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W H I T E P A P E R

Towards a World of Frictionless Change: The Role

of Automation and AI in IT

Sections1. Change Is The New Normal ��������������������������������������������������������������������������������������������������������3

4. Future-proofing with abstraction �����������������������������������������������������������������������������������������������6

3. IT is still too artisanal ������������������������������������������������������������������������������������������������������������������7

3.1. Applying abstraction and automation to IT ���������������������������������������������������������������������8

3.2. Becoming more like a software company ������������������������������������������������������������������������9

4. Not just another wave ���������������������������������������������������������������������������������������������������������������12

5. Not all automation tools are alike ��������������������������������������������������������������������������������������������15

C O N T E N T S

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Change Is The NewNormal

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1. Change is the new normalIs it possible to prepare for events you can’t predict? Counterintuitive as it may sound, the answer is yes. And that

should be welcome news for enterprise IT leaders, who are so often on the receiving end of business demands and

technological developments they can neither foresee nor control. Change is the only constant in IT these days, and

it’s coming fast and furious. As much as CIOs might wish they could hit pause just long enough for their organizations

to catch their breath, there’s no letup in sight.

And while it may seem that the only reasonable response is to stick to the most urgent priorities and do a sort of

never-ending triage, there’s a better alternative – a way to smooth the road and make change relatively frictionless,

including both the changes you can see coming and the ones you have no inkling of. That’s what this paper is about.

85%of enterprise IT environments rely on a mix of manual processing, one-off scripts and other labor-intensive methods.

Information technology is racing ahead

with a Moore’s law-like inexorability and

compounding rate. That’s put enterprise IT

organizations in a vise: told to “modernize”

or “transform” by adopting the latest

technologies while still keeping a complicated

mix of older systems up and running. Even

if you’re in the enviable position of starting

from scratch and able to cherry-pick the

latest and greatest solutions, you can’t stay

greenfield forever. You’ll sooner or later have

legacy investments and heterogeneity to

contend with.

No wonder some commentators have called

the CIO’s job “the toughest in the C-suite.”

IT organizations can’t abandon the past, and

“embracing the future” is little more than a

slogan when we barely know what’s going

to happen next year, much less a decade

from now. So wouldn’t it be more realistic

to concentrate on the immediate challenge

of keeping the lights on and incrementally

upgrade whenever possible – in other words,

to focus on the knowable and near-term and

leave tomorrow for another day?

Actually, no. While it’s impossible to know

what’s next, it is possible get out of reactive

mode and get ahead of the game. And that

doesn’t mean simply focusing on practices

like DevOps or technologies like cloud

adoption and container stack selection.

Those are important advances, and they do

move the ball forward, but like all trends,

they’ll come and go. What we’re talking

about here is a more foundational approach

that puts you in a better position to take

advantage not only of current developments,

but any future innovations.

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The advent of the tidal wave of next generation technologies like Internet of Things, big data analytics, cloud services, etc. has provided an upsurge to many AI-driven solutions and employed the insights from the colossal volume of big data and information. This is helping businesses in ensuring that they are taking conversant decisions keeping all future possibilities in mind. A study conducted by Accenture Research and Frontier Economics stated that integrated AI into economic processes is directly proportional to potential for economic growth of a business.

Artificial Intelligence (AI) Adoption

Reasons businesses adopt Artificial Intelligence (AI) Saving time and money

Acquiring a Competitive Edge in the Global Market

Understandings of the Business niceties from Cutting-edge Analytic Solutions

Generating Business by Virtual Customer Support Arrangement

Ever-growing Collaborative Pedagogy on AI

Training

15%9%

5%

3%3% 1%

Side Note: Did you know?

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Think of it as a kind of institutional future-

proofing. It relies on two principles as old

as information technology itself. The first

principle is abstraction, which has been

defined as “inventing languages that let

us talk more nearly in a problem's own

terms and less in terms of the computer's

mechanisms or capabilities.”

That, in many ways, is the story of computing.

In the primordial days of the industry, using a

computer meant talking directly to the “bare

metal.” Programmers flipped switches or

configured wires, representing the raw ones

and zeroes of machine code. They specified

every tiny step the hardware had to perform,

right down to the precise register locations

and memory addresses.

It was painstaking, error-prone, highly

specialized and impossible to scale as the

speed and sophistication of the machines

grew.

Abstraction came to the rescue, freeing

programmers from the tedious details so

they could focus on the bigger picture. The

first stage of abstraction was assembly

language, which substituted words for

numerical machine codes. It was still deep in

the functional weeds, but easier for humans

to read and remember. Next came high-

level programming languages like COBOL

and Fortran, followed by even higher-level

languages like Ruby, Python, Perl and Visual

Basic.

Instead of directly talking to the hardware and

describing every elementary computational

step, programmers could specify the higher-

order tasks they wanted the computer to

accomplish – like rendering an image on

screen or indexing a file or creating a list.

There was no need to spell out the thousands

or millions of discrete operations that went

into performing those functions.

2. Future-proofing with abstraction & automationAbstraction came to the rescue, freeing programmers from the tedious details so they could focus on the bigger picture.

60%Automation is crucial for about 60% of organizations today.

Source: Tech Pro Research

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3. IT is still too artisanalIn fact, 85% of enterprise IT environments rely on a mix of manual processing, one-off scripts and other labor-intensive methods.

By buffering us against increasing complexity and flux, the abstraction-plus-

automation paradigm has helped make life livable in the information age. But

not all of the tech industry is tapping its full potential. Most IT departments, for

example, have at least one foot in an earlier era of manual methods. In fact, 85%

of enterprise IT environments rely on a mix of manual processing, one-off scripts

and other labor-intensive methods.

Many of those who do use automation are usually doing so in a piecemeal and

limited sense. They’re manually provisioning new servers with brittle scripts that

have evolved into a complex tangle of conditionals. They’re SSH-ing into machines

to directly update configurations. They’re mapping some UML-like application

model to a series of scripts that are supposed to configure the underlying

infrastructure for an application release.

That’s not the level of comprehensive and unified abstraction and automation

needed to manage increasingly intricate and fast-changing infrastructures. No

wonder IT shops are facing mounting backlogs, delays and costs, barely able to

keep older systems chugging along, much less satisfy the endless demand for new

services.

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3.1. Applying abstraction and automation to IT

Fortunately the same model that’s worked so well for computing can be applied to IT.

Just as programmers use abstract descriptions to specify what they want a computer

to do, sysadmins can do the same with diverse collections of IT resources, be they

applications, the services they use or the servers they run on. This practice is commonly

called managing your infrastructure as code or operating a software-defined data center.

If you have a sufficiently robust, standardized language for describing your infrastructure,

you can treat your entire infrastructure with the same degree of abstraction that

programmers benefit from. This includes everything from mainframes to load balancers

to cloud-based services, and from private data centers to public cloud environments.

Abstracting management across your infrastructure eliminates a huge amount of

complexity and multiplicity, making the work simpler, more uniform and easier to

understand, and facilitating better communication and collaboration.

Once you have a uniform description, you can apply automation. You just need software

that takes that high-level description and translates it into desired actions. Automation

dramatically simplifies and standardizes all sorts of IT processes, as these before/after

scenarios illustrate:

If you have a sufficiently robust, standardized language for describing your infrastructure, you can treat your entire infrastructure with the same degree of abstraction that programmers benefit from. This includes everything from mainframes to load balancers to cloud-based services, and from private data centers to public cloud environments.

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These benefits multiply when this approach is applied across the board, when all IT resources

are specified in a common language and wrapped in a comprehensive automation layer.

Companies that fully exploit the power of automation can deploy changes 200 times times

more frequently than the organizations that don't. And that means they have 200 times as

many opportunities to deliver better software to their users in the same amount of time.

3.2. Becoming more like a software company

In effect, you’ve turned your infrastructure into code. And that means you’re ready to take

advantage of the same efficiencies that make modern software development such a fast,

repeatable, and dependable process.

The payoffs include easier management of IT infrastructure, faster development and greater

resiliency in the face of change.

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The result of all this is that your IT operation becomes more like a leading-edge software company: able

to rapidly push out updates and new functionality to users, drive continual improvements and leverage the

sort of efficiencies that make companies like Google, Spotify and Amazon the envy of the industry.

One reason those companies are able to do the things they do is because they’re working with a relatively

modern and unified stack, something that enterprise IT departments, with their mixed arrays, could only

dream of in the past. Well, they don’t have to dream anymore because the combination of abstraction

and automation confers that uniformity, providing a common language and stable interface that hides the

underlying heterogeneity.

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Since you use a common language to describe what you want your apps, services and infrastructure to do, you can use the same language to help you adopt new software as well as maintain the old.

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S E C T I O N 4

Not just another wave412

4. Not just another waveSince you use a common language to describe what you want your apps, services and infrastructure to do, you can use the same language to help you adopt new software as well as maintain the old.

This isn’t the first solution aimed at managing complexity and change in IT. In fact, some of the most important IT trends today are partial stabs at the same problem. DevOps, for example, attempts to improve speed and predictability by

structuring and streamlining the often messy relationship between development and operations. Cloud computing outsources infrastructure maintenance and provides a clean, unified stack to work with. Containers simplify the provisioning and scaling of applications across processing resources.

These are all great, as far as they go. But each tackles only a piece of the problem. DevOps is just the latest in a long line of evolving cultural norms, and it won’t be the last. Cloud solutions only apply to your newer cloud-based applications, not your existing brownfield environments and legacy systems. 4 13

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The same goes for containers, which work best with newer, containerized microservices.

Taken alone, none of these solutions addresses the sheer breadth and diversity of real-world IT landscapes. But abstraction plus automation, applied broadly, does. It encompasses everything from aging mainframes to new cloud apps, providing a standard, simple, secure and scalable mechanism for delivering and operating all of the applications, services and infrastructure in your data center. Just as important, abstraction and automation are evergreen. It’s not just a temporary solution that will be swept aside by another wave in due time. Done right, it provides a platform for reliably delivering and integrating future technologies into your IT environment with minimal disruption.

Since you use a common language to describe what you want your apps, services and infrastructure to do, you can use the same language to help you adopt new software as well as maintain the old. So though the low-level technology is constantly evolving, you’re not constantly re-coding. And that goes not only for current technologies, but also for next-gen solutions yet to be imagined.

Just as important, abstraction and automation are evergreen. It’s not just a temporary solution that will be swept aside by another wave in due time. Done right, it provides a platform for reliably delivering and integrating future technologies into your IT environment with minimal disruption.

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5. Not all automation tools are alikeNote that when discussing these sweeping impacts, we’re talking about a particularly robust and comprehensive form of automation. While there are a variety of partial solutions that automate a limited set of tasks, the real payoffs come when you use a comprehensive approach. Look for a solution that provides the following:

• A common language that describes the desired state of all resources.

• Automation for the entire data center, from mainframes to containers, from physical to

virtual and from on-premises to cloud.

• Automation throughout the stack, including distributed apps, middleware and

infrastructure.

• Situational awareness, keeping you informed on the state of and changes to your

infrastructure.

• Change orchestration, providing detailed control and automation of ordered deployments

and change throttling, as well as real-time visibility as you push out changes to distributed

applications and global infrastructure.

• Inherent security and compliance with automatic policy enforcement, remediation and

reporting.

5.1. Constantly modernThe kind of tools we’re talking about aren’t pie in the sky. They aren’t aspirational. They’re

available now, and they’re already helping forward-thinking IT organizations realize the kinds

of results described above. Companies using best-of-breed automation solutions are far more

agile and adaptable, spending less time on firefighting and more time on innovation. They’re

able to deliver software 200 times more frequently, with 2,555 times faster lead times. They

spend 22% less time on unplanned work and rework, and 50% less time remediating security

issues.

Those are massive advantages, and the dividends accrue over time. A relatively modest

investment in automation today brings huge returns down the line. Not surprisingly,

companies who’ve gone all-in on automation are pulling away from their competitors at an

accelerating rate, and businesses still relying on older methods would do well to follow suit

while they can still close the gap.

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Companies using best-of-breed automation solutions are far more agile and adaptable, spending less time on firefighting and more time on innovation.

It’s the latest chapter of an old story. Automation has been transforming industries like manufacturing for centuries, upending the status quo and enabling whole new economies of scale. Now it’s IT’s turn for an industrial revolution, and there’s no going back.

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