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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Facial Recognition, Literally?

Artificial Intelligence is transforming the way consumers purchase, the way business is conducted across industries, and the multi-billion dollar beauty industry is not getting left behind. In fact, there are a multitude of companies using AI to personalize and revolutionize skincare, haircare and everything in between.  Using AI to personalize the experience may prove to be one of the best ways to differentiate in a heavily saturated market.

Is AI the answer to not only changing how consumers interact with beauty product, but also the answer to future product development?

In a market as personal as cosmetics, specifically skincare, where everyone has unique concerns and desires, the ability to use Artificial Intelligence to personalize could be truly revolutionary. Is it possible that the key to ageless, flawless skin is Artificial Intelligence? The women behind PROVEN skincare believe that it is. PROVEN uses AI, aggregating data into the world’s largest skincare database, The Skin Genome Project, 8 million consumer reviews, 100,000 skincare products, 20,000 ingredients and 4,000 academic journals, to tailor skincare products to consumers’ specific needs. Consumers go to PROVEN’s website, take a three-minute skincare quiz and are provided with a skincare routine, taking the guesswork out of which products will deliver the best results.

“Using your answers to our Skintelligence QuizTM, our database instantly sifts through this incredible amount of information to select the best ingredients for your skin. This means you won’t waste time and money trying out products that may or may not work. And if your skin changes, your skincare can evolve right along with you.”

Proven

Taking the concept of Facial Recognition quite literally, FOREO, a tech-enabled skincare and oral care corporation, has recently launched a product that uses facial mapping and artificial intelligence to up your skincare routine. The Luna Fofo analyzes your skin and produces real-time readings that in turn customize your smart facial massage and cleansing routine. The Luna Fofo doesn’t do a one time analysis, rather it continuously analyzes your skin and constantly customizes your routine to your skincare concerns, even tracking your skin’s hydration levels. All of this is stored in a user friendly app so you can track your daily skin health with ease.

These are just a few consumer product-enhanced forms of AI that are transforming the beauty industry and the way consumers interact with AI daily. Beyond customer interaction, could AI enable advanced data capture and analysis to drive product development?  Manufacturing?  Sourcing?

Where is the limit of expansion for AI technology in business?


Challenge SolvetheUnsolvable to change your industry with AI.


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Enterprise Artificial Intelligence – Academic Theory or Ready for Primetime?

Solvetheunsolvable has already explored various aspects of consumer AI and products purporting to leverage AI technologies, but is AI for Enterprise ready for primetime?

Investors aren’t the only ones betting big on Artificial Intelligence, it turns out Higher Education is also investing heavily into the space. With heavy investment in research and development, enterprise level AI seems to be having a rocky start.  Earlier this month Northeastern University allocated $50 million to an Institute for Experiential Artificial Intelligence. This institute will be dedicated to uniting leading experts to solve the world’s unsolvable problems.

“This new institute, the first of its kind, will focus on enabling artificial intelligence and humans to collaborate interactively around solving problems in health, security, and sustainability. We believe that the true promise of AI lies not in its ability to replace humans, but to optimize what humans do best.”


Northeastern President Joseph E. Aoun

This isn’t Northeastern’s first step into the world of Artificial Intelligence and Automation. They already have an Institute for Experiential Robotics that is bringing together engineers, sociologists and other experts, including economists, to design and build robots with abilities to learn and execute human behaviors.  Northeastern isn’t just building Institutes for experts to conduct research, they are making it a priority to prepare their students for success in the age of artificial intelligence. They have an entire curriculum dedicated to what they call, humanics which is a key part of their strategic plan, Northeastern 2025.

Northeastern 2025 Promo Video

“We are building on substantial strengths across all colleges in the university,” said Carla Brodley, dean of the Khoury College of Computer Sciences. “Experiential AI is highly relevant to our mission.”

Though Northeastern is an example of one university betting heavily on AI, they are not alone in their quest to equip students with proper education for the AI-enabled future. In fact, government agencies are getting involved in funding AI in Education. The UK has pledged to invest £400 million in math, digital and technical education through the government’s AI sector deal to protect Britain’s technology sector amid Brexit and an additional £13 million for postgraduate education on AI. In the US, just a few days ago, the National Science Foundation announced a joint federal program to fund research focused on artificial intelligence at colleges, universities and nonprofit or nonacademic organizations focused on educational or research activities. 

The National Science Foundation is awarding $120 million to fund planning grants and support up to six institutes, but there’s a catch. Each institute must have a principal focus on at least one of six themes:

  • Trustworthy AI
  • Foundations of Machine Learning
  • AI-Driven Innovation in Agriculture and the Food System
  • AI-Augmented Learning
  • AI for Accelerating Molecular Synthesis and Manufacturing
  • AI for Discovery in Physics

As universities and governments bet big on the future of AI and education, it underscores the importance of AI on a global scale in the future, but does it call into question the current existence of AI solutions ready to take business to the next level? Utilizing AI and automation will be imperative for corporations to remain competitive and for the advancement of business, but when will the floodgates be swept open, and by who, remains a mystery.

Will you leap into the future and embrace AI now? How do you see the futuristic vision of enterprise AI transform your business? Challenge Solvetheunsolvable with your business conundrum or leave your thoughts in the comments below and let’s explore what AI can do for you. 


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Artificial Intelligence: The Future of Healthcare

What’s more important to parents than the health and well being of their children? Not much, if anything. So imagine this… your child could have a disease that an Artificial Intelligence enabled iPhone app would diagnose more than a year before doctors and all you have to do to find out is take some photos. Sound too good to be true? With the power of Artificial Intelligence, alternative health care tests, such as this one for children, could literally be in your hands right now…

A post from Government Technology explains:

“CRADLE makes use of artificial intelligence to look at baby pictures taken with a flash and pick out instances of white eye. When tested on 50,000 images of 40 children, half of whom had been diagnosed with an eye disease, the technology was able to identify white eye in pictures taken up to 1.3 years before the child was diagnosed.”

This non-intrusive, ultra- convenient Artificial Intelligence enabled technology is pushing the boundaries of digital health diagnosis, and this is just one example. In addition to convenience, your data privacy is less at risk as the app analyses the photos right on your device, reducing latency and increasing privacy by not uploading to a server. This type of technology, when developed further, could be very useful to detect a variety of health issues.

In fact, there are other apps that are already utilizing AI to address a host of mental and physical health concerns. Some of these health applications range from AI chat bots programmed to treat mental health all the way to AI-enabled apps that can help women track and predict fertility. Investors are betting big on these AI-enabled health apps with millions in funding being pumped into these companies of the future.

The
future of AI-enabled apps is certainly bright. Wearable tech, like FitBit or
Apple Watch, when equipped with built-in Artificial Intelligence, could one day
utilize personal health data to identify diseases such as cancer in the very
early stages. Apple Watch
is already able to use AI
to identify
irregular heart beats known as paroxysmal atrial fibrillation (Afib) with 97%
accuracy.

Image result for healthcare cost rising

With the continuous rise of healthcare costs, for individuals and the country as a whole, consumers are looking towards AI for more affordable self-diagnostics. This is evident in more than AI health apps, but also in many at home health kits such as, EverlyWell and QuestDirect, stay tuned for our Data Privacy Series article on at home diagnostics coming soon.

Artificial Intelligence is improving the healthcare space at rapid speed and the technology is only getting better. So the question that must be posed is:


Could AI be the key to solving the $760 billion healthcare crisis?


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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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Data Privacy Series

When’s the last time you read the terms and conditions before clicking “accept” as you downloaded the hottest new app to your smartphone? Do you really know what companies are doing with your data from your search history, the pictures on your phone, or even your personal health records?

How our data,
personal details and digital patterns, is being used by the scores of apps,
programs, and devices we interact with on a daily basis remains a mystery. When
we click “Accept” on the terms and conditions page, usually in a
hurry, we are blindly 
“choosing” to opt in to whatever data collection and privacy
infringements the developer has built into the technology. What’s more, most
companies use vague statements on their websites regarding what they are doing
with your data and even threaten to impact your service if you decide not to
share your data.

For example, this snippet comes directly from Nest’s FAQ’s:

Nest FAQs

How important is that app or device?  Is it worth signing over your digital rights?


Join the
SolvetheUnsolvable team this month to explore how private your data really is…

Facial Recognition,
friend or foe? Family Heritage Mapping, a key to the past or losing control of
your future?… Who else is checking in on grandma? The hidden dangers in Smart
Home technology. Are you using your cell phone, or is it using you?

Check back in on
Wednesdays this October to learn more about data privacy…

Up next: Data Privacy Part Four


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Data Bias on the Daily: Is AI hindering her job search?

The gender pay gap and women’s representation in leadership roles continues to captivate headlines but what action is really being taken… It’s time to take a journey through the application process for a young adult female, let’s call her Mira, looking to land an interview in a Science, Technology, Engineering or Math (STEM) focused corporation…

A large part of Mira’s job search is online, where she will turn to various social platforms to seek out new opportunities, and depending on the channel she picks, she will be shown job advertisements that ultimately will be based on biased pay per click (PPC) and purchasing algorithms.

Advertising a STEM job across various social networks reached 20% fewer females than males, even though it is illegal to gender bias in both the US and Europe.

Management Science Study

Since advertising algorithms are designed to optimise advertising spend, and maximise reach, less potential female candidates were exposed to the ad and therefore did not get the opportunity to apply. To put it simply, the algorithms are biased against female candidates due to the higher cost of reaching them, even if ultimately the female candidate would be a better hire and take less resources to train.

Despite all of these challenges and biases facing her, Mira chugs along and finds a job posting in her field that captures her attention. Upon reading further into the job and the company, she is both consciously and unconsciously influenced by the language used to describe the role which will determine her next step. The STEM industry, particularly, is imbalanced due to a lack of women being trained and therefore is an industry that remains largely male-dominated. This transfers into the biased language used in the industries’ job listings, which in turn biases the data the job board algorithms are trained on.

A University of Waterloo and Duke University study showed that male-dominated industries (STEM industries) use masculine-themed words (like “leader”, “competitive” and “dominant”) which when interpreted by female applicants deteriorated their perception of the “job’s appeal” and their level of “belongingness” even if they felt able to perform the job. Above this, it is proven that a female will only apply for a job if she fulfills 100% of the criteria, whereas males will apply if they feel they fulfill only 60%.

Determined as ever, Mira eventually finds a job description and company she feels confident about, she submits an application. Her CV and cover letter are parsed and ranked alongside other applicants, male and female. Each success factor identified within the words Mira has used, like her programming skills, is weighted according to what has been historically successful in the past for that particular company.

In the age of Artificial Intelligence, past hiring decisions are used to train algorithms to identify the best suited candidates for the company. The issue with biased data is that even if gender is excluded from the application itself, the gender distribution of the training data may be strongly imbalanced since the industry has been historically male-dominated. This means that even if Mira gets to the point where the hiring company or job board puts her resume through their fit algorithm, she still may not receive the interview based on the inherent bias in the program.


If you’re not convinced by Mira’s journey, here’s a real life example: Amazon’s experimental hiring tool was used to screen and rate resumes based on historical hiring patterns. To date, a majority of Amazon employees were males and inevitably the system taught itself to penalise female resumes, with far greater efficiency than a human.


Still unsure if data bias is
perpetuating gender bias in STEM? Check out these articles from others in the
industry:

How Unconscious Bias Holds Women Back

Mitigating Gender Bias

AI Is Demonstrating Gender Bias


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Chatbots, Influencer Bots, Gaming Bots, Oh my!

As it becomes
increasingly difficult to get the attention of executives and consumers through
email and advertisements, many companies are turning to Artificial
Intelligence. But the real question remains, what are the ethics behind
corporations using bots to promote their goods and services?

Chatbots have shown
real ROI to many businesses and at times have proven more effective than an
under-trained salesforce. An
Oracle survey
has shown that Chatbots could save $174 Billion across
Insurance, Financial Services, Sales, and Customer Service. With vast
abilities, including the ability to chat with actual human dialogue it’s no
wonder companies are so eager and quick to turn to this advancement in AI.
Should a company be ethically responsible to share with consumers that they are
chatting with a bot? Is this on the consumer to “beware” of bots? Do
we as a society care? Should we care?

Influencer Bots, like LilMiquela and Shudu, use CGI and are run by companies specializing in Artificial Intelligence to blend the lines between reality and robot. These two influencer bots have amassed almost 2 million followers and “LilMiquela” is touted as “Musician, change-seeker, and robot with the drip💧💖” in her Instagram Bio. These bots are influencing the purchases we make, the culture we enjoy and now even the music industry. Lil Miquela’s profile is at least honest that she is a bot but Shudu’s bio vaguely states: “The World’s First Digital Supermodel.”


In a world where social media is influencing so many consumer purchasing decisions, especially in younger demographics, is it even ethical to create an influencer bot?


Gaming bots, when
used as intended, can often enhance the gaming experience. For example,
Fortnite has upped their bots’ ability in order to maintain an enjoyable
experience for new comers in their vast online community. However, most gamers
know that bots have often been abused since the idea of using an algorithm to
replace a human online became a reality. Many have used the bot technology to
their advantage and there are companies out there doing something about it.
Niantic, creator of one of the most popular cell phone apps ever, Pokemon Go,
has been extremely strict on bots and gamers trying to cheat the system, going
so far as to take
legal action
.

With bots becoming
one of the most common Artificial Intelligence interacted with daily we need to
start questioning the ethical implications. How are AI empowered bots improving
our daily lives? What are the implications of influencer bots like Lil Miquela
and Shudu on the future of our society? How aware are you that Artificial
Intelligence is impacting your purchasing decisions?


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Data Bias on the Daily: Criminal Sentencing- Not all algorithms are created equal

Imagine This…. You’ve been convicted of a non-violent crime, say petty theft. Your legal team decides the best course of action is to take a plea deal. On the day of your sentencing, the judge rejects your plea deal and doubles your sentence. Why? An algorithm says that you are at high risk for violent crime in the future…

You may be reading
this thinking, that can’t possibly be real? But that is an all too real
scenario because of the COMPAS algorithm.


COMPAS, an acronym for Correctional Offender Management Profiling for Alternative Sanctions, is a case management and decision support tool used by U.S. courts to assess the likelihood of a defendant becoming a repeat offender.


The problem with COMPAS, as a ProPublica report states, “Only 20 percent of the people predicted to commit violent crimes actually went on to do so.” ProPublica also concluded that the algorithm was twice as likely to falsely flag black defendants as future criminals as it was to falsely flag white defendants. And therein lies the problem, the algorithm has inherently biased training data due to years of human bias in the courtroom.

COMPAS is not only
biased racially, but it also has bias against age and gender. An independent
study done by researchers at Cornell University and Microsoft found that
because most of the training data for COMPAS was based on male offenders the
model is not as good at distinguishing between male and female as it could be.
They even decided to make a separate COMPAS model aimed specifically at
recidivism risk prediction for women.

But why would COMPAS
separate the data based solely on gender when COMPAS has also shown to have
racial bias? Why are judicial systems still turning to private, for-profit,
companies whose algorithms are known to support racial, age and gender bias?

Turning to these
types of algorithms have long standing implications on human life and our
judicial system. Criminals receiving their sentences in the early ages of
algorithmic adoption should not be test samples or guinea pigs for faulty and
biased algorithms. As Artificial Intelligence becomes more main stream,
understanding the data sets and training methodologies is key to understanding
the results – how is data bias affecting your daily life?


For more information
on COMPAS and ProPublica’s report, please click
here
.

Up next: Data Privacy Part Four

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