Intelligent Enterprise-Machine Learning- The Strategic and Structured Approach To Digital Transformation

Leading the Intelligent

Enterprise-Machine Learning- The Strategic and Structured Approach To Digital Transformation 

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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 the 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 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 acrosss

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 analysis 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 opportunitistic and tactical

AI decision-making to a 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 fintechs are using

facial recognition to support know-yourcustomer 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

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 buyer

activity to reward legitimate customers with a

smoother process and block out resellers. As 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

semi-automated task, the company has created a

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 regulatory issue. In addition to efforts within organizations to manage AI ethical practices, industry

associations, governments, and multinational nongovernmental 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

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

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 customization

of products and services across time. As these and

similar developments open up new sources of revenue

and profit, new business models should thereffore 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 oneyear 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

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 nongovernmental 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

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.

CoScreen catapults to the IT collaboration scene

The Lead

[1] CoScreen catapulted from stealth into the enterprise IT collaboration scene

[2] Morphisec made millions in funding for its moving target defense tech

[3] MeetinVR is making its video meetings more real than Zoom’s

The Follow

[1] Collaboration has emerged as a key theme over the past year, with businesses forced to rapidly embrace remote work and figure out how to manage colleagues scattered over disparate locations. In the software development sphere, this provided a boost for tools such as Microsoft’s Visual Studio Live Share, while upstarts such as CodeSandbox and Replit raised sizable investments to further develop their various browser-based code collaboration platforms.

But despite the broad embrace of cloud-based integrated development environments (IDEs), the software collaboration process extends far beyond coding, which is where a new startup called CoScreen is hoping to carve out a niche. CoScreen says it’s a “deep collaboration” platform for engineering teams, encapsulating next-gen screen sharing, editing, and communication. Founded last April, the California-based company officially launches today with $4.6 million in funding.

CoScreen could be used for any number of scenarios in software development, such as pair programming, debugging, or onboarding new hires. It’s managed to attract engineering teams from enterprises such as Slack, Okta, Salesforce, and SAP. CEO and cofounder Till Pieper told VentureBeat, “We are seeing considerable interest from large enterprise customers in extending their usage, as well as organically growing usage within organizations.”

[2] With cybersecurity companies going after the big enterprise fish, midsize companies can get lost in the shuffle. But Morphisec has developed an endpoint protection service that’s designed to be cost-effective enough to serve them high-quality protection. Today, the company announced it has raised $31 million in a recent funding round.

Morphisec CEO Ronen Yehoshua said security for midsize companies is taking on greater urgency as they struggle with migration to the cloud and other aspects of their digital transformation. By automating security, Morphisec believes it can help them out. It relies on a technique called “moving target defense” that constantly changes the security parameters of a system to thwart cyberattacks. This enables a kind of zero-trust defense that doesn’t require employees to have deep security expertise in order to combat threats.

The company also helps leverage existing security tools to make them more effective. That includes a partnership for a service that makes the latest security features in Microsoft Windows 10 easier for clients to use. That means by blocking attacks, Morphisec helps reduce not only the costs of security tools, but also the expense of recovering from such attacks. The company says it is currently protecting 7 million endpoints and plans to use the latest funding to expand its team in the U.S. and Israel.

[3] Copenhagen-based MeetinVR launched an app for collaborative meetings in virtual reality. It’s now available in the Oculus Store and can run on the Oculus Quest and Quest 2 platforms. Cristian-Emanuel Anton, MeetinVR CEO, told VentureBeat that the app costs 35 euros a month ($41.38) and can accommodate as many as 35 people in the same room. 

Anton’s vision is to make both customer and internal business meetings better than in real life. The app does this by making human interaction more intuitive and effective, according to Anton, and by giving participants “superpowers.” The app has VR collaboration tools such as spatial sound, 2D/3D drawing, and speech-to-text note-taking. MeetinVR also has distinctive features such as being able to shake hands virtually, 360 media casting, Microsoft OneDrive integration, meeting management, and a set of tools for workshop participants, facilitators, and moderators. 

The need for remote working solutions continues to rise and is creating a permanent change in enterprise meeting environments across the globe. And both Fortune 500 companies and private users are keen to explore the possibilities of VR collaboration. It’s also a way to escape Zoom during the ongoing pandemic.

The Catchup

In an interview with VentureBeat, DataRobot CEO Dan Wright called for a “new era of democratization of AI.”

Intel plans to invest $20 billion in new Arizona factories and manufacture chips for other companies.

Here’s what separates NoSQL databases from their SQL counterparts.

AI is making talent management more intelligent in 2021 with virtual recruiting and fair compensation.

Companies say AI is helping them hire more women during the pandemic.

CEOs from Facebook, Google, and Twitter are testifying to Congress today about misinformation on their platforms and its role in real-world extremism.

British fashion retailer FatFace was hacked in a major breach, but asked customers to keep the breach “strictly private and confidential.”

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