a Human-“OFF”-the-Loop panacea, this session will expose the need for considering, and possibly pivoting, to the kind of Operational Excellence that can only be delivered by fast adapting HMS, Human-Machine-Systems instead. Note the rise of Dataviz (Data Visualization,) ML’s (Machine Learning’s) Collaborative Filtering, AI’s (Artificial Intelligence’s) RecSys (Recommender Systems) and a fresh take on Cybernetics… which are driving innovation in HILT and HOTL (Human-“IN”-The-Loop and Human-“ON”-the-Loop, Computing, as well as delivering effective mass-personalization with Affective Computing powered by Human Dynamics’ analytics.

Once upon a time… Telecoms’ pioneered HFE, Human Factors Engineering: a holistic systems engineering discipline addressing people (culture, workstyle, skills,) processes (procedures, methods, practices,) and technologies (crafts, tools, systems) so that we can best humanize technology and make a compelling difference across the value chain and at all levels. Unfortunately, HCD, Human-Centered-Design, fell out of favor over time while, paradoxically, took off in emerging technology sectors under other disciplines. Lost in oblivion, louder but siloed voices inflicted the sort of self-defeating Technical Myopia that props up complexity and negates differentiated Quality Experiences. Today’s telecoms industry is impacted by disintermediation and commoditization as a result and, equally telling, keeping extremely busy with importing practices from sectors frowned at in a no so distant past.

We are now embarked on a new journey. The sought after outcome of any Digital Service Provider, DSP, is to be instrumental to Digital Citizens’ Quality Experiences with new experimentation, monetization and growth models. This takes agility and dynamic system-wide (horizontal and vertical) behaviors, which prompts effortless operability at unprecedented speed, scale and scope. Our work permeates design, development, delivery and serviceability, as intertwined and continuous lifecycles instead of lock-step waterfalls.

In this context, AI, Artificial Intelligence, enables us, humans, to envision and implement capabilities beyond the reach of legacy systems’ last gasps. By the same token, practices that might have appeared to serve us well in the past, are now becoming dysfunctional and latency-prone barriers. A successful path forward takes augmented Human-Machine Intelligence. Human-Centered-Design’s outcome oriented model calls for a programmatic approach of an AI’s Code of Conduct, so that we can best interface and collaborate… instead of making good on Elon Musk’s well know fears around AI.


Software’s Defining Age

1k Fulton Market

I am on my way to Mobile World Congress and last night I had the opportunity to speak at DevMynd’s “Agile Software in a Hardware World.” That panel discussion featured BMW Technology Corporation (BMW, Mini, Rolls-Royce,) Monsanto’s “The Climate Corporation,” and Nokia Software Group, which I was proud to represent. The venue, 1KFulton, is a century-old and former cold storage building in the Fulton Market neighborhood, home to Google’s Chicago campus.


Reflecting on that panel discussion, small group conversations and one-on-one chats before and after the event, I think that it is fair to state the following:

(A) software is undergoing a defining moment while re-shaping industries. “Software defined instruments and systems” have superseded capaibilities of hardware-centric deployments.

In other words, economic value and profitability are migrating from conventional products to software dominated environments that control tools, systems, and processes.

In this new context, (B) collaborative undertakings (co-creation, open source,) platforms, modularization and mashups are paving the way for rapid experimentation and for a wide-range of services to surface.

Back to economics… a venture capital firm operating in the Silicon Valley shared with me that when comparing current investments with equivalent old-school ones, they experienced x3 times time-to-market speed at 1/3 of the investment, which allows them to better diversify risk and fund more start-ups in the process.

Moreover, we are now operating at (C) unprecedented speed, scale and scope. For that reason alone, software should improve our ability to “pivot” and dynamically adapt to changing circumstances.

Most plans don’t survive first contact and many start-ups and emerging technologies don’t survive the so-called “crossing-the-chasm” or “Valley of Death.” So, remaining lean and embracing continuous/iterative improvement are of the essence. That’s a quality mantra rather than an excuse for forgoing best quality practices.

Back to economics again: quality management’s definition of “customer satisfaction” is now table-stakes and compliance in that area drives low-cost commoditization. “Customer delight” is the higher benchmark that commands a premium and the kind of margins enabling us to re-invest to further innovate.

Let’s now state the obvious, “customers” are human beings, aren’t they? Interestingly enough, the more sophistication and diversification, the higher the need for (D) humanizing technology so that we can better create, consume, use and democratize any digital services. In turn, this has fostered (E) Design Thinking as a leading innovation practice that intersects art and science. Design Thinking addresses HMS, Human-Machine-Systems, by prioritizing HCD, Human-Centered-Design.

In terms of economic effectiveness and efficience, that means outcome-oriented system-sizing, rather than over-engineering waste. It also means the definition of meaningful and purposeful requirements: some are designed to meet customer satisfaction metrics, while others are explicetly thought out to exceed that baseline and, hence, to actually deliver the X-Factor prompting customer delight. All key to customer acceptance and adoption growth.

Better yet, one of the event’s participants volunteered the fact that “good design” factoring inuitive interaction, advanced dataviz (data visualization) and effortless controls was proven to shrink the sales cycle by literally half: not only customers perceived and experienced the service’s taginble value early, the sales team was also able to approach more customers in that timeframe. Innovative Human-Computer-Interaction based on information design, value based tasks, streamlined processes, intuitive data visualization, effortless controls and overall UX, User Exeperience, double as compeling demonstration tools.

This is a side note: that has already become a critical success factor in Artificial Intelligence’s new developments, AI being software’s top transformational exponent as DSS, Decision Support Systems for humans and/or machines become quintessential. I will detail that in another post.

One last thought… (F) software’s pervasiveness has also brought along Agile development practices. These include “user stories” borrowing a Design Thinking technique by which application features are defined by sinthesizing human optics (persona/outcome/rationale) to put technical myopia at bay.

After all, we should all be in the business of making tech human. Otherwise, what would negating or ignoring that say about each of us and our collective culture?

d.SCI: Intersecting Information Design, Dataviz and Cognitive Art

I am joining a discussion on Information Visualization and Interaction Design… and the integral role of Cognitive Art to deliver innovative HCI (Human-Computer-Interfaces.)

Heare are sample projects that I have been involved in. This set showcases: multi-modal user interfaces, metaphorical abstractions, and cognitive models, as well as ergonomic form factors that optimize for extreme ease of use.

d.SCI refers to a methodology that I am working on which purposely intersects design and science. In this particular discussion, human congition and affect are the topics of interest.

Project Portfolio - 1

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Project Portfolio - 7

Project Portfolio - 6b

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Project Portfolio - 10


Debunking Design Thinking Myths

“Reflecting the diversity of the agenda, we are thankful for the support of our advisory board. The board is integral to the development and execution of Design Thinking, supporting the strategic positioning of the brand and advising to the content and participants that matter most. Hear from some of the greatest minds in Design Thinking as they shed a light on its mysteries and separate fact from fiction.”

Debunking Design Myths CoverDesign Thinking Advisory Board.jpgDesign Thinking - Jose de Francisco 1

Design Thinking - Jose de Francisco 2

Design Thinking Agenda



Agile Software in a Hardware World


“The world of IoT and connected devices is expanding rapidly. We all carry super computers in our pockets and interact with everything from home automation, cars, consumer electronics, and healthcare devices.”

“In this complex hardware + software environment the product development cycle can be tricky. For example, you can’t just follow agile software practices by the book when you’re building a connected pace maker. So how do we approach product development when the stakes are high and the moving parts are many? During this discussion we’ll be tackling topics such as:”

“How do you roadmap a product which includes both hardware and software components? How does agile development fit in? How does the regulatory landscape affect how we approach development and iteration? How do you build teams around these integrated products? And how do you keep them in sync and working together?”


I’d first like to thank the team at DevMynd for their kind invitation. I am looking forward to joining the panel discussion in Chicago this coming Thursday, February 22. In the meantime, I will welcome any comments and insights as I gear up for this discussion.

I’m working on outlining some of the myths, dilemmas and trade-offs that I have encounter as an Industrial Designer and in Product Management.

From a design perspective, there are two topics worth looking at: Design Thinking as a Human-Centered methodology and its outcomes in terms of: (a) utility, (b) usability, (c) consumability, (d) affectivity and (e) the composite and differential value of the resulting digital experiences that involve software and hardware.

This “new brave world” equips us with the freedom to explore new form factors, cognitive models and, most impoartantly, the development human x technology networks. Some of the specifics come down to design semantics re-defining HMS, Human-Machine-Systems, in the context of multi-modal user interfaces and innovative interactions where Machine Learning and new visualization paradigms happen to surface.

From a Product Management viewpoint, there is a need for also pondering about how to best leverage Design Thinking beyond Industrial Design and Software Development to talkle product and service strategy. Here my focus gravitates toward addressing: (a) success factors and (b) limiting factors under control, as well as (d) other determining factors beyond our area of influence that can impact the difussion of innovations either possitively or negatively. Moreover, I like to couple business model innovation with behavioral economics and information network effects.

This construct really boils down to capturing the essence behind (e) stakeholders’ acceptance criteria and (f) the users’ engagement, adoption and growth rates. This means defining capability and maturity levels and how to best factor for the fact that they adapt and evolve over time. Obviously, this leads to taking a close look at how to best intersect Lean and Agile practies, but not only, so that we can lead and navigate constantly changing environments in “digital time.”

Let’s get down to a more tactical level: end-to-end system design entails a mix of loosely and tightly coupled elements, and a platform approach to operate at speed, scale and wider scope that what black boxes can match. A reality check unveils a hybrid world where decisions on capacity and performance levels, as well as serviceability and dependency levels drive decisions toward optimizing for distributed systems and, therefore, the rising value of end-to-end solutions vs. point solutions only.

In that context, inter-disciplinary teams involving creative technologists and domain experts make our organizations effectively diverse, smarter and innovative. Otherwise, self-defeating arrogance, conflicting silos and technical myopia can make pre-production and production be costlier by promoting unncessary friction and getting everyone to work harder and harder rather than smarter. Typically, that negates productivity, forces a number corrective actions, and significantly shifts and/or downsizex sought after results.

The beauty of the Studio’s human-experience-centered practice is a healthy obssession for delivering “meaning.” The definition of “meaningful outcomes” (rather than churning outputs) makes these organizations behave based on value and impact. We strive to foster not just customer satisfaction and net promoter scores, but measurable customer delight and network effects (superior and service-level performance indicators) which, in turn, set and streamline technical requirements.

Long story short, the Studio’s mindset (critical thinking / wonder & discovery / problem solving) and workstyle (collaborative / experiential / iterative / adaptive) help explain why creative technologysts are instrumental and serial innovation engines for the digital age.


Footnote: the term “team of creative technologysts” was first coined by Nokia Bell Labs back in the 1940s to single out the differentiated value of inter-disciplinary undertakings. In the late forties, Bell Labs’ Clauded Shannon pioneered Information Theory and John Karlin set up the first Human Factors Engineering in industry. That HFE team was formed by a pyschologist, a statistician (the father of quality control visualization,) an engineer, and a physicist.