Reinventing the organization for speed in the post-COVID-19 era
Just because the times are fraught does not mean that leaders need to tighten
control and micromanage execution. Rather the opposite. Because conditions are
so difficult, frontline employees need to take on more responsibility for
execution, action, and collaboration. But this isn’t always easy and requires
that organizations focus on building execution muscle throughout the
workforce. Leaders must assign responsibility to the line, and drive
“closed-loop accountability.” That is, everyone working on a team must be
clear about what needs to get done by whom, when, and why. Employees must also
be equipped with the right skills and mindsets to solve problems, instead of
waiting to be told what to do. And there must be disciplined follow-up to make
sure actions were taken and the desired results achieved. CEOs who are serious
about execution excellence are investing in helping their workforces up their
execution game—through targeted programs, realigning incentives, and directing
rewards and recognition to teams that execute with speed and excellence.
Building execution excellence does not have to come at the expense of
innovation. Quite the contrary: it can help discover powerful ideas and
innovation from the frontline teams that are closest to the customer. And it
can drive excitement and loyalty among the employee base.
"If you focus on results, you will never change. If you focus on change,
you will get results." -- Jack Dixon
Are Tech Giants With Their AIs And Algorithms Becoming Too Powerful?
This reality is why large tech companies have extraordinary power today.
Current regulatory mandates were built for corporations in the past where
the market was the consideration, not forms of power. Susskind argues that
we need to see technology not just as consumers, but as citizens. At the
same time, social media can affect one of the most fundamental aspects of
democracy, which is deliberation and the way we talk to each other. We've
seen people become polarized because through their own personal choices,
algorithms are making choices for them, and they are fed information that
reinforces their own world view. We've seen people become more entrenched
in those views because the more time you spend around people and
information that agree with you, the more deeply you come to hold those
views. There's also a significant problem with the spread of fake news and
misinformation. In a sense, it isn't surprising that this has happened.
These social media platforms have not been developed according to the
principles of the forum or of healthy public debate. If that was so, they
would funnel information to you that was balanced, fair, and rigorously
checked or otherwise engineered to make you a better citizen.
Artificial Human Beings: The Amazing Examples Of Robotic Humanoids And Digital Humans
As artificial intelligence continues to mature, we are seeing a
corresponding growth in sophistication for humanoid robots and the
applications for digital human beings in many aspects of modern-day life.
... Even though the earliest form of humanoid was created by Leonardo Da
Vinci in 1495 (a mechanical armored suit that could sit, stand and walk),
today's humanoid robots are powered by artificial intelligence and can
listen, talk, move and respond. They use sensors and actuators (motors
that control movement) and have features that are modeled after human
parts. Whether they are structurally similar to a male (called an Android)
or a female (Gynoid), it’s a challenge to create realistic robots that
replicate human capabilities. The first modern-day humanoid robots
were created to learn how to make better prosthetics for humans, but now
they are developed to do many things to entertain us, specific jobs such
as a home health worker or manufacturer, and more. Artificial intelligence
makes robots human-like and helps humanoids listen, understand, and
respond to their environment and interactions with humans. Here are some
of the most innovative humanoid robots in development today
Why Companies Still Struggle To Incorporate AI Into Existing Business Models
Cutting-edge companies are already finding patterns in user behaviour that
can lead to exceptional products or features in existing products, which
is giving them an extreme advantage over other businesses. Take computer
vision (CV) for example. With computer vision, we can create a system that
does a subset of things that the human visual system can do. In CV, a
system can analyse a picture taken by a camera and understand what’s in
the picture. For example, it can recognise objects like cars, streetlights
and of course people. Computers can perform object recognition through a
network of nodes called neural networks. An image can be fed into the
network, and convolution happens at these nodes. This kind of technology
can be used for various business scenarios and lead to incredible amounts
of productivity and efficiency. For example, you can leverage computer
vision-based licence plate recognition to run an automated car parking
business. Of course, the information from registration, billing and
computer vision-based license plate recognition systems would have to be
integrated to automate the entire process.
Why the coronavirus pandemic confuses AI algorithms
The coronavirus lockdown has broken many things, including the AI
algorithms that seemed to be working so smoothly before. Warehouses that
depended on machine learning to keep their stock filled at all times are
no longer able to predict the right items that need to be replenished.
Fraud detection systems that home in on anomalous behavior are confused by
new shopping and spending habits. And shopping recommendations just aren’t
as good as they used to be. To better understand why unusual events
confound AI algorithms, consider an example. Suppose you’re running a
bottled water factory and have several vending machines in different
locations. Every day, you distribute your produced water bottles between
your vending machines. Your goal is to avoid a situation where one of your
machines is stocked with rows of unsold water while others are empty. ...
This new AI algorithm is much more flexible and more resilient to change,
and it can predict sales more accurately than the simple machine learning
model that was limited to date and location. With this new model, not only
are you able to efficiently distribute your produced bottles across your
vending machines, but you now have enough surplus to set up a new machine
at the mall and another one at the cinema.
The importance of peer feedback in the digital workplace
As the way we work shifts, employees’ prior strengths may become
liabilities, so it’s important to monitor behaviors over time and under
different circumstances. Someone who excelled at building relationships
through watercooler chitchat will need to find new methods when the work
group goes completely virtual. Likewise, the individual who was overlooked
as too socially awkward may begin to shine in a remote working
environment. Employees will need feedback on how effective their behavior
is in this new world so they can learn which new behaviors they may need
to adopt and which may now be seen as strengths. Peer reviews help people
understand better how to adjust to new technologies, even as the
technology itself is becoming part of the process. For example, I recently
coached a business unit chief financial officer (CFO) of a Fortune 500
company in the U.S. who had been passed over for a promotion in the middle
of 2019. His 360 feedback results in 2019 made it clear he was struggling
with his peer relationships. He was whip-smart, and everyone knew it — but
his peers felt he was too quick to show them up in meetings with the
senior leadership team.
Technology and innovation: Building the superhuman agent
Proactive conversational AI platforms can resolve requests before the
customer even feels the need to reach out. Modern solutions integrated
with various data systems can analyze large quantities of internal and
external data and identify triggers to start proactive and personalized
conversations through a customer’s preferred channels. For example, a
leading telco was able to eliminate 50 percent of unnecessary service
calls and inbound calls related to repairs by using robotics to
proactively contact customers and resolve issues as soon as remote
monitoring detected a malfunction. Two-thirds of customers believe service
through online channels and mobile devices should be faster, more
intuitive, and better able to serve their needs.1 Organizations should
seize the opportunity with improved front-end robotics or “virtual agents”
to handle repetitive, transactional requests as well as to guide customers
through a logical menu of topics and intentions to address issues.
Companies that have incorporated such technologies are seeing significant
returns: in fact, effectively deploying conversational AI can create a
twofold improvement in customer experience; reduce cost to serve by 15 to
20 percent; improve churn, upsell, and acquisition by 10 to 15 percent;
and result in a fourfold increase in employee productivity.
How to establish a threat intelligence program
One of the first steps towards establishing a threat intelligence program is to know your risk tolerance and set your priorities early, he says. While doing that, it’s important to keep in mind that it’s not possible to prevent every potential threat. “Understand what data is most important to you and prioritize your limited resources and staff to make workloads manageable and keep your company safe,” he advised. “Once you know your risk tolerance you need to understand your environment and perform a comprehensive inventory of internal and external assets to include threat feeds that you have access to. Generally, nobody knows your organization better than your own operators, so do not go on a shopping spree for tools/services without an inventory of what you do/don’t have. After all that’s out of the way, it’s time to automate security processes so that you can free your limited talented cybersecurity personnel and have them focus their efforts where they will be most effective. “Always be on the lookout for passionate, qualified and knowledge-thirsty internal personnel that WANT to pivot to threat intelligence and develop them.Why organizations should consider HTTPS inspection to find encrypted malware
Setting up HTTPS inspection can be tricky as it does require some extra
effort. And if not configured correctly, this process can actually weaken
the end-to-end encryption and protection provided by security gateways and
products. "Some organizations are reluctant to set up HTTPS inspection due
to the extra work involved, but our threat data clearly shows that a
majority of malware is delivered through encrypted connections and that
letting traffic go uninspected is simply no longer an option," Corey
Nachreiner, chief technology officer at WatchGuard, said in a press
release. "As malware continues to become more advanced and evasive, the
only reliable approach to defense is implementing a set of layered
security services, including advanced threat detection methods and HTTPS
inspection." A report from the US Department of Homeland Security's
Cybersecurity and Infrastructure Security Agency (CISA) offers some
recommendations on HTTPS inspection. "Organizations using an HTTPS
inspection product should verify that their product properly validates
certificate chains and passes any warnings or errors to the client," CISA
said.
Ex-Windows chief: Here's why Microsoft waged war on open source
Smith, a top lawyer at Microsoft during its war on open source, admitted
earlier this month that the company was wrong but said it had now changed,
pointing to its acquisition of GitHub and the company's open-source
activities on the code-sharing site. Now Sinofsky, who has a new book
detailing Microsoft's antitrust and security problems during his years
overseeing Windows and Office, has attempted to put some context around
Microsoft's new attitude and its old antagonism to open source.
Microsoft today has espoused open source as its focus shifts from Windows
PCs to Azure and Office in the cloud. But Sinofsky outlines reasons why
Microsoft's approach at the time was understandable – and how its model was
upended by software-as-a-service in 1999-2000, to which Linux was better
suited than Windows, and later Google's infrastructure. Sinofsky's defense
of Microsoft fleshes out Gates' explanation of GPL in 2001 that it "makes it
impossible for a commercial company to use any of that work or build on any
of that work". "Microsoft was founded on the principle that software was
intellectual property," Sinofsky says
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