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Leading the Intelligent
Enterprise- The Strategic and Structured Approach To Digital Transformation
To prepare for the next phase of AI, leaders must prioritize
assembling the right talent pipeline and technology infrastructure.
Artificial intelligence (AI) and machine learning offer new ways to boost productivity, develop
talent, and drive organizational change by enhancing managers’ ability to make the right calls
in complex situations.
Augmented intelligence tools have already made an impact for many companies, but the next
revolution will happen when every aspect of a business, from top to bottom, is designed with AI in
mind. Call this new construct an intelligent enterprise. Like other major revolutions in management, it’s
poised to transform industries and organizations for decades to come. To prepare for this next phase,
leaders will need to harness machine intelligence for decision-making across the business, assemble the
right talent, and recognize the benefits and limitations of AI to shape organizational strategy.
Understanding the AI Advantage
It’s not hard to find examples of the amazing things
we can do with artificial intelligence. AI and analytics have changed the centuries-old techniques
of plant breeding, helped advance cutting-edge research into the disease, and even been used to decipher
damaged ancient Greek tablets.
What these achievements have in common is that
they are discrete, structured tasks. In each example, algorithms are used to absorb available data, recognize
patterns therein, simulate outcomes, and select moves
or produce results based on the statistical likelihood of
success. In-plant breeding, for example, the simple step of designing a trial to see whether
your breeding effort has succeeded or failed requires
choosing from a set of 1.16 x
1012 possible combinations.
Yet, increasing efficiency in
this highly complex process
through data analytics can save
millions of dollars.1
If improving one aspect
of one process through data
analytics can have a massive payoff, imagine what
can happen when an organization takes advantage
of AI’s ability to learn, analyze, and optimize across
all processes and business functions.
How AI Can Accelerate Leadership
Businesses, particularly large corporations with a
global footprint, are complex adaptive systems. No
one person, or even one group of managers, can
know what’s going on at all levels of an organization consisting of thousands of employees. Even so,
the CEO is responsible for keeping the board and
shareholders happy, positioning the company for
the future, maintaining employee morale, and developing an advantage over the competition — all
while turning a profit. Although the CEO relies on
an executive team for support across these different
functions, he or she ultimately shoulders the blame
for bad choices. No wonder most CEOs at large-cap
companies don’t last more than five years.2
With so much responsibility, the CEO’s scarcest
resource becomes time, and that’s where AI brings
the most value to the top job. AI is an ideal tool for
observing and gathering the available information
touching on business operations. This includes
internal reporting data as well as relevant external
news stories and analyses relevant to the industry,
digested and categorized by natural language processing algorithms. The Reuters news service, for
example, uses AI to sift through 700 million daily
tweets to spot breaking news that can be handed to a
journalist for further investigation.3
The intelligent enterprise must similarly process
a mountain of data, prioritizing items according to
relevance, which helps to avoid information overload for the leaders reviewing the reports. This gives
the CEO maximum awareness of what’s happening
throughout the business and the industry so that more of his or her time
can be spent addressing
issues likely to have an impact on the bottom line.
Moreover, the intelligent enterprise imagines
AI systems in every division, department, unit,
and group in the organization — human resources,
IT, marketing, finance, operations, and so on — so that each of these operations
can be optimized with augmented intelligence systems
that provide decision support to human employees.
Many HR departments already use a simple
form of textual analytics — keyword scoring — to
sort through unwieldy stacks of résumés that accumulate whenever a new job is posted. Applications
for an accountant position that don’t mention, for
example, the required academic credential or license
can be tossed out right away. NASA’s AI system performs a deeper analysis that evaluates the context in
which the keywords are used.4
In the intelligent enterprise, more-advanced
expert systems would use cognitive engines to understand the applications. Moreover, they would not focus narrowly on making the HR manager’s life
easier. Each corporate unit’s and division’s systems
would exchange information automatically, so the
HR system would know when new talent might be
needed. It could review past applications and have
potential candidates lined up for consideration as
soon as any new hiring was approved. In this way,
the system would become a key part in advancing
the CEO’s goals by ensuring that the company had
the talent it needs to execute the overall mission.
The interconnection between business divisions would also give the CEO a real-time look
into company performance. Data from each business unit wouldn’t be filtered by preconceived ideas
about what the numbers ought to look like or shaded
by department heads putting the best face on the results. The numbers would speak for themselves.
With a clear view of what’s happening, the CEO
could swiftly reorient the company, as needed, to
remedy problems or take advantage of favorable
conditions. Armed with solid information and options weighed by AI simulations, the CEO could
formulate multiple potential strategies to deal with
the situations that arise. Instead of being based on
hunches, emotions, or guesswork, these strategies
would be fully informed by the best available data.
Know AI’s Limits
While innovation in AI systems continues to rapidly
evolve, it’s not all-knowing — in fact, artificial general intelligence exists only in science fiction. For
now, it still falls to the human CEO and executive
team to pick the strategy and execute it. But machines and AI systems are incredibly valuable for
presenting data and providing options for leaders tto
consider based on different real-world contexts and
goals. For example, sometimes the CEO will want
to take a long-shot risk. Or perhaps it’s important to
spend money on an initiative that won’t hit certain
strategic targets but will improve employee morale.
Reality is far too complex for a statistical algorithm
to imagine every possibility that leaders might take
into consideration.
Experienced CEOs are needed to consider the
intangible factors a machine will miss. While the
CEO’s primary job is making decisions, the role
doesn’t end once a choice has been made. Here, AI
tools are essential for monitoring results and evaluating whether the strategy is producing the intended
effect. When bad choices are made, it’s important to
change course quickly. The continuous cycle of acting and reviewing results is critical for updating or
abandoning strategies when necessary to achieve
the organization’s goals.
Constant reevaluation of the company’s direction, in matters big and small, may seem like a
waste of time, but it’s an effective insurance policy against complacency. Adaptability allows a business to stay ahead of customer and market needs and avoid becoming the next BlackBerry, Blockbuster, or Borders.
What AI does is enforce discipline on corporate strategies. It continuously, and automatically,
evaluates questions like, “Is the plan working?” or
“How are the forecasts and projections?” It plots out
alternatives — what happens if the company pivots
in this direction or that direction? The intelligent
enterprise also provides clarity about the goals and
objectives of the organization, aligning every business division toward the overall strategy by setting
goals (for instance, by having the talent on hand to
accomplish the next mission) and tracking progress
toward those goals and the end results.
Sometimes overall change is needed, and
sometimes it’s not. The intelligent enterprise is a
system designed to be ready for either possibility.
In a complex market environment, success comes
to the companies best able to adapt to fast-changing
circumstances. By building adaptability into the
structure of the company, AI helps the CEO manage
challenges as varied as the disruptions of a global
pandemic or the discovery of new technologies.
Companies are investing in AI today, but to
achieve the ultimate strategic goals of this investment, organizations must broaden their sights beyond creating augmented intelligence tools for
limited tasks. In order to turn this broader vision
into reality, leaders must prioritize assembling the
right talent pipeline and technology infrastructure
to enable the intelligent enterprise of tomorrow.
The Building Blocks
of an AI Strategy
Organizations need to transition from opportunistic and tactical
AI decision-making to more strategic orientation.
As the popularity of artificial
intelligence waxes and wanes,
it feels like we are at a peak.
Hardly a day goes by without an organization announcing “a pivot
toward AI” or an aspiration to “become
AI-driven.” Banks and fintech are using
facial recognition to support know-your customer guidelines; marketing companies
are deploying unsupervised learning to
capture new consumer insights, and retailers are experimenting with AI-fueled
sentiment analysis, natural language processing, and gamification.
A close examination of the activities
undertaken by these organizations reveals
that AI is mainly being used for tactical
rather than strategic purposes — in fact,
finding a cohesive long-term AI strategic
vision is rare. Even in well-funded companies, AI capabilities are mostly siloed or
unevenly distributed.
Organizations need to transition from opportunistic and tactical AI
decision-making to a more strategic orientation. We propose an AI strategy built
upon three pillars.
1. AI needs a robust and reliable
technology infrastructure.
Given AI's popularity, it is easy to forget that it is not
a self-contained technology. Without
the support of well-functioning data and
infrastructure, it is useless. Stripped of the
marketing hype, artificial intelligence is little more than an amalgamation of mathematical,
statistical, and computer science techniques that rely
heavily on a stable infrastructure and usable data.
This infrastructure must include support for
the entire data value chain — from data capture to
cleaning, storage, governance, security, analysis, and
dissemination of results — all in close to real-time.
It is not surprising, then, that the AI infrastructure
the market is expected to grow from $14.6 billion in
2019 to $50.6 billion by 2025.
A good infrastructure allows for the establishment of feedback loops, whereby successes and
failures can be quickly flagged, analyzed, and acted
upon. For instance, when Ticketmaster wanted
to tackle the growing problem of opportunists —
people who buy event tickets ahead of genuine
customers, only to resell them at a premium — it
turned to machine learning algorithms. The company created a system that incorporated real-time
ticket sales data along with a holistic view of the buyer
activity to reward legitimate customers with a
smoother process and block out resellers. As the
the company soon realized, resellers adapted their
strategies and tools in response to the new system.
Ticketmaster then modified its infrastructure to
include feedback loops, allowing its algorithms to
keep up with the resellers’ evolving techniques.
2. New business models will bring the largest AI benefits.
AI has the potential to offer
new sources of revenue and profit, either through
massive improvements over the current way of
doing things or by enabling new processes that were
not previously possible. But incremental thinking
about how AI can be used will most likely lead to
modest results. Significant benefits are unlikely to
be achieved without a new business model mindset,
or a so-called intelligence transformation.
AI allows for improvements that far surpass
human capabilities. For example, OrangeShark, a
Singapore-based digital marketing startup uses
machine learning for programmatic advertising,
thus automating the process of media selection, ad
placement, click-through monitoring, and conversions, and even minor ad copy changes. Because of
the efficiency offered by its system, OrangeShark is
able to offer a pay-for-performance business model,
whereby clients only pay a percentage of the difference between customer acquisition costs from a
standard advertising model and the OrangeShark model. By completely automating a previously
the semi-automated task, the company has created a
the new business model that makes monetization of
massive efficiency gains possible.
At the other end of the spectrum, Affectiva,
which calls itself an “emotion measurement” company, houses the world’s largest image database of
sentiment-analyzed human faces. The company
analyzes and classifies a range of human emotions
using deep learning models that can then be made
available to clients. Some applications study emotional responses to ad campaigns, while others help
people relearn emotional responses after a stroke.
Affectiva has built a business model based on providing intelligence as a service in an area where
the nonhuman intervention was previously impractical.
These examples merely scratch the surface of
possible AI-enabled business models. We will soon
have smart cameras that facilitate franchising contracts and employee compensation schemes. Machine
learning on granular data will allow for the customization
of products and services across time. As these and
similar developments open up new sources of revenue
and profit, new business models should therefore be
considered as a foundation of any AI strategy.
3. AI without ethics is a recipe for disaster.
The final AI strategy pillar is ethics, which is not
necessarily a common component of technology
strategy. However, the use of AI raises many
potentially thorny ethical issues, such as incorrect
insights and inherent biases due to poorly constructed
algorithms, and an upswing in unemployment due to
the substitution of human labor with machine output.
Take, for example, facial recognition, one of
the most common AI use cases today. While the
technology has proved to be effective in a number
of areas, such as catching criminals, finding missing people, and even monitoring blood pressure, it
also raises a number of ethical concerns, such as the
right to avoid surveillance and the accuracy of the
algorithms used to identify individuals and groups.
For example, most AI systems are better at accurately identifying people who are white than people
of other ethnicities, and at identifying men’s faces
rather than women’s; indeed, some systems misidentify gender in up to 35% of darker-skinned females.
In December 2018, Google announced that it
would suspend sales of its facial recognition software, citing concerns over ethics and reliability Google’s competitors, in contrast, took an additional 18 months to reach the same decision. Only
in early June 2020, in response to the Black Lives
Matter movement did IBM halt the sale of facial
recognition software to police forces in the United
States. Two days later, Amazon announced a one-year moratorium on sales of its facial recognition
software to police, followed by Microsoft the very
next day. For these organizations, the reputational
damage of producing systems that systematically
misidentified minorities, and selling the technology
to police forces to identify criminals, had already
been done. Google was proactive, while IBM, Amazon, and Microsoft were reactive, demonstrating
that compliance with today’s ethical standards is
insufficient; instead, organizations must also anticipate future ethical issues.
The need for a responsible approach to AI is
likely to increase even further, for three reasons. First,
as organizations scale up their use of AI, the ease of
capturing sensitive, personal data about individuals
will increase. Already, we are faced with the prospect
of social networks and internet giants knowing significantly more about our day-to-day habits than our
loved ones (and perhaps even we ourselves) know.
Second, as organizations transition into newer business models, the marginal value of collecting and
using data will increase. Organizations will be able to
assign a dollar value to each bit of data collected and
accurately calculate the risk-reward ratio associated
with each data point. Under these circumstances, the
the temptation to extract additional value from the data
they have collected or purchased may push organizations to overstep ethical boundaries, such as by
repackaging and selling data without consent.
Third, despite the importance of ethics, there is a
general lack of overarching guidelines or benchmarks
for responsible AI practices. Without a single established ethical arbiter, each organization and industry
will have to determine its own standards and limits.
Unfortunately, the fragmented approach to AI
will only exacerbate this problem. Unless organizations take a coordinated approach to AI ethics, it will
be too easy for a rogue team to breach ethical guidelines. It is possible that an AI ethics office will need to
be created within organizations to oversee AI activities, establish and implement ethical AI guidelines,
and hold the organization accountable for its ethical
practices. Companies that consider the ethics function as a branding and trust-building mechanism
will come out ahead of those that deem it merely a
regulatory issue. In addition to efforts within organizations to manage AI ethical practices, industry
associations, governments, and multinational non-governmental organizations can also play a role by
setting out clear guidelines governing the responsible use of AI technologies.
Because AI is not a regular technology, the AI
strategy needs to be approached differently than
the regular technology strategy. The power of AI to fuel
the extremes of corporate performance, both positive and negative, requires a purposeful approach
built on three pillars: a robust and reliable technology infrastructure, a specific focus on new business
models, and a thoughtful approach to ethics. An AI
strategy needs to be built on a solid foundation to survive the strong winds of change.
3 Technologies to improve customer and employee experience during the pandemic
Although the use of contactless payments has steadily increased, the CVID-19 pandemic has pushed contactless toward mainstream adoption. An August 2020 survey found that one in five consumers used contactless payments for the first time during the pandemic. Many believe this technology is essential moving forward, even after the pandemic subsides. In fact, according to a study by Entrust, 83% of respondents believe contactless cards are here to stay with 70% reporting they prefer contactless payment for sanitary reasons.
While contactless cards provide some solace for consumers worried about the risk of contracting the virus from payment devices, banks can go a step further and implement additional technology that will improve both customer and employee experience during the pandemic. Like contactless payments, the three technologies listed below may achieve mainstream adoption due to COVID-19, but their various advantages mean they’ll likely have long-term staying power in the financial services industries.
1. ID proofing authenticates identity remotely.
With COVID-19 cases surging across the U.S., many customers are limiting in-person visits to stores and other facilities, including bank branches. Unfortunately, many banks still require customers to physically visit a branch to open a new account or access certain services due to the difficulty of authenticating identity online.
You can help your customers stay safe by adopting ID proofing technology, which authenticates customers’ identities from a mobile device. The customer uses their device to photograph a government-issued photo ID, then takes a selfie for comparison via facial recognition technology. The user is then verified, denied, or issued a challenge to further test their identity based on rules set by your company. It’s important to note that ID proofing won’t work for every customer in every situation. No facial recognition algorithm is perfect, and many identify certain types of faces more accurately than others. A genuine user could fail the facial recognition test due to poor lighting when their selfie was taken, or their physical appearance may no longer match their photo ID. Some customers lack official photo IDs or mobile devices with facial recognition capabilities.
That being said, the AI engines that refine these algorithms are getting better by the day, and soon ID proofing will enable everyone to open new accounts and access services without leaving their homes. This capability will continue to show dividends after the pandemic by making your customer experience more streamlined and convenient, removing friction from the account onboarding experience. ID proofing involves collecting sensitive personal information, including biometric data. As such, it’s important to protect customers’ privacy by partnering with security providers that prioritize protecting personal data, as well as implementing strong cryptography strategies to avoid data breaches and other disruptions. Customers also deserve to know how their sensitive data is stored and used. Be transparent about your bank’s data practices and make sure you’re compliant with all current data privacy legislation.
2. Instant card issuance enables faster onboarding.
As we move toward a more on-demand future, customers will continue to want things instantly, including their payment cards. With instant card issuance, customers can drive to their banks and pick up a new card right away, rather than waiting several days for it to arrive in the mail. To minimize contact during the pandemic, banks can offer a drive-thru, touchless instant issuance, so customers don’t even have to step inside the bank. Then, they can immediately start using their new chip-equipped card for safe, contactless payments.
Instant card issuance is relatively easy to adopt: All you need is an instant card printer and either an on-premises or cloud-hosted software stack. Look for one with a user-friendly interface that’s able to handle multiple card designs, including both credit and debit card profiles. Your printer should also include cybersecurity protections including secure booting, malware detection, and encryption of user certificates to protect sensitive data.
As more customers look to adopt contactless cards during and after the pandemic, this technology will be particularly important for banks to address that need. And we also expect that instant card issuance will continue to be popular in the post-COVID-19 world, for both sanitation and convenience purposes.
3. Single sign-on authentication streamlines employee access.
Creating and remembering secure passwords is a major burden for bank employees and customers alike. Financial information is incredibly sensitive and requires the highest levels of protection, but the strongest passwords are often the hardest to remember. And if a user needs to remember multiple passwords to access different services or accounts — or secure areas of a building — the difficulty is multiplied, and so is the potential for password theft or password fatigue.
Single sign-on (SSO) authentication alleviates this difficulty by tying access to multiple accounts to one password. Bank employees can sign in once on a mobile phone or device and gain access to computers, networks, or areas of the bank building without the need to manage multiple passwords. By removing the need to type passcodes into keypads for room access, SSO can reduce opportunities for COVID-19 transmission among employees at work. It also streamlines the login and authentication process, improving productivity.
Most importantly, though, by eliminating the need to remember multiple passwords, SSO makes it easier for an employee to create a single, strong password to manage all their accounts. But with SSO, cracking one password can give a bad actor access to multiple platforms, so it’s especially important to avoid easy-to-guess combinations. Make sure to train employees on what constitutes a strong password and why it’s vital that theirs is secure.
Organizations can support security by implementing additional layers of protection, like two-factor authentication, as well as strong cryptography strategies that keep sensitive information safe. While no plan is 100% fool-proof, having a clearly defined strategy and team dedicated to protecting sensitive information is essential to maintaining customer and employee trust.
Thinking ahead to the post-COVID future
Any discussion of employee and customer experience today must take the impact of COVID-19 into account. With the widespread distribution of vaccines on the horizon for 2021, however, it’s also important to think ahead to how today’s investments will play out after the pandemic. While ID proofing, SSO, and instant card issuance offer sanitary benefits that are especially valuable during COVID-19, they also streamline customer and employee experiences in ways that will continue to improve ROI after the pandemic is just a memory.
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Classroom / Online / Hybrid 8/2/2021 (Monday)
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Next date: 8/2/2021 (Monday)
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