preemptive analytics

“A health body involves a ongoing commitment to healthy habits and despite these efforts disease can occur when we least expect it.” – J. Durant

Over the last four decades we have been involved with numerous examinations and initiatives that centered on building business.  Some occurred as a result of issues that arose and others occurred in response to potential opportunities.  Each and every effort had one common element, the lack of an objective assessment.  Now one might wonder why is this at all important?  Flanked by skilled professionals that are intimately familiar with the business why would an outsider provide value?

In this world of opportunists these are very valid questions and often the conclusions reached are abetted by prescriptive solutions of the assessing organization.  We have not seen any marked difference between the big and the small, or the local vs. international organizations performing these so called assessments.   It all comes down to ceasing opportunity when it presents itself and optimizing on existing presence within the company.

I Know My Business

This is a true statement and one that a small fraction of companies cannot attest to.  However, what you know is about what is and not what could be.  Like a person who lives with perpetual pain, they grow accustom to it and will find ways to compensate for it.  Companies will do the same whether its a matter dealing with specific human elements, market issues, product status or even leadership.  Pain becomes a condition and diverted attention is given to other options with the hope that they will replace the discomfort.

Knowing your business is a valuable element in the independent and objective evaluation process.  It solidifies consistency, understanding and harmony of operation but it can also reveal discord.  I’m sure you have heard of difference of opinion that exist between people and even documented processes.  How does this happen?  It is possible that this occurs simply as the result of maintenance attention, but it can also be the result of misinterpretation or disruptive events.  Left unattended the flexibility of adaption creates inconsistencies.  While we would hope that these would have little effect they can turn into full fledged customer servicing nightmares.

Companies that are reticent on the need for some form of independent and unbiased examination are bordering on a state of denial.  Possibly out of fear of the unknown or that they don’t wish to introduce more disruption to the existing chaos.  However in this latter case the chaos is often the result of the health conditions of the organization.


An urgent outreach is symptomatic of issues.  It may simply be the lost of insight or it can be the result of a barrage of internal and external challenges.  Think for a moment about the journey of Research In Motion (RIM aka Blackberry) and their boom to bust to recovery scenario.  Here was a company that flourished with a dominating 37% of US market and a commanding presence in almost every business technological arsenal.  It had presence and it had endorsement.  Slowed growth fueled by operational mishaps drove them to the brink of closure.  The recovery was slow, painful and littered with senior corporate replacements upon replacements.  It wasn’t until 2015 that earning returned to a level on par with 2010.   Some would argue that it was a great learning experience, but is it a ‘great’ experience to endure this level of pain and newly created uncertainty?  The future remains still in question for RIM and to a similar extent to what was experienced by Nokia during the almost identical time period.  So what went wrong?

  1. Failure to be objective and consider the ever existing presence of failure.
  2. Measuring and evaluating conditions on a routine basis but denying the reality of threats and obstacles.  You may have a better mouse trap but if you can’t maintain or convert markets it is irrelevant.
  3. Celebrating loyalty and customer support but overlooking sustaining relations.  Many still appreciate the Nokia 3310 and was recently reintroduced in early 2017.  The same holds true with the Blackberry 9800 and has features reflected in some of the new models being introduced.
  4. Narrow examination perspective concentrated heavily on back end sales and promotion and failing to look at operational/research advancements.  Let me state that this isn’t completely and 100% a complete failure.  Rather it was not given the attention that it should have been to measure holistically the health of the business.
  5. Emergence of fire fighting over attention.  As the business started its rapid downward descent more meetings, reporting and analysis took place.  Decisions were made to bring the business back on course.  In direct response to markets and investors drastic steps were taken to replace known resources with unknown resources.  Again unknown as to the context and the abilities as it pertained to each organization (and known to the extent of what they had to offer from another business enterprise).
  6. Total and complete abandonment of existing strategies.  We would call this ground up or zero based approaches.  My concern, and this isn’t a matter of personal style, is that if a total replacement is needed it makes the assumption that there was no redeeming value to what existed.  If this is the case then why did it take so long for the business to raise the flag and embark on a replacement (pride, effort, disbelief, confusion, helplessness, inabilities???)?   Therefore if there was value then why was it subjected to a more pragmatic recasting?
  7. Market conditions were totally overlooked with the belief that market presence and prominence would in fact allow for leadership to exist.   Looking back on the rise and fall of businesses, technology based or others, it all comes down to misguided arrogance and a lack of humility.

Assessment Flaws

Objectivity is an important part of assessments.  A flaw however is to what extent is objectivity is maintained.  For example, assessment organizations often have biases and build their evaluations around those biases.  Maybe its a process or a technique or even what the assessment will concentrate on.  Often is the case that its these very biases that the credibility of the assessor is based upon.  Without prejudice or opinion an example is Gartner’s Magic Quadrant Research Methodology that outlines the way they depict a company’s industry positioning.  Rest assured that all organizations has some linchpin tool.  After all most would not consider any assessor or simply stated that they come in, look about and ask a few question in order to reach as assessment opinion.  So how do you address this prescriptive bias?   One must look deep and hard into what the tool/approach will address and how (aka scope).   Is this really what you are expecting, is it looking at elements that haven’t been considered, and will it provide unbiased insight?

Another flaw in the assessment process is bench marking.  Will the assessment measure your organization on fair terms or on an overly simplistic basis?  If you are a health care clinic are you being measured against research hospitals?  While the information may be insightful it may be a bit out of character to treat all health institutions on the same basis.  It should also be noted however that there may be some elements of similarity that will exist and need to be shown.  This is where our understanding and approval of the approach will come into play as it relates to the assessment model.

Credibility can be a problem.  New entries into the assessment arena suffer from the lack of credible endorsement.  The same can also be true when credible assessment organizations give way to the field personnel that are used on the engagement.  Both require handling with care.  It has been our observation that there have been good and bad situations overall.  The deciding factors involve;

  • Model used,
  • Level of transparency,
  • Degree of involvement,
  • Independent unbiased and adaptable data sources,
  • Field of vision beyond the present, and
  • Interpretation with action plan (which considered right options vs. ‘my’ options).


Humans look for approval, they look for endorsement and we strive for success.  So do companies but unfortunately daily demands get in the way of objective and ongoing self-examination.  The advent of more progressive analytics have made significant strides forward.  Despite data shortcomings many are getting authenticity back on track purely based on contributory value.  An essential part of transitioning on a routine basis and dealing with disruptions is a healthy assessment mechanism.  It cannot be effectively used on a piecemeal basis and needs to be done in a comprehensive fashion.  This is not entirely a matter of performing periodically but can also be embraced with a comprehensive framework of unbiased analytics but further supported by unbiased evaluation.

Know who you are, know where you want to go, be aware of your surroundings (internally and externally) and rapid readiness to transition remains a key component over a plan that needs constant care and attention.


“A mind is a terrible thing to waste and I dread to think that it will become more so with the assistance of a collective of machines.” – J. Durant

No, my age has absolutely nothing to do with my concerns and apprehensions.  If anything its the result of applied human intelligence and the creative processes that spin off from it.   It is fair to say that today we are into an experimentation cycle and are using research as a means to see just how far we can take the science of artificial intelligence.  There will be some, like Alphabet, who are ahead of many.  Let be perfectly clear that this is a normal learning curve, not just for the innovators but will prove beneficial to later adopters as well.

I do have concern about the notion of intellectual implosion.  Intellectual implosion is where the deployment of AI becomes centered on a closed and narrow application universe.  A simple example would be the use of an AI framework that provides ‘what if guidance’ for business decisions.  It sources of input become limited to a single domain, the company.   Now you might respond to this by saying that we then need to entertain other external sources in order to further elaborate the possibilities both arithmetically as operationally.   But here is where we introduce several factors of concern involving these external sources.  Some of the concerns include;

  • Accessibility and negotiated access
  • Timely (at least as expedient as internal sources) Availability
  • Reliability
  • Balance of understanding (simple definitions)
  • Units of Measure
  • Raw and Deductive Elements

It then becomes a question of need and viewing the concerns with open eyes.  While traditional systems can contain damage, an AI system can propagate even the slightest condition extensively.   It’s not a ‘do not go there’ condition, it means that more regiment must be deployed to check, authenticate, isolate, repair, respond, or release in the course of use.

Beyond Implosion

Let me reiterate that my concerns are not my hesitation to progress.  In fact I am a strong proponent to most things that can make our lives more enriched and productive.  But with that said it places an immense burden on the shoulders of engineers, architects, administrators and management to see to it that we act responsibly.  Our concerns are not just something that is downstream but starts with our present conditions.  Nearly a day goes by when we don’t hear of some technological mishap.

  • Compromises
  • Attacks
  • Failures
  • Denial of Service
  • Efficiency Losses
  • Unreliability
  • Usability Challenges
  • Technological Excesses

are but a few of the things we face today.  How will these challenge what we do tomorrow if we are to advance in the direction of AI?  Overlooking these will be solved by AI, they will be magnified and even acted upon.  The old mnemonics GIGO (Garbage In-Garbage Out) takes on a whole new depth of meaning.  Where humans would act now a rule based action would occur in mostly a non-visible fashion.  To emphasize this point I recently read about experimentation being done at Alphabet where the AI platform had adapted itself to conditions that weren’t set in the rules (came up with its own auto response/reaction).  This for some might be a bit discerning but it shows not only the depth of capability but also the need for the extensive level of human consideration that must be exercised with each an every element.  Catch state, rule limitations, plausible creations elements and redo back checking are but a few of the safety nets that can be considered.

Human Intellectual Depth

As suggested in the previous section there is an elevated level of human thinking that is necessary in AI.  It’s not simply a language form, some rules, data inflow/outflow and a permissive deductive element… it requires real thought, collaboration, postulation, experimentation and a mindset of value generation maintenance.  Present experimentation aside we need to think abnormally.  I think of this is the sense that mere replication of present habits, conditions and outcomes may or may not be the way we need to go.  Why create a robotic arm that emulates the limitations of a human arm or for that matter why create an arm when it is possible that some other form of fetch-release-retain-manipulate mechanism might be better engineered?  In the same context, why think of need or solutions utilizing AI in the same way as we would today?  While it may be comforting that we ‘can do it’, the focus is upon outcomes and growing possibilities.   Even these have a strong potential for change.  The fluidity of AI will change us from thinking in a steady state sort of way to one in which we are driven by rapid adaptation.  The bigger limitation for mankind is the ability of adoption and possibly whether some of the adoption will have to remain vested with the technology.  It remains quite possible that some adoption will remain out of human hands because of the frequency and extent to which it is taking place.

Big Questions

Thinking about the topic should excite us but is also apt to raise up a multitude of questions, concerns and elements for investigation.  Listed below are few of the ones that I have been pondering and I hope that it can be used as a basis for your further inquisitiveness.

  1. How will AI-AI or even Global AI be negotiated?
  2. Will AI-AI/Global AI represent a definable limited and restricted access point(s)?
  3. How does Smart Cities play into AI applications?
  4. To what extent will not AI institutions hinder?  Who will be hindered, AI or the non-AI player or both?
  5. What happens when AI acts cross over that are either wrong or out of control?
  6. Will risk become normality and normality become risk (in the present context)?
  7. Rogue AI threats and issues?
  8. What other present day technologies and practices will put under strain?
  9. How much computing power will be required and the importance levied for comprehensive network connectivity?
  10. How does it affect society and human collateral?
  11. Will the divergence from task to intellectual focus enable or disable societies and companies?
  12. Pervasive and responsible constraint becomes a matter of philosophy.  Should it be regulated, mandated and reshaped?
  13. Convergent roles require collaborative AI interaction (eg. elements of software engineering such as dev-ops, verification & validation (V&V), analysis).  Considered or overlooked or simply ignored?
  14. Extent of human intrusion and at what level of intrusion?
  15. Is there a safe state for change or is it invoked real-time (and should it)?
  16. What paradigms will change, become obsolete or need to be totally created that involve not just AI but also it’s close partnership with Robotic Process Automation (RPA) and advanced analytics?
  17. Speed and quality have plagued businesses, will diametrically different levels of speed give way to quality issues (resolve, mask or create)?
  18. …. Others…. over time there are I’m sure more.  What will your additions be?

“For every complex problem there is an answer that is clear, simple, and wrong.” – H. L. Mencken and as I have often said “A complex problem doesn’t necessarily require a complex solution.” (J. Durant)

Do we move forward with AI…. YES.  Moving forward is not with reckless abandon but still following sound business and engineering tenants.




“Fear is debilitating and causes irrational thinking.  We need to back up a couple of steps and look at things in a better light before we throw ourselves from the building.” – J.Durant

For quite sometime we have been bombarded with articles and news about the coming of artificial intelligence (AI), robotics and all sorts of other ‘human replacement’ tools.  As I am sure some of you are aware there are just some things that these things can’t do or at least we would not permit them to do just because they might be a bit more reliable, efficient and consistent.   There again we have walked on the border with such controlled experimentation as cloning and various other forms of neuro response solutions so who knows just what kind of mad scientist may be lurking to set forth their madness in intellect driven machine.

So let’s take a moment and accept a few things…..

  1. Some form of intellect driven solutions will be put into play and these will replace legacy solutions that involve a human component.
  2. Most like intelligent solutions will embrace a consumer-to-business (C2B) paradigm.  This will ultimately reduce costs and expediate the formation of relationships.
  3. The care and oversight for such solutions will require humans.  However, rather than relying upon casual oversight they will be nurtured by intelligent analytics either in the form of predictive or preemptive forms.   And,
  4. Robots are apt to be involved in order to serve as a service conduct into the interaction with the AI environment.

Now for the really questions that never seem to get mentioned from those expounding on the AI exploits of leading companies like Google, SalesForce, DeepMind, Facebook, OpenAI, Baidu, Microsoft Research, Apple, IBM and Remark.   But in delving into these deeper it is clear that there are a few elements to recognize.  Some of these are noted as AI involved based purely on name and maybe a slight be of tinkering with the concepts and technologies.  There are some that are heavily focused on the AI mechanism that would be used to drive an AI like behavior.  Finally there are some that have wrapped the AI wrapper around an intelligent process, possible an advanced analytic element, and labeled it as AI.   You are also apt to see a similar situation with terms such as learning machine and robotics (especially those that are non-mechanical) as well.

Classical Transitioning Concern

An all to common condition that exists in transitioning is having a plan that is doomed before steps are taken due directly as a result of existing issues.  Thus far we have not seen any of these points raised up by the AI and robotics enthusiasts or those who have expressed guarded reluctance to journey toward the utilization thereof.   Some of these existing conditions include,

  • Green field conceptualization of the AI model and the metamorphic conditions one can anticipate.
  • Interface negotiations from sending as well as receivers.  Stakeholders in receiving solutions may be a bit reluctant to accept AI driven infeeds.
  • Verification and Validation (V&V) readiness.  Most recently British Airways had a system wide shutdown that crippled their operations.  If we are experiencing these conditions in complex networked but traditional systems what is it going to be like with flight by automatic systems like AI?
  • What mechanisms will be used to fuel the AI solution?  Will those mechanisms be ready to provide reliable feed information but in doing so be expedient enough to fuel the AI application?
  • Have boundary reach parameters been set?
  • Consideration for security, validation, performance, real-time conflict management, in-flight updating, and some of the more technical elements of AI?
  • Has thinking been toward ‘right solution’ and not remain focused on existing solution?   Again more green field/blue sky thinking.
  • Formulating a growth based design that will engage elements of robotics and analytics.
  • Understanding that the AI solution may be more than just an event driven paradigm and will demand the inclusion of event base stimulation, deductive modeling that builds upon (or adjusts) a rule frame repository, and the concepts of prediction/authentication/ and progressive simulation (apart from the live environment).
  • Destination driven repository containers that are distributed but interconnected globally as opposed to single destination service.  This also brings up the question about non-stop up-time.
  • Extent of human or non-human intervention schema.

And there are allot more that are required in order to insulate from failure and elevate the opportunity of success.

Circle Condition

Unbeknownst to consumer/recipients of change there exists some form of exploratory cycle.  It may be as simple as a survey and an alpha test of market, or as formal as experimental research.   I reread an article (actually from a different source) on TensorFlow Playground a working example of neural technology.  Impressive and stimulating, well illustrated form of scientific/mathematical application to draw deductive suggestive outcomes with a high probability of accuracy (but not at 100%).  Then I got thinking about whether 100% was attainable from humans either, after all we are prone to mistakes whether through random attention or the result of circumstantial conditions that exist.  Clearly the purpose is to build a sense of trust and understanding, as a commercial effort for the market place.  It also illustrates that the technology was being applied to the known science of math and to legitimize its ability.  What we have seen however is that the line between research and usable solution is often a fuzzy line.  The jumping from concept to application overlooks some grooming required and especially in this case the need for a science that has an element of runaway evolvement based on conditional stimulation and seed data.

In some respects the concepts and principals of AI follow a similar path as is the case with compilers.  There are a finite set of conditional parameters that can be involved based on formalized criteria, set by the institution, to produce and outcome.  What creates the circle is that the outcome is then feed back in the process to which some events may be repeated and others taking a totally different path.  The fear isn’t in the use of the technology it’s all of the possible things that can go wrong.  To understand their potential and to determine what the appropriate level of care that must be exercised should be.   This is not a path in which we have seen similar debates about before.  Space programs, nuclear reactors and fly-by-wire systems have all had their moments of glory and those times when intervention (and often spot creativity) must be exercised.

So Where Are We Now?

We are in some interesting times.  It remains uncertain the degree and speed in which AI will advance.  My suspicion is that for some that are already poised with intellectual driven tools, whether it be predictive analytics (ready for preemptive forms), robotic clusters looking to advance from rule based paradigms or semi-thinking information technology solutions looking to employ a bit more merging of trends with behavior change they definitely will have a leg up.  For the rest it will become a decision as whether to wait or to start taking some of those formative steps now that exists for the organizations that are poised.   Looking at past failed attempts at AI it was the result of institutional support (left mostly to universities and the Department of Defense with Ada).  Today respected institutions, like Google, provide a groundswell of interest and support by association.  Whether its rightly so is not up for debate but rather to be acknowledged as a fact.  It is not without risks but as long as we humans have control we can do what is needed to insure that our AI will succeed in a controlled and appropriate fashion.

The maturity of a game changing technology is reflected by the non-academic clarity of how it is characterized and transitioned to the society to which it applies. – J. Durant

I consider myself very lucky that I was given the chance to work with computers at a very young age (1967) and at a time when the concept of automation was more a novelty than a pervasive reality.  It was also quite by coincidence that it was working on a project  (Ford Grant Project/BASIC compiler construction) at an institution (Dartmouth) that was a short distance from my hometown in New Hampshire.  At the time I was fascinated by what could be done with automation and how it could be used as a tool and not as a treat to society.  But I think back to some of the words that came from my mouth, as a babe of technology, and wonder whether they have had some play in all of this.  One example was while working with a bit of machine code I grew frustrated by the never ending barrage of diagnostic error conditions.  I made statements to my mentors (Drs. Kemeny and Kurtz), “if the machine is so smart to tell me I have a problem why isn’t it smart enough to correct itself?”.  While naive it certainly is within the realm of what we envision AI to do and not require the use of time nor intervention to resolve.

Today, some 40 years later, I am no less fascinated by the potential of technology.  At the same time I am also utterly disappointed with the lack of consideration given to transitioning society for its acceptance.   During the course of my life I learned that the best ideas often fail as a result of inept ability to #transition markets (also known to some as market conversion).  Even the humble personal computer had its moments until manufacturers demystified the technology and produced an affordably simplistic paradigm.

A Bit of History

Artificial intelligence (AI) had some early roots at Dartmouth in 1956.  The basis of academic research surrounding the use of computers to perform tasks lead scientists on a pursuit of assimilating thinking (or what we now refer to as learning machines).  Since that time there has been the ebb and tide of AI exploration.  My first exploits (1970s) were with a simple personal computer based package produced by Visi Corp. called ‘VisiExpert’.  It was a rule based solution that would allow you to create lists of conditions and correlate them to one or more rule bases (or in this particular case a simple set of tables).  The example that the package came with was the pairing of wines with cheeses.  I’m sure we could have done something similar with microbreweries and draft choices, or with employment roles and applicants.  The product never got much acclaim not because of its merits but because society was still trying to get their grasp around spreadsheets and word process solutions (databases would come later as they contributed to mailing and label list processing and table feeding for spreadsheet analytic review).  We saw a reappearance of AI in the form of Ada, a high-level Pascal derivative that introduced the concept of ‘object oriented’.  For some the provided a stepping off point from linear programming paradigms and created the potential for reference-able containers (or objects in terms of 1980s nomenclature).  But was this really AI or was it that the reference-able elements created the impression that conditions could invoke established constructs?

Since this time we have seen several what has been referred to ‘AI winters’.  In simple terms these were cooling off periods cause by economics, introduction failures and the reduction in academic and industrial research.  In the corners however there have been those individuals who sustained the course and continued to chip away at the rough hewn work of their predecessors.

Why Now?

There has been allot of topically provocations that have brought AI back to the forefront.  Utilization of big data, robotics advancement from mechanical to intellectual and demand caused by shortfalls in talent resources.   So while society laments about the concerns over loss of jobs and potential infringement on confidentiality the real culprits are in fact self-inflicted.   As I quoted at the onset, the conceptual framework that reflects the interrelationships of technologies (robotics, AI, analytics/data) remains loosely defined and fraught with personal opinion based prejudice.  It isn’t without coincidence that the few models that exist have yet to be proven in either an industrial or an academic research setting. so  how far would you lay trust unless you are also in an exploratory mode?

Let me get to the point on the two concerns voiced by the person on the street.  One – job loss (YES) but this will be replaced not just by people to care and feed AI but also those who will be engaged in different, yet to be defined jobs.  Concern two – confidentiality (YES) but fear not that its a matter of sharing by others but simply a sharing based on the need to know.  Not this sounds a bit like you need to grant permission.  But in today’s world the act of engagement is an act of implied permission.  We must cast away 20th century thinking if we wish to exploit 21st century services.  I recently read a comedic piece posted by Phil Fersht, CEO and Chief Architect of HfS Research involving a phone order being placed for pizza delivery.  It is as follows:

– Hello! Gordon’s pizza?
– No sir it’s Google’s pizza.
– So it’s a wrong number?
– No sir, Google bought it.
– OK. Take my order please .. – Well sir, you want the usual? – The usual? You know me? – According to our caller ID, in the last 12 times, you ordered pizza with cheeses, sausage, thick crust
– OK! This is it
– May I suggest to you this time ricotta, arugula with dry tomato?
– No, I hate vegetables
– But your cholesterol is not good
– How do you know?
– Through the subscribers guide. We have the result of your blood tests for the last 7 years
– Okay, but I do not want this pizza, I already take medicine
– You have not taken the medicine regularly, 4 months ago, you only purchased a box with 30 tablets at Drugsale Network
– I bought more from another drugstore
– It’s not showing on your credit card
– I paid in cash
– But you did not withdraw that much cash according to your bank statement
– I have other source of cash
– This is not showing as per you last Tax form unless you got it from undeclared income source
-WHAT THE HELL? Enough! I’m sick of Google, Facebook, twitter, WhatsApp. I’m going to an Island without internet,where there is no cell phone line and no one to spy on me
– I understand sir, but you need to renew your passport as it has expired 5 weeks ago..

Although totally comedic it remains plausible.  But again my question repeats itself, is this really AI or is it simply a form of what is also being touted by the label of DevOps (Development Operations)?

More Work Required

Aside from the need for framework clarity there also is the question of transitioning of society to embrace the emergent AI paradigm.  People aren’t looking to be sold on the merits or wowed by simple examples.  What societies need certainty about is the clarity of the vision and how control can be maintained.  Images of runaway robots, inaccuracies, false actions and other elements of mistrust abound.  These represent apprehensions created from shortcomings that exist today without AI that can only be elevated with the deployment of a data driven, rule based solution.  Certainty must be earned and shown.

I also believe, in the field of many-many things that need to be done to promote and empower AI, that we need to be looking at things very differently.  If something is done in a certain way do we still need to do that?  Do we need to ask the question if we already have the information or do we need to do it differently or do we need to do it at all?  This isn’t just pertinent to AI but can be equally asked for robotics or even advance analytical applications.  Do we need to perform a six loop analytical calculation or is sufficient for us to simply be alerted and observe a present condition in anticipation of a potential outcome?  It’s these sorts of question but more importantly the thinking mind set that will put advanced technologies like AI into the realm of usable/plausible.


Of course until finality.   I believe we need AI, not because I’m a technologist that is fascinated by potential but because I am concerned.  I’m not concerned about job losses or confidentiality they have exist, will continue to exist and that is just the way it is.  If you are concerned about job losses you can escape it with being self-employed, you can lose that too with the stroke of a governmental pen or the lack of personal attention to the care and feeding of an enterprise.  You can’t avoid the loss in confidentiality by believing you can lock it away, life in the 21st century is a transparent box whether you want to believe it or not.  If you want total confidentiality then become a hermit but even the hermit has someone who will have your fingerprint.  AI like robotics and analytics has a place to create an opportunity for thinking.  Whether this is focused upon innovation, optimizing, creating or simple recreating the ability to put to task routine parts of our life should be embraced.  Those that chose not to embrace will be destine to a emerging nation paradox where brute force in numbers will overlook efficiency though work augmentation by mechanization.   You can chose to break up concrete with a workforce of thousands or employ a machine that will convert it all to rubble in a matter of hours.  The decision is ours and ours alone.

Yes there will be a next.  ‘The next’ will explore the clear thinking necessary to create a outcome based AI and not a crafted emulation of what we think is human though and logic processes.  A look into the rough constructs necessary to transition from legacy intellect (automation and non-automated systems) to the next forms of creating a durable AI learning machine paradigm.

Till then keep thinking and dreaming (unconventionally).

In the spring of 2011 Frost & Sullivan held a BPO event in Manila.  Held on the heals of the much welcomed attainment of #1 global status as the lead in BPO services the Philippine BPO celebration was in full swing.  There is nothing like a celebration in the Philippines and nothing can dampen the opportunity or the spirit of such revelry.  So it comes as no big surprise that the words I shared at that event were drowned out by the merriment that was being enjoyed.  It was no small feat that rising to this level of achievement over India had taken place.  The future was all bright and there was a sense of invincibility.

While some make predictions on the basis of a hope that no one will remember when it doesn’t take place.  I chose my words careful in issuing a warning that while celebrations are taking place it is also the time to look towards reinvention.  Clients were investing heavily in technology and in doing so it remained committed to solutions that provided long term value and not simply long term reduced cost containment.  This being the case in supporting a month-on-month commitment to manual and voice based BPO support.  The investment in technology would inevitably lead to some form of replacement for shifting from people to an asset invested technology solution.  At that time we weren’t talking artificial intelligence or robots, I was simply looking at a commitment to technologies that could dutifully provided consistent support with a minimum of added capital investment (vs. expensed loss).

Now that the vogue technologies have reached a level of interest concerns have been raised.  IBAP (Information Technology and Business Processing Association of the Philippines – 5/12/2017) is stepping up measures to address the impact of artificial intelligence (AI) on the BPO industry.  Conferences like the Digital Transformation Summit are also adding to the pace by increasing the interchange on what measures need to be taken now in order to combat the effect that might occur on the BPO industry.  Leading Data Analytics Super Star Dan Mayer has weighed in with the importance to embrace the change by shifting focus to data analytics services.  Finally, even the government (DOST – 2/20/2017 – Fortunato dela Peña) has jumped on board to address the real possibilities of AI based impact on the BPO sector.  So the question is why did the message miss being heard?  But even more importantly why didn’t any of the leading analyst groups (Gartner, McKinsey, HfS…) see or hear it either?  It appears as though there was an intermediate fixation with a much broader expanse of technologies dealing with the cloud, Internet-of-Things (IoT), Big Data and Shared Services leaving way for those up-and-coming technologies to remain as background noise.

I don’t want to be too cynical but the story doesn’t end with an acknowledgement of change.  Its not even going to end with a concern and a grass roots movement to follow a correction path.  What is about to happen will be a groundswell of concern by executive management to not only understand the potential accompanied by the ebb-and-tide effect of change.  It will involve significant strategic decisions being made that will lead to tactical plans being crafted.   History however is a horrifying reminder that shifts, such as this have painful paths.  These are littered by potholes of interruptions and a steep learning curves.

There are many questions that remain unanswered.

  • Can #BPO as a service transition to providing high value/high risk value services (this sector was once called ‘knowledge based outsourcing’… #KPO)?
  • Will customers trust offshore institutions with this level of operational intimacy and have the stakes changed for what a provider will be required to maintain (e.g. institutional security)?
  • Cradle to grave time… trained to capability (is it doable given the current state of AI and robotics considerations)?
  • What mindset change will be necessary to make the shift?  Those that have already set an information technology business unit will find the shift easier.  HOWEVER, it isn’t just about technology enable AI, robotics or analytics its about understanding the various industries being serviced.
  • What should be happening now?  Obviously action but what kind?  Waiting is not suitable at the present, a plan needs to be formed.  But more important is the need to understand that success requires intense consideration for #transitioning.

As early as May 2017 the DTI continues to track progress and goals of BPO as though nothing has changed.  This is of concern, it also echos my concern why the messages of dutiful messengers is not reaching those that can address change and the elements necessary to respond to the ever changing technology and commerce worlds.   You cannot rely upon revenue predictions or employment numbers to remain the same, even if you capture a significant segment of the AI/Robotics/Analytics markets.  There will be reductions in head counts, increases in both revenue and cost predictions and an investment in capital assets to support these sciences.  You cannot expect that these will follow the historical ‘life-and-drop’ paradigm of BPO.  There will be issues of trans border data transfer, security, time sensitivity, ground up development, repository control and matters as simple as general understanding of socio-business transference.  In short, allot to be done in a short period of time.

In closing, I remember the day when the Philippines became #1 in the BPO sector.  I remember some of my Indian clients exclaiming that it was always their plan to shift away from BPO (which I considered to be just a sour grapes comment).  Was it that they saw something that others were seeing but from a different perspective, was it that BPO was the low end of the outsourcing food chain in terms of complexity and cost or was it simply ill sentiments?  Regardless, we need to look at conditions not with a focus on critique but with a look towards the potential opportunities that need to be acted upon now.

#BPO #Analytics #AI #Robotics #Transition #TransitionalSciences

From that single organic nodule of package life an offspring is produced, or not.  This week has been particular enriched with insight and wisdom, some unsolicited and others remaining a bit of a quandary.

This week Hubert Dreyfus passed away.  A professor and a human being, a philosopher who challenged us to consider the practical limits of computers.  Aside from his academic acclaim and intense experience as a human being his message was far deeper than the antidotal point on message.  Yes, he asked us to consider the depth of use and application of technology in our personal lives, business and society.  He appears to have know the extent of human temptation and addiction.  Today we are drawn to the light like a moth to a candle, embracing the new and forsaking the tried and true. Is it because we are afraid of being left behind or are we considering the real vs. illusionary value of it all?  Or is it that we are sitting on the edge of restrained obsolesce and the jump seems right even if we might be stepping out into the darkness with hardly a basis of comfort.  Although I never had the chance to meet him in person I enjoy some brief ‘technological’ interchanges to better understand his stance on artificial intelligence.  During my advanced studies he provided invaluable opinions about the difference between machine learning, the need for social contact and interchange, along with some quite private discussions about risk.   It was a bit unnerving to consider to realize which was always in front of my eyes that the success or destruction is not in the device but the enablement provided by the human steward.   To me it wasn’t just a learned opinion, although gifted with experience at MIT and Rand, but his deep and profound thought given on the subject.

The Junk Drawer

Few people do not have a junk drawer.  Whether it be made up of household repair items or kitchen gadgets we all manage to eek out a space to stash away an much anticipated device of salvation.  Likewise we see the emergence of the same for abandoned technology.  Cables, cell phones, chargers and various explored ancillary devices find their way to ‘the drawer’.  We pretty much know that what goes in is unlikely to come and be used, time is not on the side of technology and the obsolescence that occurs.  But our frugal nature suggests that we or someone might find a need or use for these cast aside items.   I mention the junk drawer in a broader context that we have lots of technologies that have come and gone.  Often replaced by what appeared to be superior solutions, that later prove to be less superior that even more future ones.

I think back to my very first expose to artificial intelligence in the 1970s.  It was with a very simple but quite illustrative product called VisiEXPERT produced by the now defunct Visi Corp.  The product was a very rudimentary rule based artificial intelligent (AI) solution.  Its operational example used the pairing of cheese with wine and allowed for the addition of new elements and relationships.  At that time it was robust enough to learn or be driven by inference, it required aggressive assertions in order to advance outcome delivery.  Later in the 1980s we saw the ADA and LISP development as service languages support for data driven behavioral modification processes.  In both cases their emergence was not months but decades in the making and although solidly formed it struggles to produce a groundswell of disciples.

So our junk drawer continues to grow and with this we see the rekindling of interest.  For those in the AI community the drive is not so much from the technology as it is the promotional support of the business community.  AI is talked about in a single breath with learning machines but how does this all fit together with life?

Embryonic Appearance

This past week (May 5, 2017) I read a piece that Scott Ambler a legendary agile disciple wrote about the “Darth of Qualified Agile Coaches”.  The points reflected a condition in which labels get applied but the lack of substantive value creates an abundance of non-value.  Even though the focus was on promoting professional qualifications there remains a quite similar condition as it pertains to AI.  We see a plethora of AI involved entities who for all intense purposes are new market entrants.  But let us not also be fooled by capabilities driven by attending a course, as Scott pointed out, it involves intense and purposeful experience to fulfill the obligations as an expert in a given field.  Even in my case with over 40 years in the information technology field and intensely active engagement in AI related activities I still feel I have lots to learn.  Its from this vantage point I wish to ask the question about capabilities.

When I think in quite simplistic terms as pertaining to AI I think of ‘the seed’.  That kernel catalyst that will drive the growth of technology based learning.  I also think about risks and what level of permissiveness that we should allow the AI model to undertake.  Embedded in that kernel is data and we need to be astutely aware that data is not always clean, controlled and ready for use.  It necessitates sterilization to make it ready and all must be done in as near to real-time as possible.  Momentary lapses in time or hesitation in commitment of cleanliness jeopardizes the AI value proposition.

To further emphasize this point I will refer you back to some earlier writings on did on advanced analytics.  I find that while analytics also makes use of data, it also has the potential to become a close partner with AI and the learning machine.  As stated in this earlier article (10/15/2014) the real next generation is not in preceptor or predictive analytics, it in the real of preemptive which takes action vs. alerting us of or indicating that a condition has the potential to occur.  In short, the model reflected the raw basis of the learning machine.  While not centered on growth of knowledge and more centered on action the elements exists for the feeding the AI model through preemptive analytics.  I also contend that anything short of being soundly grounded preemptive logic, including predictions is really shaky ground for AI.  The basis of this opinion is the potential for runaway illogical reaction by the AI model paradigm.  In the most simple of examples I think of how I look at a situation and react to later discover it was not exactly as I saw it.  If this had been applied to an AI scenario who knows whether reversion would have occurred and if it did was it through intervention or a separate set of rules to deal with error management?

Conclusion… Just a Wee Bit of Fertilizer

No planting would be complete without a bit of care and accelerated nurturing.  AI is no exception.  In this context our growth enhancement hormone is a combination of pragmatic engineering, anticipatory examination and a purposeful examination of our present state of intellectual discourse.  Most would agree that humans make errors and thus anything we do is both prone to error creation but also possibly error propagation caused by what we presently do.  One cannot view human emulated thought process as simplistic.  Even the most rudimentary movements, considerations, evaluations and commitments entail literally thousands of possible paths and choices.  While technology can handle volume and responsiveness it remains the dutiful obligation of humans to craft the paths, the gates of decisions, correlation of relationships and discernment of probable paths with rational and common place or dissenting opposition.  Its for these reasons that the engagement of personnel involved with AI cannot be causal technological Spartans.  Technical proficiency will remain important but the management of the operating intellectual paradigm will remain critically essential.  This involves raw in-sources from data, progressive analytics, paradigm development and deployment to error corrective models as the minimum for sound control.   Even then understanding is bounded by experience and therefore secondary ability to communicate, examine and model will help bolster the skill set needed for application of AI.

To the consumer there may be some worry or dissension, much as was the case with the use of voice response systems.  People are hesitant to embrace what is uncomfortable or viewed as inadequate by comparison to what they have grown accustom.  So while AI proponents dabble in the science there remains a great degree of need for transitioning of people to a new world order.  Some may enjoy a more hidden affront to consumers where others will be challenged regularly by real-time consumerism exchange.  Simply remember that all things are solvable as long as we understand the nature of the best, the human condition.