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AI & COVID-19

Throughout modern history, several infectious diseases have spread with sufficient speed, scale and severity to be considered pandemics. Malaria, Smallpox, H1N1, and HIV/AIDS, which remains active, made the list. COVID-19, however, is in a league of its own. The last pandemic of comparable economic impact, if not on human life, was the Spanish Flu, which directly followed WWI and wrought havoc in a significantly less interconnected world than the one in which we live today.

The Global Impacts of COVID-19 have already been unprecedented. “Stay-at-home,” “social-distancing” and travel and trade restrictions imposed to protect public health have had devastating impacts on the global economy, supply chain and employment. How and when this pandemic will end remains unclear. What is certain is that businesses now face an existential threat, and their leaders must take immediate action to set and execute strategies to weather the storm and ensure they are prepared for future economic, operational or human crisis.

In a time when “non-essential” businesses have been forced to shutter operations or shift to remote collaboration, and “essential” businesses face unparalleled demand alongside novel operational and supply chain challenges, how can manufacturers, O&G companies, airports, utilities, universities and other major businesses survive?

If they adapt quickly, can they thrive?

The answer to this question for industry lies in the adoption of end-end Artificial Intelligence.

Over the next few weeks, we will dive into the major business impacts of COVID-19 and how Elutions‘ Artificial Intelligence platform, Maestro, is addressing them across multiple verticals.  We strongly believe that businesses that successfully and swiftly adopt automated, autonomous applications of AI and rethink their business models will be the ones realizing a competitive advantage once we return to a “new normal”.

Inspired by industrial trends a multitude of sources, reports by McKinsey and many others, Elutions’ AI & COVID-19 Series will specifically address the themes of Survival, Optimization, and Transformation. 

Survival: Artificial Intelligence applied immediately to stabilize business operations.

Optimization: Artificial Intelligence’s intermediary impacts, growing and preparing for a “new normal.”

Transformation: Artificial Intelligence’s long-term impacts in a post-pandemic landscape.

Check back in with us weekly for new content on how to address the business impacts of COVID-19 through the strategic application of end-end Artificial Intelligence.

Up Next for NCW: Digitization and Chemical Manufacturing

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Why In-House Artificial Intelligence Projects Fail

Companies all over the world, from giant corporations to start-ups, are keen to cash in on the vast value of Artificial Intelligence. With an intent to capture as much of that value as possible, many spend millions on in-house AI solution development rather than outsourcing to address their most critical business challenges. In a world where there’s a feasible DIY solution for almost everything, Artificial Intelligence is most often the outlier. The complexity and cost of AI solution development demands experience to reduce financial risk and ensure speed to benefit, especially in highly competitive sectors.

Think of your business as a human body, and your business challenges as illnesses of varying severity. Some challenges, like some illnesses, are treatable with over the counter medications, while others require a visit to the doctor, prescriptions, long-term treatment or intensive care. If you had an illness that required extensive medical attention, you wouldn’t hesitate to seek out the best medical team for treatment. Should you treat your business any differently?

In deciding whether to go in-house or outsource, it is important to consider how a strategic AI implementation will impact your business. If it’s done right, it will reduce costs, increase revenue and enhance competitive advantage. If it’s done wrong, to what extent is your business at risk?

Financial opportunity from AI abounds across sectors (see the figure below), and there is both margin opportunity and market share on the table for the businesses that harness AI to tackle their strategic challenges first. In other words, getting your AI implementation done fast and right matters, and you must weigh your decision to go in-house or outsource accordingly.

Artificial intelligence (Al) has the potential to create value across sectors. Al impact, $ billion 700 500 400 Healthcare systems and services Public and social sectors 300 Advanced electronics/ semiconductors Retail Transport and logistics Travel Consumer packaged goods Automotive and assembly Banking Basic materials Insurance Media and entertainment High tech O Oil and gas 100 20 Telecommunications Pharmaceuticals and medical products 30 Chemicals Agriculture Aerospace and defense 40 50 60 McKinsey&Company Share of Al impact in total impact derived from analytics, % Source: McKinsey Global Institute analysis

With competitive advantage on the line and the clock ticking, corporations place their bets on whether to navigate the AI journey alone or with partners. Instinctively, they are hesitant to collaborate, tantalized by the prospect of minimizing solution costs, while building their own innovative capacity and owning the resulting IP outright.

Logically, they then look to market outcomes and learn why in-house AI solution development efforts fail more often than not, even in the Fortune100 and at tech companies.

Without experience developing and delivering AI solutions, many corporations fail understand the costs, resources, processes, stakeholders, and even the objectives involved from the onset. As a result, in-house projects often lack a clear and viable design and delivery strategy, roadmap and KPIs, dramatically reducing the speed to benefit if not inhibiting benefit delivery altogether. Program costs and timelines become a driving force for failure. With little transparency into which aspects of the solution will drive the most value, there is no clear way to prioritize spending. Costs either spiral out of control, or corners are consecutively cut in design, development, testing and delivery, resulting in piecemeal solutions that impair data quality, promote bias and diminish solution accuracy, functionality, utility and outcomes.

More often than not, successful AI adopters partner with proven providers on a combination of off-the-shelf solution tailoring, ground-up solution design and solution delivery. How do they decide to partner rather than go it alone?

First, they recognize the competitive imperative for AI—the opportunity cost of following rather than leading in their market—along with the direct costs of failure, and their lack of in-house knowledge and experience with AI solution design and delivery. Second, they find a provider with a successful track record in similar or analogous environments. Third, they develop trust with that provider by laying the groundwork for a happy marriage in contracting. Then they see it through. From the leadership level-down, they commit to the partnership and collaborate from end-to-end to ensure project success.

Strategic AI implementations are broad in scope, capturing data and impacting activities across corporate ecosystems. This complexity is readily apparent in industry, where AI not only provides data-driven direction for decision-making at every step of the value chain and in every organizational department, but directly informs control and automation strategies in production, testing, packaging, distribution and even purchasing.

In recognition of the immense value and complexity of AI in industry, and the competitive need for speed in adoption, the World Economic Forum, in collaboration with McKinsey & Company, has published a toolkit of “practical recommendations” for industrials to accelerate their AI journey at scale. Appearing in The Next Economic Growth Engine Scaling Fourth Industrial Revolution Technologies in Production, this toolkit advocates the adoption of proven AI solutions and related technologies through a partnership and acquisition approach rather than in-house development.

Figure 6: Industry toolkit for accelerating adoption of technology Value delivery engine Intelligence • Predictive maintenance • Machine learning-supported, root-cause problem-solving for quality claims Connectivity Augmented reality-guided assembly operations Real-time IOT-based performance management Flexible automation • Robots to automate challenging tasks • Real-time product release 39 high-impact digital applications ready for deployment Scale-up engine Mobilize Mobilize the Organization Strategize Set the vision and the value to capture Innovate Spark innovation by demonstrating the value at stake Scale up Capture full value & Company, Fourth in With the World

With more and more data available for exploit across industries, the opportunities for its monetization through AI are greater and increasingly complex. So too is the risk of getting your implementation wrong.

Can your business afford the DIY approach?

Challenge us to solve your unsolvable business quandaries.

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Robotics: Automation or Artificial Intelligence?

The rise of robotics – a long touted seismic shift in human existence, the day an inanimate creature is brought to life.  A scary reality in the minds of many conspiracy theorists, and a reality many tech leaders would have us believe is already upon us.  But how close are we to engineering a robotic race?

“Who controls the past controls the future. Who controls the present controls the past.”

― George Orwell, 1984

It’s difficult to not think about physical robots tackling common human tasks when we see the word robotics, but now robotics refers to a much larger application of technology and rising industry. Robotics refers to a focus on creating efficiency and replicating mundane tasks, a world that exists beyond purely physical robots, giving rise to automation bots.

Robotic Process Automation (RPA) is an example of an automation bot operating in a digital world. You may be thinking, obviously it’s automation, it’s even in the name, but what is RPA? Used to perform simple, repetitive tasks, such as data entry, RPA is a programmable “bot” that automates a process in order to free up more time for humans that would otherwise be doing these mundane tasks. RPA cannot be considered Artificial Intelligence as it does not have the ability to understand the implications of the tasks it is performing, or predict future scenarios arising from the performance of these tasks.

Amazon Scout

In contrast, Amazon’s Scout is out on the streets in California, a physical robot, making package deliveries.  This Scout robot may be physical and operate in the real world but similarly to RPA it is another example of automation, lacking human intuition. Just like RPA this Scout robot is programmed to deliver a package straight to your door, removing this repetitive task, lessening the burden on man and machine, but the bot is not capable of modifying the delivery location to the back door under the overhang when rain is predicted, unless the delivery instructions are explicitly programmed to do so.  Far from artificial intelligence, the Scout is simply a machine programmed to automate a repetitive human function.

While individuals commonly mistake robotics as artificial intelligence, it’s important to understand why RPA and delivery robots are not examples of true artificial intelligence. Are they intelligent bots? Maybe. They certainly process and execute complicated instructions and factor many variables, but they lack inherent cognitive function.  Humans are constantly concerned about the demise of humanity as robots are brought to life, but because artificial intelligence still lacks the ability to replicate common sense, the rise of the robotic race will still remain in the halls of science fiction.

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Solved: Dismantling the Silo

We are living in a world that is obsessed with connectivity yet so many large corporations are still working in silos. How can a corporation become connected and move towards Industry 4.0 if their data and systems are trapped in silos? This is a problem many corporations, big and small, face today. In order to understand and address the problem, first we must understand the silo. There is a solution, there is a path forward and Artificial Intelligence can lead the way.

Silos are created when information, goals, tools, priorities and processes are not shared with other departments and this pitfall becomes enforced by corporate culture. An effort to achieve the lowest overall cost and best functionality for different departments, or in the case of some manufacturers the same department in different plants, has created disparate data. Many systems are programmed to not function well together or only function in a stack but there is technology out there dedicated to dismantling the silo.

Executives often get into the trap of thinking the only way to advance into Industry 4.0 is to update disparate systems and increase capital expenditures. What many execs and people in all corporate functions do not yet understand is that Artificial Intelligence (AI) can be the systems connector. The entire premise of AI is built on the notion of interconnected information that may have previously been thought to be unconnected entirely. It is entirely unnecessary for a corporation to increase CAPEX when working with the right AI provider.

In order to get the most accurate picture of underlying issues within the corporation, AI must be able to connect to a vast amount of data from many different silos. This doesn’t mean that you have to dismantle the silos, you just need the right AI connector…. Let Artificial Intelligence be your Silo Dismantling Agent.

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Data Bias on the Daily: What’s in your Amazon Cart?

With multiple billions of packages shipped per year, and even more billions of items purchased, it’s no wonder that Amazon is a household name. In the time it takes you to read this article, an estimated 100,000 items will be purchased on Amazon. But how many of those purchases will be impacted by data bias? It’s likely every single one.

Have you ever stopped to think about the algorithms behind those convenient home page suggestions, people also purchased, and related to items you viewed? Maybe in passing or idly but likely not in detail. In this post we will be exploring just how much one quick search on Amazon can be littered with bias.

Bias can end up in your shopping cart in many different stages along the way. It’s safe to assume that Amazon wants to maximize it’s bottom line, even though they claim they want to provide the consumer with a great deal. When the engineers set to writing shopping algorithms at Amazon corporate, they have to add parameters that are computable and achieve a certain goal. With this in mind, they are likely adding the bias that they would like to increase Amazon’s bottom line (aka, profitability of your purchase). Let’s apply this to an example search.

Perhaps you’ve been searching for storage solutions for your messy closet. The moment your results appear for “closet storage” you are encountering data bias. Exhibit A, the screenshot below:

You’re looking at the first instance of bias because a majority of the page is showing you “sponsored” results. “Funding Bias” is written into many algorithms all over the internet and is the reason why sponsored items always show up first. These brands: JYYG, TYPE A, OUMYJIA and JEROAL are paying to bias your search. This is one of, if not the most common search bias encountered but we see so much “sponsored” content that, at this point, we may not even think of the bias’s effects.

Regulations have been placed on funding bias, note the “Sponsored” tag and other evidence for example on social media platforms when influencers post with “#ad.” These indicators are set up to clue in the consumer that their purchasing decisions are being biased by funded posts, something relatively new the last several years. Corporations spent years biasing search history towards paid posts without having to let the consumer know, this is a huge stride against hidden data bias. There’s nothing wrong with “pay to play!” As a consumer, this can be a helpful way to find one of your new favorite brands, products or technology solutions but people now believe it’s important for the consumer to know when funding is biasing their purchase.

Another example of bias in this search are the items marked with the “best seller” tag. Under “Help & Customer Service” Amazon.com says: “The Amazon Best Sellers calculation is based on Amazon.com sales and is updated hourly to reflect recent and historical sales of every item sold on Amazon.com.” There is a litany of data bias that could be going on here. Was the “Simple Houseware” organizer at one point a “sponsored” post which led to it becoming a “best seller?” A quick google search shows that Simple Houseware only sells on Amazon, this could be another contributing factor to the status as a “best seller.” We simply do not know the parameters the calculation is setting for “sales” as that is quite a broad term.

To play devil’s advocate, the “sponsored”  results could be amazing products that you will purchase, love and even re-order in the future. The data bias written into the “Best Sellers” calculation could absolutely be favoring great products you will know and love. However, it’s critical that we don’t turn a blind eye to the bias and we continue to scroll past the “sponsored” and “best sellers” to get a full picture for purchasing.

The objective of this article isn’t to get you to stop buying from Amazon but rather to consider the data bias right in front of you. If there is this much data bias in one simple Amazon search, how many more things in your life are impacted by data bias? How much is your business and its bottom line impacted by data bias? Algorithms, AI and their inherent bias are a part of daily life. How will you use them to create change for good?

Up Next for NCW: Digitization and Chemical Manufacturing

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Data Bias on the Daily

Our upcoming September Blog series is Data Bias on the Daily: How Data Bias in Artificial Intelligence is Impacting You. This series will focus on data bias in various forms encountered daily and the goal is to educate consumers on how bias can enter algorithms, knowingly and unknowingly. It is important to define bias:

Bias: The systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results.

How to Identify Statistical Bias

Bias in Artificial Intelligence and algorithms is sometimes intentional, can be caused by a number of things including, but not limited to, sample selection or data collection, and can be avoidable, if that is the desired outcome. (Many corporations want to write their algorithms with bias, in order to increase their bottom line.)

Maybe you’re a woman searching for a STEM job, but the data is biased against your application? Perhaps, your Amazon search is biased towards products and brands that will only increase Amazon’s bottom line? Data bias is even entering our judicial system, is it possible that algorithmic “advancement” is simply confirming long standing racial bias?

Stay tuned to learn more about how Data Bias impacts our daily lives by checking in with us on Mondays in September (9/16, 9/23 and 9/30).

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