Emerging AI Attached to Business Meetings and Process Management.

Some of the work we did in the enterprise a dozen plus years ago was to improve executive decision making based on visualized data.   Especially useful when it needed to focus complex, expensive  and difficult to obtain expertise.    And results that further need to be linked to known and developing process.  At the time we were also working with strategic AI approaches, but it had yet to mature.   It comes to mind now that methods like GPT make this easier, especially when integrated with data visualization and large data base exploration.     Exploring further .... 

Here is a set of posts:   from this blog about the work then.  

Connect to discuss ...     Franz   

Retail Inventory Shelf Analysis with AI

 Worked on something very similar to this in Laboratory Stores, but without AI.   Learned much in the general process. 

Google Cloud Introduces Shelf Inventory Predictive Tool for Retailers

The Wall Street Journal

Isabelle Bousquette, January 13, 2023

An artificial intelligence tool developed by researchers at Google Cloud aims to help big-box retailers improve shelf inventory tracking. The algorithm uses videos and images from the retailer's ceiling-mounted cameras, camera-equipped self-driving robots, or store associates to assess the availability of goods on shelves. The tool was trained on a database of more than a billion products and can recognize products regardless of the source or angle of the images. In tests at the innovation lab of supermarket chain Giant Eagle Inc., the tool achieved more than 90% accuracy, which Giant Eagle's Graham Watkins said is not sufficient to deploy it at scale. Giant Eagle will roll out a pilot program in an actual store, but a chain-wide deployment is not likely for several years (if at all).

Full Article   

Shuffling Cards

 Some time ago when doing simulation projects, we discovered some problems in code based random numbers.    We even tested them vs some physical machines.  To feed analytical simulation ofprocess.  Humans are well known to be terrible generators of randomness.  The article below points out that the problem is yet to be completely solved. 

On the Randomness of Automatic Card Shufflers   by Bruce Schneier

Many years ago, Matt Blaze and I talked about getting our hands on a casino-grade automatic shuffler and looking for vulnerabilities. We never did it—I remember that we didn’t even try very hard—but this article  shows that we probably would have found non-random properties:

…the executives had recently discovered that one of their machines had been hacked by a gang of hustlers. The gang used a hidden video camera to record the workings of the card shuffler through a glass window. The images, transmitted to an accomplice outside in the casino parking lot, were played back in slow motion to figure out the sequence of cards in the deck, which was then communicated back to the gamblers inside. The casino lost millions of dollars before the gang were finally caught. ... ' 

Its interesting that the comments point to further experience and research, including using a magician to detect regularity in in choice!

Ask Dumb Questions

Ask enough questions and you will notuce they are getting smarter.

When AI Asks Dumb Questions, It Gets Smart Fast   In Science, September 22, 2022

New research suggests patiently correcting artificial intelligence (AI) when it asks dumb questions may be key to helping the technology learn.

Stanford University scientists trained a machine learning AI to identify gaps in its knowledge, as well as to formulate often-stupid questions about images that strangers would answer.

When people responded, the system received feedback prompting it to adjust its inner mechanisms to behave similarly in the future; the researchers also "rewarded" the AI for writing smart questions to which humans responded.

The AI absorbed lessons in language and social norms over time, refining its ability to compose sensible and easily answerable queries.

The researchers said the system's accuracy at answering questions similar to those it had asked improved 118% over eight months and across more than 200,000 questions ...

Many AI systems become smarter by relying on a brute-force method called machine learning: they find patterns in data to, say, figure out what a chair looks like after analyzing thousands of pictures of furniture. ... 

From Science

The Dawn of Crowdfarms

 Considerable piece starts simple and becomes process-task technical, worth understanding.Note useby China,  considerable detail at the link.  Approach is new to me.    Looking for more detailed examples.

The Dawn of Crowdfarms   By Yihong Wang, Konstantinos Papangelis, Ioanna Lykourentzou, Vassilis-Javed Khan, Michael Saker, Yong Yue, Jonathan Grudin

Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 64-70     10.1145/3490698

Crowdsourcing is the process by which organizations or individuals outsource tasks with an online "open call."9 With tasks posted, and instructions and finished goods digitally exchanged, crowdsourcing enables the geographically distributed online workforce and work solicitors to cooperate on various tasks—improving productivity, social mobility, and the global economy.

Common crowdsourcing practice, illustrated by Amazon Mechanical Turk, comprises the completion of tasks by crowdworkers as opposed to solely computational systems. This approach has achieved impressive results in data clustering, content labeling, and other small tasks that individuals can complete in a short time. However, this has limited the opportunities for crowdworkers to collaborate and develop specialized skills while preventing crowdsourcing to be applied to projects that require higher levels of expertise and closer teamwork, such as software development and industrial design.

As crowdsourcing platforms and practices mature, will they reach a steady state and continue to grow while still focusing on simple tasks? Or, could there be a shift or disruption?

In previous research into Chinese crowdsourcing, we identified a new paradigm that could indicate a shift: small companies that regard crowd-work as part of their formal business and assemble teams to take on multi-faceted crowdsourced tasks requiring specialized expertise. We refer to these companies as "crowdfarms."a A crowdfarm is a small but growing crowdsourcing workforce in China that is positioned between traditional crowdsourcing and consultancies. A similar focus recently appeared in Up-work, which unveiled an "Agency Experience" policy to support small firms that specialize in complex, high-value crowdtasks.7 The emergence of these small businesses in both Eastern and Western crowdsourcing contexts indicates that organizational participation in crowdwork could become a widespread trend.

This article describes a series of studies conducted through interviews to obtain an in-depth understanding of this emerging organizational form. We describe how Chinese crowdfarms that were early adopters of this form operate, explore the perspectives of people who work in them, and assess the implications for the evolution of crowd-sourced work.  .... ' 

Chip Proccess and Classification

 Impressive speed for detecting and classifying images

Chip Processes, Classifies Nearly Two Billion Images per Second

Penn Engineering Today

Melissa Pappas, June 1, 2022

University of Pennsylvania (Penn) engineers have designed a 9.3-square-millimeter chip that can detect and classify images in less than a nanosecond. The chip directly processes light received from objects of interest using an optical deep neural network. "Our chip processes information through what we call 'computation-by-propagation,' meaning that unlike clock-based systems, computations occur as light propagates through the chip," explained Penn's Firooz Aflatouni. "We are also skipping the step of converting optical signals to electrical signals because our chip can read and process optical signals directly, and both of these changes make our chip a significantly faster technology." Penn's Farshid Ashtiani said direct processing of optical signals makes a large memory unit unnecessary. ... 

Should a Black Box be Transparent?

Was once met with exactly this dilemma. 

When Should a Black Box Be Transparent?   Opinions piece with further comments. .... 

By George V. Neville-Neil

Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 23-24    10.1145/3544550

Dear KV,  (Code Vicious)

We have been working with a third-party vendor that supplies a critical component of one of our systems. Because of supply-chain issues, they are trying to "upgrade" us to a newer version of this component, and they say it is a drop-in replacement for the old one. They keep saying this component should be seen as a black box, but in our testing, we found many differences between the original and the updated part. These are not just simple bugs but significant technology changes that underlie the system. It would be nice to treat this component as a drop-in replacement and not worry about this, but what I have seen thus far does not inspire confidence. I do see their point that the API is the same, but I somehow do not think this is sufficient. When is a component truly drop-in and when should I be more paranoid?

Dropped In and Out

Dear Dropped,

Your letter brings up two thoughts: One about recent events and one about the eternal question, "When should a black box be transparent?" While we all know the pandemic has caused incredible amounts of death and destruction to the planet, and the past two years have brought unprecedented attention on the formerly very boring area of supply chains, the sun comes up and the world still spins—which is to say the world has not ended, yet. Supply-chain issues are both real and the world's latest excuse for everything. It is as if children were telling their teachers, "The supply chain ate my homework."

At this point, KV is quite skeptical when a vendor's first excuse is supply-chain issues. Of course, that skepticism will not help unless you have a second supplier for whatever you are buying, which you can use to bludgeon your errant vendor.

Another eternal question, "When is a replacement not a replacement?" is one that will plague us in technology forever. The number of people who believe they can treat whatever they are providing as an opaque box with a fixed API is, unfortunately, legion. This belief comes from the physical world, in which a box is a box, and a brick is a brick, and why would you care if your brick is made from a different material anyway?

Here you see the problem: The metaphor breaks down in the physical world as quickly as it would in the realm of software and hardware. Two bricks may both be red, and therefore present an identical look and feel to the external user, but if they are made of different materials, then they have different qualities—for example, in strength, but let's also consider something less obvious, such as their weight. The number of bricks that can be stacked on top of each other to build a wall depends on their weight, as well as their strength. If you use heavy but weak bricks, well, you can imagine how this goes, and if you cannot, try it—just do not tell your health-insurance plan KV suggested this. And let's say you do not build the wall out of weak and heavy bricks, but years later you replace some damaged bricks with newer, heavier, and weaker bricks. The key here is you would not want to stand near that wall.

A topic KV keeps coming back to is the malleability of software. I keep returning to this because it is this malleability that often results in the catastrophic failures of software and systems engineering. You mentioned you saw timing problems with the new component. I can imagine few situations more treacherous than a change in the timing of a critical component. Timing bugs are already some of the most difficult to track down and fix, and if the timing is off in a critical component, that is likely to affect the system, so good luck debugging that. Those who wish to stand on the "API as a contract" quicksand are welcome to do so, but I am not willing to throw them a rope.  ... ' 

On the Physics of Thought

 Closer to understanding of the thinking process.

ACM TECHNEWS

How the Brain Prepares to Think

By Texas Advanced Computing Center,  June 23, 2022

The University of Texas Southwestern Medical Center's Jose Rizo-Rey and colleagues used the Texas Advanced Computing Center's Frontera supercomputer to probe the physics of thought activation in the brain.

The researchers have generated all-atom molecular dynamics simulations to explore the nature of the primed state of synaptic vesicles, indicating specialized proteins are "spring-loaded" and awaiting calcium ions to induce fusion.

The models only simulate the first few microseconds of the fusion process, but Rizo-Rey posits that fusion should occur in that time.   Rizo-Rey said, "If I see how it's starting, the lipids starting to mix, then I'll ask for 5 million hours [the maximum time available] on Frontera" to record the spring-loaded proteins' trigger and the fusion/transmission process.

From Texas Advanced Computing Center  ...    

Said University of Texas professor Jose Rizo-Rey, "This country was very successful because of basic research. Translation is important, but if you don't have the basic science, you have nothing to translate." ... 

Building Decision Trees by Hand without Code

 Cool little piece that is instructional and even inspirational.   Math and process seen together to produce real value.

Random Forest and Decision Trees by hand — no coding

By Thomas Le Menestrel,      Computational Engineering student at Stanford | Passionate about Machine Learning and Data Science  in TowardsDataScience 

Introduction

In this article, we will discuss Decision Trees and Random Forest, two algorithms used in Machine Learning for classification and regression tasks.

I will show how to build a Decision Tree from scratch using a pen and paper and how to generalise this and build a Random Forest model.

Dataset

Let’s see how this work in practice with a simple dataset. .... ( much more )

Robotic Process Automation

Well put intro piece in Venturebeat  by Peter Wayner

Robotic Process Automation in 2022

In 2021, enterprise teams turned to robotic process automation (RPA) to simplify workflows and bring some order to office tasks. The next year promises to bring more of the same sophisticated artificial intelligence and task optimization so more offices can liberate their staff from repetitive chores.

The product area remains one of the poorly named buzzwords in enterprise computing. There are no robots in sight. The tools are generally deployed to fix what was once known as paperwork, but they rarely touch much paper. They do their work gluing together legacy systems by pushing virtual buttons and juggling the multiple data formats so that the various teams can keep track of the work moving through their offices.

Here are the 10 ways that RPA marketplace will shift and adapt in 2022:

Better Integration

The main job for RPA is to knit together some hundreds of legacy systems that now make up the backbone of many companies. The main challenge for each RPA company will be strengthening the connections between systems. That means more modules or bots in the marketplaces and better versions of the existing ones.

Lower Code

One of the major selling points for many RPA vendors is that their tools can come close to programming themselves through what some call “process discovery.” While this may never be as magic as anyone wants, the tools will continue to simplify this job. It may even approach “no-code” level automation for some simple tasks.

Higher Code

It seems contradictory to imagine that RPA platforms will simultaneously get easier to program and harder, but these changes will be seen in different levels of tasks. While the interns and managers will be able to automate more simple tasks, the developers will be called to customize the RPAs for more complex integrations. In many cases, RPA tools make good frameworks that sophisticated programmers can revise and extend. The RPA handles 95% of the work and the development team handles the last 5%. This is why some companies are reporting that RPAs are more complicated and expensive to maintain than they thought. Companies are asking them to do more and more sophisticated jobs, and that means bringing in better programming talent.

More AI

Craig Le Clair at Forrester Research predicts that every RPA company will either embrace AI or “become a dinosaur”. While this may never become strictly true, there’s no doubt that RPA is one of the simpler vectors for inserting AI into corporate DNA. The standard modules tackle tasks like optical character recognition, machine learning, and machine vision. RPA firms that ship better, smarter AI modules will be able to win more contracts. The accuracy and depth of the AI algorithms will rise in importance.

Divergence

Some firms need all the cleverness that AI scientists can deliver. Some firms, though, do not. Many of the AI options are aimed at dealing with older, paper interfaces or other tasks that require adaptability. One popular job for AI is to convert paper documents into digital form and then search for relevant data like the invoice number or the expiration date for a driver’s license. Some workflows, though, are pretty mature and don’t need this extra dose of smarts. Companies that process little paper or don’t need the extra intelligence may find they’re not as interested in AI-based innovations.   ... ' 

Analytical Gaming for Coast Guard

Interesting example intro of using gaming

Analytical Gaming Could Help the U.S. Coast Guard Address Key Challenges

COMMENTARY

(RealClearDefense)  by Scott Savitz and Abbie Tingstad  from RAND

November 8, 2021

Wargames can provide valuable insights that enable military services to anticipate challenges and improve future decisionmaking. Partly because its primary focus is on steady-state problems not directly associated with full-scale conflict, as well as its limited resources to focus on long-term challenges that wargaming can inform, the U.S. Coast Guard has historically conducted relatively few wargames compared with other military services. While the Coast Guard Academy and others within the service have undertaken some gaming efforts, these remain relatively small in scale or limited to participation in analytical games led by other organizations.

A larger-scale analytical gaming effort could help the Coast Guard improve planning, decisions, coordination, and training across a range of areas. These methods could provide a versatile option for exploring a large range of “what-ifs,” embodying the Coast Guard motto Semper Paratus (always ready). Unlike using other methods such as interviews, surveys, or computer simulations alone, the participatory aspect of gaming can be compelling for boosting understanding, engagement, and discussion as the participants “live” through the challenges embedded in the game. This can be particularly valuable for senior leaders, for whom games provide an opportunity to viscerally experience long-term issues in a way that grabs their attention. This can contribute to organizational redirection to address a changing operational environment and mitigate risks.

Gaming could be particularly useful to a service facing a series of rapid changes, as the Coast Guard is today. Many changes involve rapidly advancing technologies, such as uncrewed systems, low-cost cube satellites, cyberdefense, and 3-D printing. Gaming could help the Coast Guard to ascertain how best to acquire and prioritize specific technological capabilities, recognizing the need to integrate these swiftly advancing technologies with cutters, aircraft, and infrastructure that endure for decades.  .... '

Managing Cultural and Process Improvements with AI

Seeking Truth from Data, Interesting thoughts.

Managing Cultural and Process Improvements with AI

Published on November 9, 2021. Sam Ransbotham

Professor at Boston College; AI Editor at MIT Sloan Management Review; Host of "Me, Myself, and AI" podcast

For me, this week's biggest news is the publication of our 2021 MIT SMR-BCG artificial intelligence and business strategy research report. While not discounting the substantial financial potential with AI, our research this year focuses on the cultural benefits. Based on a survey of more than 2,000 global managers and dozens of interviews, the report is chock full of examples and data about the cycle between Culture, AI Use, and Effectiveness.

S. Ransbotham, F. Candelon, D. Kiron, B. LaFountain, and S. Khodabandeh, “The Cultural Benefits of Artificial Intelligence in the Enterprise,” MIT Sloan Management Review and Boston Consulting Group, November 2021.

Our key result is that over 75% of global organizations implementing AI report that the technology helped improve their culture. In our fifth year of researching AI and business strategy alongside Boston Consulting Group, we found a wide range of AI-related cultural benefits at both the team and organizational levels. Our report, "The Cultural Benefits of Artificial Intelligence in the Enterprise," outlines these benefits and explains how they relate to financial benefits and competitive advantage.

These financial and cultural benefits do not come automatically to organizations. Instead, they depend on active preparation and ongoing management – two important topics also in recent news.

Developing an Appetite for AI: New Episode of Me, Myself, and AI

Organizations need to have systems and mindsets in place to implement technologies like artificial intelligence, and, often, organizations may not be ready. Sarah Karthigan, AI operations manager for IT at ExxonMobil, joined our Me, Myself, and AI podcast to discuss how she prepares for technology and cultural challenges long before starting AI pilots. She ensures end-users know "under-the-hood" what the tech actually does so that users encourage the necessary changes. Sarah observes that "the partnership goes really, really well once they understand the value that the new solution is able to bring to the table." Active preparation for AI makes a difference and profoundly depends on culture. Plus, if you're curious what Shervin's first time trying sushi has to do with artificial intelligence, this episode's for you.

Managing AI to Promote Financial and Cultural Benefits

Of course, just getting ready for AI is not enough to realize these financial and cultural benefits. Managers are still crucially important. MIS Quarterly, a premier academic journal, just published a special issue on the managerial challenges that come with artificial intelligence. Seven papers address different facets of these challenges.

In "AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms", Bowen Lou and Lynn Wu demonstrate that using AI can accelerate new drug discovery... sometimes, but not always. Innovation depends not only on employees' domain expertise, not just AI skills.

Machine learning tools reduce the costs of repetitive tasks but can introduce systematic unfairness into organizational processes. In Failures of Fairness in Automation Require a Deeper Understanding of Human–ML Augmentation, Mike H. M. Teodorescu, Lily Morse, Yazeed Awwad, and Gerald C. Kane introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance.

Sarah Lebovitz, Natalia Levina, and Hila Lifshitz-Assaf question the seemingly objective labels organizations use to train AI tools.  Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts' Know-What describes how experts address uncertainty by drawing on rich know-how practices that many ML-based tools do not incorporate.

In Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI, Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter raise concerns about the loss of unique human knowledge in a host of human-AI decision environments.

Algorithms may produce insights superior to experts by discovering the "truth" from data. But how can systems produce knowledge independent of domain experts yet remain relevant to the domain? When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring (by Elmira van den Broek, Anastasia Sergeeva, and Marleen Huysman) describe how developers navigate this tension when building an ML system to support hiring job candidates at a large international organization. 

Coordinating Human and Machine Learning for Effective Organizational Learning (Timo Sturm, Jin P. Gerlach, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph Tauchert, Ning Nan, and Peter Buxmann) recognizes that humans are no longer the only ones contributing to an organization's stock of knowledge. 

And finally, Strategic Directions for AI: The Role of CIOs and Boards of Directors (Jingyu Li, Mengxiang Li, Xinchen Wang, and Jason Bennett Thatcher) finds that the presence of a CIO positively influences AI orientation discusses how to build top management teams and boards capable of effectively developing AI orientations.  .... '

Hard-Rock Mining

Once consulted in this space.  McKinsey talks the underlying economic processes of mining.

Digging deeper: Trends in underground hard-rock mining for gold and base metals

July 13, 2021 | Commentary

While underground mining methods show higher cost than open pit, their complexity almost always means that there is opportunity in both productivity and cost improvement.

 Article (5 pages)

Underground hard-rock mining accounts for 40 percent of global mining operations but only 12 percent of run-of-mine (ROM) production.1 Underground mines also tend to be more targeted, more costly, and less productive than open-pit mines. Because the choice of which underground method to deploy is predominantly driven by the geology of the deposit being mined, the operator has little flexibility in choosing the mining method given that the objective is to maximize net asset value over the life of the mine. But given the inherent complexity in underground mining, we frequently uncover improvement opportunities in both productivity and cost. Of these methods, stopping not only is the most common but also delivers the highest overall production share, at almost 50 percent; block caving is one of the least-used methods but is responsible for an outsize share of overall production, at almost 25 percent (Exhibit 1). Again, these underground mining methods are often determined by the deposits and the economics of mining and are, thus, somewhat out of the operator’s control. ... '

Is the U.S. Labor Shortage the Big Break AI Needs?

Labor needs and process are being re thought.too.

Is the U.S. Labor Shortage the Big Break AI Needs?

AI May Be the Big Break Labor Needs

Hesitancy and misconceptions about AI in the workplace have long been a barrier to widespread adoption, but companies experiencing labor shortages should consider where it can make their employees' lives better and easier, which can only be a benefit for bottom-line growth.

Millions are unemployed, yet companies can't find enough workers. At this particular moment, businesses need a stopgap solution either until September, when COVID-19 relief and unemployment benefits are earmarked to expire, or something longer-term and more durable that not only keeps the engine running but propels the ship forward.

Adopting AI can be the key to both.

Declaring that we're on the precipice of an AI awakening is probably nowhere near the most shocking thing you've read this year. But just a few short years ago, it would have frightened a vast number of people.

From TechCrunch

Algorithmic Impact Assessments

Welcome  to Algorithmic Impact Assessments.  I should note that I have done optimization and statistical models for decades, and this has very, very rarely come up.   And then only when included in a risk analysis that indicated potential errors being made.   Will all business process be under detailed scrutiny?    Article linked to below is interesting.

Assembling Accountability, from the Ground Up     in  Point.datasociety.net

Algorithmic impact assessments should leverage diverse expertise & complex histories

By Emanuel Moss, Ranjit Singh, Elizabeth Anne Watkins, and Jacob Metcalf

By now, it is a tired trope: Sen. Orrin Hatch asking Mark Zuckerberg how Facebook makes money. Zuckerberg replying with a wry “Senator, we run ads.” Another congressperson grilling Sundar Pichai, CEO of Google, about the Apple iPhone, made by Apple.

Lawmakers, it is said, don’t understand technology well enough to regulate it. They are too old. They are out of touch. They have disinvested from staff and other experts that could help them understand it. And while all those criticisms may be true, why should we expect our lawmakers to become individual experts on every challenge facing society? There are advocacy groups, community activists, forensic technologists, thoughtful developers, and critical scholars who have devoted their careers to building expertise on these issues. Given the burgeoning influence of algorithmic systems over social affairs, and an increasing awareness of the harmful impacts of these powerful systems, we are at a moment in which complex sociotechnical systems require robust, adaptable regulation — and legislatures and regulatory bodies are drafting new rules.

Our new report Assembling Accountability  demonstrates a pressing need to establish algorithmic impact assessment practices from the ground up, which requires cultivating and synthesizing a broad consensus of expertise from industry, scholars, and public interest advocates, including people from affected communities.  ... ' 

Dell Releases Open Source Suite Omnia

 This was new to me, lots more detail at the link. I like the combination of AI, analytics and process workload,  which is the way it should be.  

Dell releases open source suite Omnia to manage AI, analytics workloads

By Kyle Wiggers  @Kyle_L_Wigger   in Venturebeat

Dell today announced the release of Omnia, an open source software package aimed at simplifying AI and compute-intensive workload deployment and management. Developed at Dell’s High Performance Compute (HPC) and AI Innovation Lab in collaboration with Intel and Arizona State University (ASU), Omnia automates the provisioning and management of HPC, AI, and data analytics to create a pool of hardware resources. ... '

On the Power of Work Ecosystems

Much experience in this space.  Though many companies will say they have a well designed ecosystem and processes, it is often not the case,  especially as such systems interact with data, goals, resources, risks  and design. In other words its hard to use the model to predict things accurately.  Below an intro to a piece on how this is being addressed now.

The Future of Work Is Through Workforce Ecosystems

Workforce ecosystems can help leaders better manage changes driven by technological, social, and economic forces.

Elizabeth J. Altman, David Kiron, Jeff Schwartz, and Robin Jones  in Sloan MIT  January 14, 2021

Ask leaders today how they define their workforces, and you’ll immediately hear some version of “Well, that has become a very interesting question, and even more so recently.” Today’s workforces include not only employees, but also contractors, gig workers, professional service providers, application developers, crowdsourced contributors, and others.

Effectively managing a workforce comprising internal and external players in a way that is both aligned with an organization’s strategic goals and consistent with its values is now a critical business necessity. However, legacy management practices remain organized around an increasingly outdated employee-focused view of the workforce — that it consists of a group of hired employees performing work along linear career paths to create value for their organization.

Seventy-five percent of respondents to our 2020 global survey of 5,118 managers now view their workforces in terms of both employees and non-employees. Growth in the variety, number, and importance of different types of work arrangements has become a critical factor in how work gets done in (and for) the enterprise.

We see many companies experimenting with ways to manage all types of workers in an integrated fashion. Several novel management practices have emerged across the business landscape. Even so, few — if any — best practices exist for dealing strategically and operationally with this distributed, diverse workforce that crosses internal and external boundaries. Executives seeking an integrated approach to managing an unintegrated workforce are left wanting.

We contend that the best way to conceptualize and address these shifts and related practices is through the lens of workforce ecosystems. We define workforce ecosystem as a structure that consists of interdependent actors, from within the organization and beyond, working to pursue both individual and collective goals.

Managing a workforce ecosystem goes beyond efforts to unify the dissimilar management practices currently organized around employees and non-employees. It’s a new approach to a new problem that demands a fresh solution. Our view draws upon two years of research that includes two global executive surveys and interviews with leaders and academic experts. This brief article introduces the concept of workforce ecosystems and discusses how they can help managers rethink the way they align their business and workforce strategies.  ... " 

Pandemic Holiday Retail Post Mortem

What Just Happened?  Good overview by a number of experts on retail process.  Join us in January for a recap of the craziest holiday season on record.  Wed., Jan. 27 – 12 noon ET/9 am PT

(Can't make it? Register and we'll alert you when the recording is available.)  Register Now:

All attendees will get a copy of the full research report. Join us live for the analysis, panel discussion and audience Q&A.

Amanda Nichols  Sr. Manager, Industry Marketing, UKG   Rob Snyder  COO & Co-founder, SYRG, Bob Phibbs  President/CEO,  The Retail Doctor, Moderating:  Al McClain  CEO, Co-founder, RetailWire

Grab a virtual seat for this holiday post-mortem of seasonal hiring, staffing and in-store safety, based on a just-completed pulse survey of retail managers conducted by UKG, with additional analysis from the workforce gurus at SYRG. You'll get reactions to the findings and advice as well in our panel discussion featuring Bob Phibbs, The Retail Doctor — insights that will help retailers streamline and optimize workforce operations in the year ahead. 

We’ll do a prior-year comparison to reveal:

How stores changed their approach to seasonal hiring and staff engagement;

How associate performance impacted store success;

To what extent COVID-19 had a direct impact on store operations (and staff morale), and;

Whether managers deemed their stores fully prepared for the holiday rush.   ... 

Automated Workflows are Rolling

Clearly, and this will continue.   But doing it right will still be a challenge.  The advantage will be to those that use the best approaches, applied to context with the right AI and analytics.  Think Autonomous is the future. 

Automated workflows are eating the world  By Tristan Pollock in VentureBeat

In just the past 12 months, over $2.2 billion has been funneled into tech companies building workflow automation solutions. This isn’t a bubble. It’s a balloon.

Hot technology companies like Slack ($14.5 billion market cap), Calendly ($60 million ARR), Docusign ($36 billion market cap), GitHub (bought by Microsoft for $7.5 billion), and Airtable ($185 million Series D), all have built-in workflow builders that range from simplified code to low code to graphical no-code. All it takes is a matter of minutes plugging X to Y to Z.

Just days ago, Adobe purchased Workfront, a marketing workflow automation platform, for $1.5 billion. Before that, Apple purchased a startup called Workflow and turned it into the Apple Shortcuts app. Workflow automation is the future.  .... ' 

AI Scanning Construction Site Spotting when things are Slipping

 I like the integration of relatively cheap cameras looking for key patterns in work progress (and potentially process) to determine missed schedules.  A classic use of AI in pattern recognition.  As suggested, also a key aspect of construction management .  Also could be linked to contractual timing and quality of work agreements.   Both in meeting those agreements, and looking at trends towards missing them.   As the article suggests, lots here.

AI that scans a construction site can spot when things are falling behind in TechnologyReview

Construction sites are vast jigsaws of people and parts that must be pieced together just so at just the right times. As projects get larger, mistakes and delays get more expensive. The consultancy Mckinsey estimates that on-site mismanagement costs the construction industry $1.6 trillion. But typically you might only have five managers overseeing construction of a building with 1500 rooms, says Roy Danon, founder and CEO of British-Israeli start-up Buildots: “There’s no way a human can control that amount of detail.”

Danon thinks that AI can help. Buildots is developing an image recognition system that monitors every detail of an ongoing construction project and flags up delays or errors automatically. It is already being used by two of the biggest building firms in Europe, including UK construction giant Wates in a handful of large residential builds. Construction is essentially a kind of manufacturing, says Danon. If high-tech factories now use AI to manage their processes, why not construction sites?  ....

AI is starting to change various aspects of construction, from design to self-driving diggers. But Buildots is the first to use AI as a kind of overall site inspector. 

The system uses a GoPro camera mounted on top of a hardhat. When managers tour a site once or twice a week, the camera on their head captures video footage of the whole project and uploads it to image recognition software, which compares the status of many thousands of objects on site—such as electrical sockets and bathroom fittings—to a digital replica of the building.  ...."