First steps to adopting AIOps

Gartner placed AIOps (artificial intelligence for IT Ops) platforms ‘on the rise’ on their Hype Cycle for I&O Automation in 2019. It is only a matter of time that it moves to the ‘at the peak’ stage. As artificial intelligence and machine learning practices evolve, AIOps as a practice is also expected to grow. We anticipate that AI will mature rapidly over the next decade becoming a transformational force for any enterprise. And AIOps will play a significant role in that transformation.

But, AI isn’t there yet. To completely leverage the opportunities and returns that AIOps can deliver — some of which we outline in this earlier blog post — enterprises need to look beyond the hype. AIOps adoption needs to be far more than jumping on the latest bandwagon. It needs to be a strategic initiative, built for the long-term.

In this blog post, we’ll outline key considerations you must keep in mind while adopting AIOps.

Pick your strategy: Take a holistic view of your organization

True success of AIOps is in its ability to deliver at scale. Therefore, the long-term goal of any AIOps initiative should be to bring the entire enterprise on to a single platform, and automate as many processes as possible. A bottom-up approach of taking one process at a time and automating it will leave you with multiple inefficiencies. AI adoption will remain piecemeal, crippled by the very siloes you wished to eliminate. Moreover, you will be automating processes that were designed for humans, which might not be best suited for machine learning.

Therefore, to make the most of AIOps, you need a top-down approach. Begin by performing a thorough assessment of all assets, applications, processes and structures. Identify which of these can be re-wired for AIOps. Pick use cases that will have maximum impact, and begin your pilot there.

Prepare your data and build your team

Once you’ve identified the right use case for your pilot, prepare your data. Get an organization-wide process to gather data across application, infrastructure, business and user information. Also get as much historical data as possible for the AI engine to learn from. At this point, the quality of your data might be lower than ideal. Identify areas where you have quality issues and find ways to address them effectively.

In parallel, put together a team that can work will power your AIOps initiative — process engineers, data scientists, application leaders etc. who will come together to set the vision and business-level goals for the success of your endeavour.

Choose a platform

This is perhaps the biggest and most important step in your AIOps adoption journey. There are several platforms in the market that allow you to bring AI for specific processes such as helpdesk automation or DevOps automation. There are also general-purpose platforms that can apply to any part of your IT organization.

Considering the maturity of AI tools today, we encourage enterprises to choose a platform that will be adaptable and expansive enough to accommodate the future needs of your organization. An enterprise-grade AIOps platform should be able to do the following:

While shortlisting platforms and comparing them, keep the following in mind.

Data adaptability

Identify possible data types and file formats available in your enterprise and ensure your AIOps platform can process that. Depending on the kind of data you generate — code, logs, text etc., to IVR, emails and spreadsheets — how often you generate data and how dynamic it is, choose a tool that can handle that variance.

Interoperability

Ensure that your AIOps platform can integrate seamlessly with your existing tools such as those for trouble ticketing, IT service management (ITSM), DevOps, cloud management, project management etc.

Suitability

Outline your long-term goals and choose the platform accordingly. If data quality is a matter of concern for you, you might need a platform that has data cleansing and harmonization capabilities. If C-suite decision-making is your primary goal, you will need a platform that has engaging data visualization capabilities.

Scalability

It also goes without saying that an AIOps platform should also be able to dynamically scale, and adapt to the organization’s needs.

Take a phased approach

Just because we recommend a top-down approach with a holistic view, you don’t have to adopt AIOps all at once. Take it one step at a time. The first step would be induction — feed historical data to the AI engine, for it to learn about your business, environment and current state. Here, you will define metrics, build dashboards and build reports. Then, get the AIOps engine to build machine learning models for pattern discovery, anomaly detection etc. with the historical data. Post this phase, connect your AI engine to real-time data sources for predictive analytics, root-cause analysis etc.

Remember the AI engine is only as good as the volume, variety and velocity of the data it gets. Ensuring that there is enough accurate and good quality data is an important part of making the most of your AIOps platform. By rushing to the last phase, without ensuing the efficacy of the historical data, you might be setting your AIOps initiative — and yourself — up for failure.

If you’re unsure of whether to adopt AIOps, read our whitepaper to know about the transformative outcomes it can bring. If you’re considering AIOps adoption, speak to our consultants now.

Artificial Intelligence – here for good

What comes to mind when you hear Artificial Intelligence? Perhaps one of those slick videos of robots making an omelette or an android (a humanoid robot – as it used to mean before the operating system came about). In reality, Artificial Intelligence (AI) has crept into a lot of things we don’t typically see – from contextual advertisements on the web, to customer support systems.

As banking goes digital, AI has begun to play an interesting role in the evolution of the digital bank. From intelligent automation systems that simplify payments reconciliation, general ledger management and OCR enabled document validation to First-Responder roles in customer support, AI has taken bold steps forward. Another extremely important trend in banking – one where market segments are of one customer has relied on AI to delivery hyper-personalized experiences based on customer profile, buying behavior and choice of products and services. In a day and age where customers demand simplicity, speed, and superlative experiences, Artificial Intelligence has come of age, so to speak.

This said, AI gets a resounding aye, when it comes to thrust areas for banks today, and come 2020. As AI maturity increases, banks have discovered newer and significantly more efficient means to reduce costs, improve regulatory compliance, and most importantly, consistently improve customer confidence in the bank’s ability to serve their needs, secure their information, and simplify their transactions with banks.

With use cases showing tremendous promise already, the trend for 2020 and beyond will pivot around AI, machine learning and deep learning platforms. These new entrants are poised to take customer experience management, Identity verification, fraud prevention and security by storm.

How will banks develop on the existing use cases and innovate their digital offerings with AI at the core? Learn about this and all the trends for banks to scale digital from our report.

This piece gives you a glimpse into one of the 10 trends that I believe will reshape banking in 2020. Click here to read our full report “Scale Digital: 10 Trends Reshaping Banking in 2020”.

Security 2020 – Fixing the link that risks breaking the chain

In 2015, two hackers, albeit in a controlled environment, hacked into a Jeep Cherokee through the entertainment system and disabled the transmission. This led to a recall of over 1.4 million vehicles to fix the vulnerabilities that made this attack possible.

Today, many such devices, connected directly to the internet are used to pay tolls and also send confidential and personally identifiable information through this internet to points of sale devices in drive through restaurants. The connected car isn’t the only connected device. Watches, Refrigerators, TVs, Home Automation systems – all connect to the internet and make transactions on behalf of the person. And they all transmit infinitely sensitive information.

The whole digital revolution has had everyone progressively move their data and applications to the cloud. And this opens an entirely different can of worms from a security standpoint. While public cloud providers have improved infra security, banks have to configure their applications safely on the cloud. They will need to build resilience through employee education, mature cloud competence and acquire new tools for managing cloud security.

And it doesn’t stop there. We have bragged about using big data, machine learning (ML) and artificial intelligence (AI ) to detect and prevent attacks – hackers are now using ML and AI to refine their attacks to an unprecedented degree of sophistication.

The biggest challenge for Cybersecurity experts is the fact that there is no longer a “Safe Zone” – no longer routers, switches, or firewalls to enclose sensitive information from the prying hands of hackers. 5G, and the Internet of Things (IOT) have brought an end to what we imagined as privacy.

Understandably, across the world, nations are enacting stricter laws and regulatory requirements to define and protect the privacy of citizens. At the same time, these very lawmakers are also forcing banks to share customer and transaction data with third parties in the name of anti-protectionism and free markets.

What will be the security of the future? How will banks of tomorrow protect customer information and yet collaborate with the global ecosystem of authorized stakeholders? This is the challenge for 2020.

As the digital age evolves, so will cybersecurity devices. Artificial Intelligence, machine learning and cognitive automation will form the basis for analyzing, understanding, identifying, and preventing cyber-attacks. Read in the Infosys Finacle report on top 10 trends for banks to scale digital in 2020 to explores the different strategies that banks are adopting to build a flexible, yet robust, fluid, yet firm security system that both protects data and prevents its breach.

This piece gives you a glimpse into one of the 10 trends that I believe will reshape banking in 2020. Click here to read our full report “Scale Digital: 10 Trends Reshaping Banking in 2020”.

Banks on the Public Cloud – The final frontier breached!

While historically, banks have been guilty of slacking when it comes to technological evolution, digital disruption has woken them to a harsh reality. If banks don’t get to where their customers are, new-age competition and those leading the way will do so, and in the process mop the marketplace with the erstwhile giants.

It is this race for survival that has had banks turn their heads to cloud, especially public cloud as a means to the end. But what end? Even in the recent past, most banks were content with moving non-critical and non-core workloads to the cloud. How did the resistance to cloud diminish?

A lot of what has changed in the past year or two can be attributed not just to the competition in the banking space, but also the fact that Cloud has itself evolved:


Here’s how we expect the banking-on-cloud trend to play out in 2020 and beyond:

All said and done, single tenant cloud systems are still the safest haven for confidential information. So how will banks reconcile with new environmental realities? How safe is the bet that Public Cloud IaaS and PaaS will grow multi-fold in the next two years? The biggest question is – will it really happen? Read about all this, and more in the Finacle Banking Trends to Scale Digital in 2020 Report to learn more about how Public Cloud will shape the future of banking.

This piece gives you a glimpse into one of the 10 trends that I believe will reshape banking in 2020. Click here to read our full report “Scale Digital: 10 Trends Reshaping Banking in 2020”.

Blockchain – Returning home of the prodigal

A while ago, there was this insurance advertisement. The father hears the baby say daddy for the first time, then comes mumma, even banana – the dad’s expectations rise, and he goads the baby to say Czechoslovakia.

The story of blockchain isn’t vastly different. After the initial hype, where some marketers saw, as John Oliver put it, their soft drink company stock prices rise because they put blockchain in their name. The higher it rose, the harder it fell and suddenly it was disillusionment everywhere. Luckily folks didn’t give up. Like in the ad which ends with the dad reassuring himself that the baby said banana – and that’s no mean feat!

The distributed ledger gained momentum with the two-fold experiment to demonstrate proofs of concept and value. With the launch of platforms such as R3 Corda, and Hyperledger Fabric, the arrival of 2020 marks the return of the prodigal blockchain. Faith and fanfare is back in the distributed ledger. Regulators aren’t far behind – promoting areas such as eKYC to have their basis in blockchain, there’s a bandwagon again – including Infosys’ own blockchain-based trade finance network.

The value of blockchain is finally real. And was demonstrated inescapably when its secure environment for digitization, automation, and approval in workflows. With a blockchain-based working capital finance system, banks are disbursing funds against invoices, albeit limited to certain types and value, in less than a day. This turnaround time used to be as long as 20. To mimic the dad in the insurance ad, 95% reduction in disbursal time isn’t a mean feat!

With the disillusionment in blockchain being unable to deliver on promises firmly in the past, there is a renewed focus and fervor in blockchain solutions. What will the future hold? How far will blockchain take us, as digital takes the world by storm? Read more in the Finacle’s report on top 10 banking trend to scale digital in 2020 as we look into the different strategies businesses are adopting in their blockchain initiatives, newer revenue streams, and diverse applications.

This piece gives you a glimpse into one of the 10 trends that I believe will reshape banking in 2020. Click here to read our full report “Scale Digital: 10 Trends Reshaping Banking in 2020”.

Internet of things – More there are, more we bank on them

As the number of connected devices slowly creeps past unnoticed catching up with the number of humans on the planet, we are beginning to rely on them increasingly.

The possibilities as much a cliché as it would be – are endless:

But how will IoT impact the way banking is done in 2020 and beyond?

Earlier, before the digital explosion, there were definitive guides to decision-making. Rotten tomatoes, yelp, word of mouth, financial advisors, fashion experts – there was a place to go or a person to approach, who would give us the basis for making our decisions. Today, analysis and reporting are no longer the realm of the experts. From restaurant menus to mutual fund performance, everything is analyzed thread-bare by a multitude of experts, each offering a unique, or a deeper perspective. This information, when personalized and contextualized can serve as a powerful influencer. This is where banks would like to be. And IoT will help them get there.

But there are also the questions of privacy, protection, and prevention of misuse. With so many devices connected, people often lose track of which information, which card, which bank account, or which line of credit is available to whom. Banks of the future will use IoT data such as geotags and multi-factor authentication using the different intelligent devices of the user to validate, authenticate, and process transactions smoothly. Likewise, we would need a robust governance layer regulating and monitoring the manner in which the Internet of Things deal with the little things in your wallet and your life. Furthermore, the data generated by these devices will also need adequate protection to prevent fraud and misuse.

Unlike market studies, which take weeks to present their findings, IoT offers real-time insight. This means banks can continually evolve and personalize their products instead of maintaining a static portfolio. As financial ecosystems connect among themselves and with other ecosystems on the IoT, banks can facilitate customer journeys around products from end to end and thus intensify engagement. An example comes from a challenger bank in the United Kingdom. The bank’s mortgage smart app also offers property insights to customers on their connected device. In addition, when a customer applies for a loan, the details are automatically forwarded to other agencies in the ecosystem, such as the local council and legal advisors, activating a logical chain of events. In 2020, expect a growing number of banks to use insights about device usage patterns to not only develop contextual portfolios but also build loyalty with effective loyalty programs for their products and services.

We will also see a sound governance framework evolving around IoT in 2020 and beyond.

What will be the landscape of IoT in the future? How will banks, marketers, service providers, enablers, aggregators, and users interact with this intricate web of devices and connectivity? If these questions are on your mind, do read our report on top 10 banking trends to scale digital in 2020.

This piece gives you a glimpse into one of the 10 trends that I believe will reshape banking in 2020. Click here to read our full report “Scale Digital: 10 Trends Reshaping Banking in 2020”.

2020: It’s time to scale

A Citibank analysis makes an interesting observation that while digitization could lower incumbent banks’ costs by 30 to 50 percent, largely by reducing headcount, it could also take away 10-30 percent of their revenues and give it to their even more digitized (new) competitors. This statement is referring to a scenario when the banking vertical layer will unbundle to digital disruption, laying open any inefficiencies for new age service providers to exploit. It conveys the extent to which digital is disrupting, pervading and transforming business; an extent that many organizations are yet to come to terms with. In banking, for example, digital has eroded the (incumbents’) advantages of reach and capital and completely commoditized products and services. With price, incentive and advisory also losing their potency as differentiators, banks are in search of that elusive unique proposition that will be valued by customers. Meanwhile, the agents of digital disruption new digital players (big tech, Fintech etc.) powered by modern technologies – Cloud, Artificial Intelligence, Blockchain, Mixed Reality, and Internet of Things – are steadily disintermediating incumbents from those very customers.

The way out of this grim situation – and it’s not an easy one – is to scale digital transformation throughout the enterprise. Most banks understand this but are unable to carry it through: in the latest Efma – Infosys Finacle ‘Innovation in Retail Banking’ survey, only 17 percent of respondents confirmed deploying digital transformation at scale.

Our conversations with banks around the world reveal a growing impatience to get past the challenges of legacy technologies, integration and culture, which are the biggest barriers to transformation at scale. Actually, incumbent banks have no time to lose, because each day is bringing a new competitor that is digital-native by design. Hence in 2020, we hope they will focus on five actions to achieve digital transformation at scale:

In 2020, Infosys hopes to help banks scale digital transformation throughout the organization. Our trends forecast also covers the shifts impacting this agenda.

Click here to read more.

Why modern enterprises need AIOps

In 2019, all of the top five most valuable US businesses were tech companies. But what’s more transformative is that in this decade and the future, every company will be a tech company — irrespective of what you sell.

Take the case of Walmart, which is the largest brick-and-mortar retail company in the world. In the last few years, in order to compete with the e-commerce disruption, it has made some significant acquisitions in the space. Not just in the US, where it bought Jet.com — which was the biggest deal at the time — but also across the globe, with the acquisition of Flipkart.com in India, for instance. Fast forward to now, it is the second largest online retailer, even if by a distance.

As more and more companies undergo digital transformation, putting tech at the core of their businesses, their ability to succeed will come to depend on their IT backbone. So, IT Ops — the monitoring and management of organizational technology (typically other than software engineering) — will come under immense pressure.

IT Ops, as we know it today, will become insufficient, for various reasons.

IT Ops is inadequate for scale

Digital transformation initiatives has enabled unprecedented scale in technologies across the board — compute, storage, network, security etc. Along with scale, also came variety, of infrastructure and processes, creating complexity that outpaces traditional IT Ops.

As a result, traditional IT Ops can no longer:

Org siloes makes IT Ops ineffective

Breaking a scaling business into smaller manageable business units is a logical move enterprises regularly make. While it is good for local optimization and closer oversight, it misses out on leveraging organizational knowledge and size.

As a result, IT Ops in a siloed organization will suffer from:

IT Ops fails in linking its efforts to ROI

With fast-paced nature of IT projects and with new development paradigms like agile, IT Ops teams are constantly spinning many plates at once. Managing complexities, addressing various different departmental goals/metrices, putting out fires, meeting compliance standards, IT Ops will not be able to demonstrate ROI for the organization as a whole.

As a result, IT Ops will suffer. It will:

To overcome these challenges, IT Ops teams in many enterprises adopt automation. It works, in most cases. It makes them react faster to incidents; centralised dashboards in real-time for faster decision-making. But, in the long-run, localised automation for organizational issues is like treating a chronic illness with band-aid.


For true innovation at scale, IT Ops needs to break down siloes, measure outcomes effectively, and align themselves with business goals — IT Ops needs to become predictive and pre-emptive.

Simply, IT Ops must become AIOps.

What is AIOps?

AIOps is the application of machine learning and data science to IT operations to make them more efficient. Gartner predicts that AIOps in large enterprises to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.

An AIOps platform is a data layer for your IT Operations.

  1. It ingests and analyses data: Across various sources including historical data, real-time data, log data, network packet data, document text data etc.
  2. It discovers pattern automatically: Identifying patterns in data streams, offer correlations, and predict future incidents.
  3. It detects anomalies: Using patterns to identify what constitutes normal behaviour and what is a deviation.
  4. It performs root-cause analysis: Through automated pattern discovery to identify genuine causal relationships and aid operator intervention.

What can AIOps do for your enterprise?

Firstly, it can intelligently automate monitoring at scale. An AIOps platform can separate the noise from the signals — filter out the real incidents that demand your attention.


It can integrate your siloes, even if not break them down. In that, you can continue to have your departments and verticals run as smaller units, yet be connected seamlessly through the centralized AIOps platform for efficiency and global optimization.

This improves governance, allowing you to have an organization-wide view of your IT. As a corollary, AIOps can improve measurability of key metrics and link back to business ROI. It can eliminate tool sprawl and optimize not just your applications, but also data usage.


In our opinion, the biggest advantage of adopting AIOps is that it changes the face of your IT operations from machine centric to customer centric. Instead of being an asset management function, AIOps can enable your IT to focus on your customers — be it internal like employees and vendors or external like your end-consumers.

With AIOps, your IT teams will build deeper relationships with your customers, pre-empt issues, and offer proactive service, having direct impact on your bottom line. Read our whitepaper to know more about how AIOps can impact each of these areas.

In fact, Nia AIOps helped a large European bank automate their L2 support, achieving 90% improvement in MTTR and equivalent reduction in IT operations effort. Click here to read the case study.