The last couple of years have been unlike any others. Digital transformation – be it through the establishment of remote working or the creation of new e-commerce channels – has taken place at lightning speed.

At this time of rapid change, IT teams have operated under extreme conditions. They’ve had to deal with reduced teams and tech budgets, fast-changing organisational priorities, and increased threats in terms of business continuity and information security.

As 2021 progresses, what are the elements that would help CIOs and their teams to deal with the new challenges that lie ahead? Here are five tips that we believe would help tech leaders to create a game-changing IT department.

Clear objectives from the business
Technology is still often implemented in isolation. Rather than being purchased to achieve set aims, IT is bought as a solution looking for a problem. Yet 2020 has seen a step change; IT is now seen as a problem solver. Digitisation helped companies deal with the challenges of coronavirus and now the business wants more from its tech.

With IT budgets still under downward pressure, CIOs and IT teams need direction from their business peers. They need to work in unison to create strongly-defined use cases. With data-led emerging tech, like artificial intelligence and machine learning, moving from the laboratory to the enterprise, that requirement has never been more pressing.

The good news is that CIOs and their IT teams will have spent much of the past 12 months engaging closely with their business peers to ensure that their technology demands are met. Now they must create a clear long-term digital strategy that puts the business first.

More commitment to the cloud
The past 12 months has almost been like a giant beta test for the cloud. At a moment’s notice, companies had to switch to home working, real-time collaboration and online fulfilment. It was a test that the cloud passed with flying colours.

The task for 2021 is to make even more use of the cloud. Global spending on public cloud services is forecast to grow 18.4% next year to total $304.9 billion, up from $257.5 billion in 2020, according to Gartner. The analyst says companies have more IT to do and less money to do it, so they will spend on areas such as the cloud to help accelerate digital business.

Gnarly elements of legacy IT remain in incumbent businesses of all sizes. If CIOs and their tech teams want to help their businesses change directly quickly, then they will need a cloud-first approach that allows the organisation to move with flexibility.

Agile development methodologies
Moving quickly, however, isn’t just about implementing the right technology. Without a people-centred information strategy that allows employees to exploit new opportunities collaboratively, then the promises of big data will go unfulfilled.

That’s where Agile development methodologies come in. Unlike traditional Waterfall approaches, which use a linear approach to task completion, Agile methods rely on iterative collaboration. While these techniques started in the IT department, Agile techniques have moved out of the data centre and out across all lines of business.

Smart companies use Agile techniques to bring in various stakeholders to explore how a business aim can be reached. By working together, cross-organisation teams can take small, iterative steps towards fulfilling data-led business aims in 2021.

Effective data management
Even if you’re moving towards completing business aims in an Agile manner, there’s still one area where your organisation can come unstuck: poor data management. It’s only possible to act on data if the information your business collects is consistent, accurate and secure.

Gartner says CIOs and their IT teams must as a matter of urgency prioritise spending on data management through 2021. The central role of data in the business is only going to increase, as companies rely on key performance indicators to ensure their organisation is making moves into the right market areas.

Your business will need to break down the traditional walls that define it. Rather than holding information in silos, CIOs will need to consolidate information so that decision-makers can make informed and intelligent choices.

Great account management
From the cloud through to data management, organisations needs to rely on the external partners they selects. Building trust is not necessarily straightforward, as CIOs have to use an ecosystem of IT providers with different capabilities for specific areas of provision.

With the business likely to up its demands for digital transformation through 2021, IT departments need partners that can deliver on their operational promises, rather than just hyping their offerings and selling more services when they believe the time is right.

Look for vendors that offer the highest possible level of account management. With IT budgets likely to remain constrained, tech teams need a trusted account managers that can work to help them deliver data-led digital transformation plans.

Summary
The coming months are going to involve building on some of the lessons learnt through 2020. CIOs should try to reflect, reset and reprioritise for the future.

In a power packed ‘AWS CXO’ panel that saw participation of Sourabh Chatterjee, President and Head – IT, Bajaj Allianz General Insurance Co, Balkrishna Singhania, EVP and Head – Digital, HDFC Life and Manish Verma, CTO, SonyLiv agreed on a common view that cloud computing is the most important technology for their digital transformations during the past five years.

The session was chaired by Puneet Chandok, President – Commercial Business, Amazon Internet Services Private Limited, AWS India and South Asia.

In his opening remark Chatterjee of Bajaj Allianz said, “At Bajaj Allianz we decided to move our core policy admin platform to a public cloud, and this is easier said than done, because we needed top-down commitment and other approvals, and we were fortunate to have that. So for us, agility and future-readiness was the primary driver of digital transformation. From a pure tech standpoint, besides the infra and provisioning, more importantly, we had clearly defined the business outcomes for each of the areas of transformation. I think the thing that worked for us and we’re just still working on, is a variable cost model, and the Covid pandemic times proved that fixed infra and high capex is not needed.”

Sharing SonyLiv’s story of digital transformation, Verma said, “We started our transformation journey sometime in December last year, and we put an aggressive target for ourselves to relaunch our application with the revised product and content positioning in the next four-five months. So you can imagine what it takes to launch 20 different applications across 20 different platforms. And we also had content in different types – GEC audience content is more organic, as compared to sports and live sports content with cricket and football, where we see spikes in traffic, and having a reliable tech backend and cloud infrastructure hence becomes important. To address these different types of traffic pattern and consumer behaviour, we had to use a reliable tech platform and ensure we were able to offer a seamless consumer experience across 20 different platforms. Since we launched, we have seen a 5x increase in subscription traffic and a 70-80 per cent increase in the traffic for content consumption.”

Highlighting the cloud journey of HDFC Life, Balkrishna Singhania stated, “We setup a separate team that focused on developing applications solely on the cloud. At some part earlier this year, as a part of our migration of our infrastructure. We did a ‘lift and shift’ of our core applications from on premise machines to the cloud, with the whole idea that if we wanted to be cloud-native and digital-native, we couldn’t afford to build patch-up systems where some part of it is on the cloud, and some cloud system is talking to on premise machines for data, and so on. Insurance is more B2B2C business than a B2C business. Enabling our partners was important – having our API installed in our partners’ systems, to enable them to query our systems for a particular customer, could become seamless with the cloud.”

The CIOs also shared their challenges and experiences during the pandemic. Responding to the big business focus area that tech and cloud will enable or drive in 2021, Chatterjee shared, “For us, it’s very clear the data and analytics piece, and all the modeling that we are doing around fraud, retention propensity, the entire claims experience, I think, across the value chain, anything that is data and insights. And I will be careful in using the term ‘analytics’, because in a lot of areas we use analysis and we incorrectly call it analytics, but the idea is, cloud will enable the entire data and insights as a capability within the organisation. This is something big for us and will be driven by the cloud.”

For SonyLiv, the focus is on harnessing the use of data. “We as an organisation, are digital and are using data in each and every decision that we make, whether it is on the infrastructure side, content programming, content production, churn analysis, retention – everywhere. I think it is all about data and democratisation of the data. We are working big time on introducing some of the prediction models, machine learning models, which can help us to retain users. So, I think data is going to play a critical role. The other area which I feel we as a business, is on the OTT side. As we have seen that especially cricket in India, each match is a new benchmark,” Verma highlighted.

Lastly, Singhania of HDFC Life mentioned, “Focus on making our applications intelligent is the next big thing, so data and analytics definitely comes there. And as a company where you have multiple touch points with customers, being able to take all the pieces of information and what customers need, where they need it, how they need it, and being able to give them in a nice and intelligent fashion, is the key according to me. When you add to technology a recommendation engine, propensity models, etc., you can make the systems intelligent. Making a lot of our systems intelligent is the key for us.”

Sharing some of the best practices for the Indian counterparts of BFSI sector, Chandok highlighted, “AWS work globally, and we’re the world’s largest and most broadly adopted cloud platform, including in financial services. And globally, we’ve got Capital One, Intuit, HSBC, Barclays – many large organizations building and scaling upon the cloud. And there’s a real network effect to this. And that’s the beauty of a cloud platform like AWS, where some of the largest organisations, not just enterprises, but even startups, small and medium businesses, government organizations are building on it and everything they build, you are able to democratize that and bring that to all your customers. The best practices very quickly get democratized, and we learn from each other. “

He also reiterated that, “India is leading from the front on this right. And you can see this in this conversation, two of the largest insurance in the country, are thinking big and bold and moving fast onto the cloud and leveraging it. And not just in financial services, virtually every vertical business segment today is being reinvented by the cloud, due to its cost effective, functionalities.”

DevOps is not a new tech for organizations any longer. The tech has been used consistently in the IT industry to improve development processes and increasing team productivity. However, organizations continue to commit mistakes that hurt the result and annoy the enterprise leaders and developers.

CIOs believe that DevOps still has significant scope to be honed, and is currently not matured enough. The COVID-19 situation has increased the pressure on enterprises to release software and updates at a faster pace.

Most enterprises are already implementing the vital elements needed to ensure the success of DevOps success. Industry leaders are in regarding the key tenets. Automation is vital; collaboration is essential and consistent practices are critical.

This includes continuous deployment/integration/testing and improvement.

CIOs believe that the main premise for continuous improvement is to proactively search for mistakes that are a hurdle to success and avoiding them.

Avoid overburdening developers
The majority of the CIOs have included digital transformation as part of their most urgent agenda for 2021. Most businesses expect to have a continuous stream of game-changing functionalities. They should be provided at high speeds and be ahead of the competition at all times.

Such a process involves efforts of multiple teams, from developers to testers, product owners, SREs, and ops. Developers are often the targets for disappointment when any of the much-required features fail to follow through.

Unless the organization is a very prominent one, they lack the luxury of having the most intelligent developers trying to be hired. Hiring and retaining experienced and valuable developers is a continuous battle for organizations.

Enterprise leaders believe that developers already have it hard when trying to satisfy the business’s need for software while focusing on development tasks. As a result, the strategy to “shift left” processes like security and testing needs to go beyond the traditional response of passing on responsibility to the developers.

CIOs agree that building both security and quality into applications right from the initial step is critical. This should be completed in a manner that doesn’t increase the pre-existing burden on developers. In the absence of this, the organization will lose its best development talent to another place that lets them focus on more creative work.

Excessive expectations on everyone
Enterprise leaders believe that each team member and organization can contribute to the DevOps success with a proper approach and relevant training and support. However, enterprises should be aware that every personnel can’t contribute in the same manner or to the same extent.

The majority of the early adopters of any process or technology end up being overachievers and superstars. They tend to track all upcoming trends in the said field and feed on constant motivation to do what is needed to make a solution work.

It is not easy to calculate such a success. Many application delivery personnel don’t work in this manner. They might excel in their field without necessarily having the primal urge or adventure to bring new solutions or tech to life. Different enterprise teams will have different comfort zones, skillsets, application stacks, compliance needs, and priorities.

The DevOps method that works for one domain will not always work for another. Setting expectations should be contextual and relevant to the skill set of the team or individual.

Launching the solution with proper analysis and insights into the user experience
In the current scenario, both organizations and end-users have a very low tolerance for any factor that can be perceived as a problem. C-suite leaders acknowledge that even the smallest improvements in the processes to boost customer experience can significantly impact its final bottom line. Similarly, a negative impact can result in drastic revenue loss.

CIOs explain that everyone on the development team wishes to develop and launch the relevant and useful tools. One team can have multiple different strengths, weaknesses, and perspectives.

It is vital that every team member understand the requirement and involvement in planning, testing, releasing, and monitoring. This ensures that they completely understand the business and can consistently defend the comprehensive user experience.

DevOps teams are often reliant on base-level technical checks to detect if the release candidate is good to launch. These tests are good to identify coding issues; however, more exceptional checks are required to ensure the delivery of an expected experience that ultimately gives the business an edge.

CIOs say that enterprises should opt for risk-based testing, end functional testing of the transactions, and perform performance/load testing to ensure the application can meet a sudden increase in demand.

The Coronavirus pandemic has catapulted digital transformation to the top of the must-haves in the minds of CIOs. It has been a go-to enabler for organizations to enhance their customer experience and generate higher ROI. Businesses with brick-and-mortar presence were shifting their approach towards building a digital presence to stay relevant and ahead of their competition.

Many have already begun their digital transformation journey, while some are in the designing phase. Technology landscape is dynamic in nature. While the pandemic forced businesses to adopt digital drivers such as automation, big data, digital workforce, etc., they were soon replaced with the next big wave of digital transformation. This new wave has brought in new trends that will shape up the next normal. Let’s look at 5 such trends that will soon become the next must-haves.

Customer Experience Reimagined
It’s not just about having best products anymore. The new wave of millennials prefers convenience over loyalty. Create digital experiences that support modern web in the form of progressive web apps, true native mobile apps with best-in class UX, intelligent chat-bots, conversational apps, wearables, and immersive experiences that leverage augmented reality. Organizations are trying to maximize the use of components and backend integration that leads to a unified and seamless experience across channels.

The next big thing in Customer experience (CX) is to design amazing applications for any audience across any device with no limits on user experience taking full advantage of the underlying device OS for native and full power of modern browsers for web. Omni-channel tools eliminate data siloes and bring all the data at one place. Be it a web presence, a physical store visit, or a mobile application click, omni-channel binds all customer-facing interfaces together, integrated at the back-end, and helps in better CX and data visibility across the board.

Data and User Privacy
In the wake of privacy concerns amongst digital users, we will be seeing organizations focusing on user and data privacy. This will soon become a unique selling proposition for many to leapfrog their competitors. This could mean that users in the next normal will be able to allow/deny access to their data residing on applications, web and other digital channels. The user will be the owner of his/her data. Data privacy has shaken even multi-national brethren, forcing them to comply with new regulations laid by authorities. EU has already entered the game with General Data Protection Regulation (GDPR) compliance policy and other regions have followed suit.

APIs – The Road to Cloud
Cloud is already adopted by most organizations to create seamless business continuity and fasten go-to market. Application programming interfaces (APIs) are key in building economies of scale. From converting legacy infrastructure to micro-services, APIs will help businesses to reduce data silos and create collaborative experiences. It will also help them convert scattered applications into one behemoth of a data mart, helping them in making critical business-decisions and increase their overall ROI.

XaaS (Everything-as-a-Service)
As-a-Service (aaS) has already become the norm to become a truly digital-native enterprise. The new trend in the aaS model is Everything-as-a-Service (XaaS) where services rendered and delivered will completely reside on the cloud with virtual access to almost everything. Tools such as Internet of Things (IoT) and Artificial Intelligence (AI) will play a crucial role in building those services, or augmenting existing services to achieve the digital-native status quo.

AIaaS (Artificial Intelligence-as-a-Service)
AI technologies are already used by 77% of consumers. A number of technological innovations such as data processing, and face & speech recognition have become possible due to AI. AI is a vast area that includes AI platforms, chat-bots, machine learning (algorithm category consisting of various libraries and frameworks), deep learning and analytics.

AI has led to rise in the use of machine learning in computing. AI has contributed significantly in creating a framework for cloud computing. Many IT industry giants are already betting huge on AIaaS (Artificial Intelligence as a Service) with new line of services such as digital assistance, cognitive computing, machine learning, business intelligence, and so on.

These services can have significant impact on day-to-day lives of the consumers. With the help of vital statistics from health-care organizations, AI can help identify patterns and predict the desired outcome of a given situation in a focused environment. AI can also help in tracking of smart devices, thereby, creating a footprint of individuals. It can help marketers and advertisers with predictive data on what one is likely to do in a given event. This will help marketers to show relevant ads to consumers at the right time that could help them fetch better returns for their marketing dollars.

Needless to say, the new wave of digital technology trends can reduce human interactions, increase productivity, initiate automation, and simplify lives. Organizations need to understand the importance of this technology paradigm shift, and leverage it to create digital ecosystems and become future-ready enterprises to welcome the next normal with both hands.

Why Collaboration Remains a Core DevOps Challenge

Despite widespread tool adoption, many organizations still struggle with the human and process gaps that fragment the software delivery lifecycle. Common friction points include:

• Complex environment sprawl: Provisioning and configuring new environments manually is costly, slow, and error-prone—especially when configurations diverge across dev, test, and production.
• Risky manual promotions: Moving code between environments without automation introduces inconsistencies that can trigger outages.
• Dev vs. Ops culture clash: Development teams optimize for change velocity while operations teams optimize for stability. Without shared tooling and visibility, this tension fosters a blame culture rather than shared ownership.
• Synchronization gaps: Manual processes cannot keep dev, test, and production environments consistent at scale, which is a leading cause of production incidents.

Bots address many of these gaps by acting as an automated intermediary across the entire delivery lifecycle, reducing the manual handoffs that introduce risk and delay.

What Are ChatOps and Bots?

ChatOps is a term popularized by GitHub to describe the practice of “putting tools in the middle of a conversation.” Rather than requiring engineers to switch between a chat client and a dozen separate tool dashboards, ChatOps surfaces tool actions and outputs directly inside the chat interface the team is already using.

A typical CI/CD pipeline involves many discrete tools: source code management, defect tracking, continuous integration, continuous deployment, and monitoring systems. These tools rarely talk to each other natively. ChatOps bridges that gap.

The three core components of a ChatOps architecture are:

• Chat app / collaboration tool: The front-end interface (such as Slack or Microsoft Teams) where team members send commands and receive responses. This is the single pane of glass for both human conversation and automated tool interaction.
• The bot: The engine at the center of ChatOps. The bot receives commands typed in natural language or structured syntax, interprets them, and executes the corresponding action against a target system. Well-known bot frameworks include Hubot, Lita, Errbot, and cloud-native options such as AWS Lex.
• Target platforms and DevOps tools: The downstream systems the bot interacts with—CI servers, deployment orchestrators, monitoring platforms, issue trackers, and infrastructure provisioning tools. The richness of these integrations determines what the bot can actually do.

Where Bots Fit in the DevOps Pipeline

Bots can accelerate every stage of the software delivery lifecycle. The table below maps common pipeline phases to the tools bots can integrate with:

Beyond pipeline automation, bots handle a wide range of routine operational tasks that would otherwise consume valuable engineer time:

• Cleaning up temporary files or stale resources on servers.
• Triggering build and deployment jobs on demand or on a schedule.
• Rotating and archiving server logs.
• Onboarding new applications into monitoring platforms.
• Collecting and reporting metrics from tools such as Nagios or AppDynamics.
• Bringing multiple teams into a shared channel to triage and resolve incidents collaboratively.

Bot Technical Architecture

A practical bot implementation typically involves the following layers:

1. Chat platform hosting: The bot process runs alongside or connects to your chat platform (for example, as a Slack app). This can be hosted in the cloud or on-premises, depending on your security requirements.
2. Command parser: The bot’s logic layer interprets incoming chat commands. This can range from simple keyword matching to natural language processing depending on the bot framework chosen.
3. Response / configuration store: A key-value data store maps commands or identifiers (such as application names or environment IDs) to the responses and actions the bot should execute. This store is populated when you configure the bot’s capabilities.
4. Tool integrations: The bot calls the APIs or CLIs of target DevOps tools (Jenkins, Ansible, Jira, Nagios, etc.) and returns structured output back to the chat channel.

The tighter the API integration between the bot and downstream tools, the more powerful and reliable the automation becomes. Poorly documented or unstable APIs in target tools are the most common source of bot implementation friction.

DevOps Bot Use Cases Across the Full Lifecycle

Proactive Planning

Create new user stories directly from the chat window, assign them to sprints, and update product backlogs—without leaving Slack or Teams. Sprint planning status can be queried and surfaced at a glance for the whole team.

Build and Continuous Integration

Trigger Jenkins builds with a chat command, receive pass/fail notifications in the team channel, and run scheduled nightly builds with automated status reports distributed to relevant stakeholders.

Continuous Deployment

Execute deployment jobs across environments, check deployment status, and initiate rollbacks if failures are detected. Post-deployment actions such as JVM restarts or cache flushes can be automated as part of the same bot workflow.

Infrastructure Provisioning and Configuration Management

Invoke Chef or Ansible playbooks from chat to provision new environments, restart services, or apply configuration changes—turning what was a multi-step manual process into a single command.

Continuous Monitoring

Query application performance metrics, retrieve health check status, or pull daily summaries from monitoring tools directly within the team channel. Alerts can also be routed to the relevant chat channel so the right people see them immediately.

Continuous Feedback

Monitor application health trends and surface analytics to the team on a regular cadence, helping teams identify degradation before it becomes an incident.

Measuring the Impact of DevOps Bots

Introducing bots without measuring their impact makes it difficult to justify further investment or identify where automation is falling short. Three key metrics to track are:

• Effort reduction: Measure the reduction in engineer-hours spent on manual tasks such as triggering builds, running deployments, or pulling metrics from dashboards. This is most easily captured by comparing time-per-task before and after bot adoption.
• Human error reduction rate: Track the decrease in incidents or rework caused by manual mistakes—misconfigured deployments, missed steps, incorrect environment targets. Fewer errors mean less time spent on remediation.
• Issue triage time: For incidents that require multiple teams, measure how long it takes from alert to active collaboration. Bots that automatically pull relevant teams into a shared channel and surface diagnostic data can dramatically cut this window.

Conclusion

DevOps bots are not just a productivity novelty—they are a practical mechanism for reducing the manual friction that slows delivery pipelines and erodes team cohesion. By embedding automation into the chat tools teams already rely on, bots reduce context switching, lower error rates, and accelerate incident response. Organizations that invest in ChatOps build a foundation for faster, more reliable software delivery while simultaneously improving the day-to-day experience of their engineering and operations teams.

The key to success is integration depth: the more seamlessly your bot connects to your actual toolchain, the more value it delivers. Start with a single high-friction workflow—such as triggering builds or querying monitoring data—and expand from there as the team builds confidence in the approach.

Why DevOps Matters More Than Ever in the Cloud Era

As software-as-a-service (SaaS) became the dominant delivery model for enterprise computing, the pressure on development teams to ship faster and more reliably grew dramatically. DevOps—the cultural and technical practice of unifying software development and IT operations—emerged as one of the most effective strategies for meeting that demand.

Rather than treating development, QA, and operations as separate silos, DevOps organizations use automation, continuous integration and delivery (CI/CD), infrastructure-as-code, and shared accountability to shorten release cycles, reduce failures, and recover faster when things go wrong. For companies migrating to or building natively on the cloud, these practices are no longer optional—they are foundational.

Core DevOps Practices Driving Modern Software Teams

To understand the breadth of DevOps today, it helps to look at the specific practices and disciplines that define it in production environments:

• Continuous Integration (CI): Developers commit code to a shared repository multiple times per day. Automated builds and tests run on every commit, catching integration issues early.
• Continuous Delivery/Deployment (CD): Code that passes automated testing is automatically prepared—or even deployed—to production, reducing manual handoffs and release risk.
• Infrastructure as Code (IaC): Server provisioning and configuration are defined in version-controlled code, making infrastructure reproducible and auditable.
• Observability and Monitoring: Teams instrument applications and infrastructure to collect metrics, logs, and traces, enabling faster incident detection and root-cause analysis.
• Open Source Tooling: Much of the modern DevOps toolchain—Kubernetes, Prometheus, Terraform, Chef, and others—is open source, lowering barriers to adoption and encouraging community innovation.

Five Practitioners Shaping Modern DevOps

The following five professionals represent a cross-section of DevOps expertise—from platform engineering and open source tooling to cloud-native infrastructure and observability. Their backgrounds illustrate how DevOps spans roles, industries, and technology stacks.

Tara Hernandez — Engineering Manager, Google Cloud

Hernandez is recognized as an early advocate of continuous integration. CI is a discipline that requires developers to merge their changes into a shared codebase frequently—often multiple times per day—with automated validation at every step. At scale, this practice dramatically reduces the cost and complexity of integrating large teams’ work, and it forms the backbone of modern CI/CD pipelines.

Nell Shamrell-Harrington — Chef (now Progress Chef)

Shamrell-Harrington works at Chef, one of the pioneering companies in infrastructure automation and configuration management. A significant milestone in Chef’s history was its decision to make all of its core product code open source—a strategic move that reflects a broader industry trend toward open, community-driven tooling. Open-sourcing infrastructure tools lowers vendor lock-in risk and allows practitioners to contribute fixes and features directly.

Illya Chekrygin — Founding Engineer, Upbound

Chekrygin brings hands-on cloud migration experience from a high-profile project at HBO, where he was involved in moving the company’s streaming service to Amazon Web Services using Kubernetes as the container orchestration layer. This type of large-scale migration—taking a latency-sensitive, high-traffic consumer application and re-platforming it onto managed cloud infrastructure—is one of the most complex challenges in modern DevOps. He later co-founded Upbound, a Seattle-based startup focused on cloud-native infrastructure tooling.

Lee Atchison — New Relic

Atchison has spent years working on cloud strategy at New Relic, a company that provides application performance monitoring (APM) and observability tooling. Observability—the ability to understand a system’s internal state from its external outputs—is a cornerstone of mature DevOps practice. Teams that can measure what their systems are doing in production can iterate faster, respond to incidents more confidently, and make data-driven architectural decisions.

Idit Levine — Co-founder and CEO, Solo.io

Levine focuses on helping organizations adopt cloud-native technologies such as Kubernetes and serverless computing. Solo.io builds tooling around service mesh and API gateway patterns—infrastructure that manages how microservices communicate securely and reliably. As organizations decompose monolithic applications into distributed services, this layer of the stack becomes increasingly important for maintaining reliability, security, and observability across complex environments.

Key Takeaways for DevOps Practitioners

Looking across these five practitioners, several themes emerge that are relevant to any engineering team investing in DevOps today:

• CI/CD is table stakes. Continuous integration and delivery are no longer differentiators—they are the baseline expectation for competitive engineering teams.
• Open source tooling dominates. The most widely adopted DevOps tools are open source. Understanding contribution models and community health is part of evaluating tooling choices.
• Cloud-native migrations require operational discipline. Moving to Kubernetes and managed cloud services demands deep understanding of networking, storage, and reliability engineering—not just containerization.
• Observability is a force multiplier. Teams that instrument their systems well move faster and recover from incidents more efficiently than those operating blind.
• Service mesh and API management are maturing fast. As microservices architectures become common, the infrastructure connecting those services—including service meshes—is an active area of tooling development.

The Broader DevOps Landscape

DevOps is not a product you purchase or a certification you earn—it is an ongoing organizational and technical practice. The practitioners highlighted here represent different entry points: tooling, platform engineering, observability, cloud migration, and emerging infrastructure patterns. What they share is a focus on reducing friction between the act of writing software and delivering reliable value to users.

For engineering leaders evaluating where to invest, the message is consistent: automation, observability, open standards, and cultural alignment between development and operations teams remain the highest-leverage areas for improving software delivery performance.

Original reporting by GeekWire. Speaker details reflect information available at the time of the 2019 GeekWire Cloud Summit announcement.

CIOs and other leading tech and IT business leaders explain how they’re using DevOps to

Where CIOs Stand on DevOps

A TechRepublic CIO Jury survey asked IT leaders whether they had implemented DevOps or had concrete plans to do so. Results were split evenly. The six leaders on the pro-DevOps side cited improved team communication, smoother testing cycles, increased productivity, and faster time to market as primary drivers. Those on the other side reported that they were either not organizationally ready or remained unconvinced of DevOps’s practical value for their context.

That split reflects a genuine tension in enterprise IT. DevOps is not a plug-and-play solution — it requires deliberate investment in tooling, process, and people. The resources below help CIOs and IT leaders assess the opportunity, avoid common traps, and navigate the culture question that almost always surfaces during adoption discussions.

1. The Case for DevOps: Where It’s Going and How to Get Value From It

Source: ZDNet — In a piece by Mark Samuels, several IT leaders articulate the tangible upside of a DevOps approach. Paul Pogonoski, Director of Cloud Foundation Services at Capgemini, summarised the benefit of iterative development: organisations that embrace it can break work into smaller increments, fail fast, and create a structured environment for experimentation and learning. These are not abstract principles — they translate directly into reduced release risk and shorter feedback loops between development and operations teams.

Key themes from this coverage include:

• Iterative delivery: Smaller, more frequent releases reduce blast radius when things go wrong.
• Fail fast culture: Teams surface problems earlier in the development cycle, when fixes are cheaper.
• Experimentation: DevOps toolchains and practices give teams the safety net to try new approaches without risking production stability.
• Improved collaboration: Breaking down silos between dev, ops, and QA leads to shared ownership of reliability and quality.

2. Nine Mistakes DevOps Teams Make — and How to Avoid Them

Source: InformationWeek — Cynthia Harvey provides a practical counterpoint: DevOps benefits are not automatic. Her article identifies nine common mistakes that can undermine even well-intentioned DevOps initiatives. These pitfalls are worth reviewing before a rollout, not after.

Two of the most frequently cited mistakes are:

• Prioritising speed over quality: Continuous delivery pipelines can create pressure to ship faster, but without adequate automated testing and quality gates, velocity becomes a liability rather than an asset.
• Neglecting culture change: Tooling alone does not create DevOps. If development and operations teams still operate with adversarial mindsets or misaligned incentives, automation only accelerates dysfunction.

Other commonly flagged mistakes include insufficient monitoring and observability, failing to involve security teams early (a precursor to the DevSecOps model), and treating DevOps as a one-time project rather than an ongoing operational discipline.

3. Which Comes First — DevOps or Culture Change?

Source: InformationWeek — Eric Bruno addresses one of the most debated questions in DevOps adoption: does cultural change need to precede the implementation of DevOps practices, or does adopting DevOps tooling and workflows organically drive cultural change over time?

Bruno’s conclusion is nuanced and practically useful: rather than treating cultural change as a prerequisite gate or a downstream outcome, organisations should treat it as a continuous improvement target — one that evolves alongside, not before or after, technical adoption.

This framing is important for CIOs who feel they cannot begin a DevOps programme because their culture “isn’t ready.” The evidence suggests that waiting for a perfect cultural moment is itself a mistake. Starting with a bounded pilot — a single product team, a single pipeline — and using the results to demonstrate value is often a more effective catalyst for cultural change than any top-down mandate.

Key Takeaways for CIOs Evaluating DevOps

• The business case is real but context-dependent. Improved time to market, higher deployment frequency, and better cross-team communication are consistently cited benefits — but they require deliberate organisational investment to realise.
• Common mistakes are avoidable. Auditing your team’s readiness against known failure patterns before you start saves significant remediation effort later.
• Culture and tooling are co-evolutionary. Do not use cultural immaturity as a reason to delay; use a phased adoption approach to evolve both together.
• Security and quality must be built in. Speed without quality gates and security integration creates technical debt and risk, not competitive advantage.

For IT leaders still assessing whether DevOps is the right move, these three perspectives — the value case, the risk inventory, and the culture question — form a solid starting framework for a structured evaluation.

One technology that has gained significant traction is the software container. For CIOs evaluating where to direct engineering investment, understanding what containers actually are, what they genuinely solve, and where they fall short is essential before signing off on a platform migration or a new toolchain budget.

What Is a Software Container?

A container is a lightweight, standalone, executable package that includes everything needed to run a piece of software: the code, runtime, system libraries, and configuration. Unlike traditional virtual machines (VMs), containers share the host operating system kernel rather than running a full OS per instance. This makes them faster to start, cheaper to run at scale, and easier to move between environments.

The practical result is that a containerized application behaves the same way on a developer’s laptop, in a continuous integration pipeline, and in a production cloud environment. That consistency closes one of the most persistent gaps in enterprise software delivery: the “works on my machine” problem.

Why Containers Matter to the Enterprise

According to Jay Lyman, principal analyst at 451 Research (as quoted in a 2017 CIO Dive article), the core enterprise advantages of containers are speed, simplicity, and efficiency — particularly in helping developers move faster, onboard more quickly, and build applications for the cloud. He noted that containers allow organizations to “more simply create and destroy infrastructure and applications as needed.”

Chip Childers, then CTO of Cloud Foundry, framed containers within a broader strategic context: organizations adopting containers see them as part of a larger shift toward continuous delivery, greater agility, and operational flexibility.

Brian Gracely, then director of product strategy at Red Hat, identified several concrete use cases enterprises were already pursuing:

• Modernizing existing legacy applications
• Building new cloud-native applications from scratch
• Accelerating big data pipelines and analytics workloads
• Exploring Internet of Things (IoT) opportunities

The open source ecosystem — including projects like Cloud Foundry, Kubernetes, and Docker — has accelerated the expansion of container use cases significantly, because improvements created by one organization benefit the entire community.

Containers vs. Virtual Machines: Coexistence, Not Replacement

One of the most important things CIOs need to understand is that containers do not yet replace virtual machines in all scenarios. VMs have been in enterprise production for decades and carry a mature ecosystem of tooling, support, compliance frameworks, and operational knowledge.

Containers do not offer full feature parity with VMs in every dimension. Strong VM isolation, mature snapshot and backup tooling, and decades of compliance-ready configurations still give VMs an edge in certain regulated or legacy contexts. Lyman noted that what enterprises should expect is a period of mixed use and coexistence, with containers progressively displacing VMs for specific workload types — not a wholesale replacement.

Containers Are Not Cloud-Dependent

Because containers emerged around the same time many enterprises began cloud migrations, the two technologies are often conflated. This is a common misconception worth correcting explicitly.

As Lyman put it: “Adopting container technology doesn’t have to be dependent on moving from on-premises to cloud infrastructure. There are container platforms out there that allow for deployment on-prem as well as on any cloud, hybrid or public.”

CIOs should evaluate container platforms on their own merits — portability, developer productivity, CI/CD integration — rather than treating container adoption as synonymous with a cloud migration project.

Where to Start: Challenges and Practical Guidance

Many organizations find the entry point for containers unclear. The technology ecosystem around container engines, orchestration platforms, networking, and storage continues to evolve rapidly, which can make it difficult to know where to commit.

Developer-Led Adoption Creates Operational Gaps

In practice, containers are typically adopted first by developers seeking faster build-and-test cycles. Operations teams are then left to address the resulting questions around networking, security, persistent storage, high availability, and compliance. Gracely described this reactive pattern as a source of friction that CIOs should proactively address.

A more effective approach, he argued, is to treat container adoption as a platform discussion — one that brings development and operations together from the start, with a shared focus on accelerating software delivery safely.

Legacy Application Migration Is Hard

Container technology is best suited to greenfield projects. Migrating existing monolithic or tightly coupled applications to containers can be costly and time-consuming. CIOs should be realistic about the effort involved and prioritize new workloads and services as initial container candidates, rather than attempting to lift-and-shift legacy systems wholesale.

Platform Selection

A recurring challenge for enterprises — particularly mid-sized organizations — is that many commercially available container platforms are designed and optimized for massive-scale deployments. CIOs should evaluate whether a given platform fits their actual scale and operational maturity, not just the largest reference customers the vendor promotes.

Containers Are Not a Panacea

Containers are a tool, not a strategy. Childers was direct on this point: “They could be really key in streamlining [software delivery], making it more efficient and eliminating the difference between development and production departments, which frequently is a major problem. But they’re not a panacea. They’re technology that you can use as part of changing the way that your company operates and uses technology.”

CIOs should evaluate containers within the broader context of their software delivery lifecycle — alongside CI/CD pipelines, DevOps culture, infrastructure automation, and observability tooling. Containers accelerate delivery when the surrounding process and culture are ready to support them.

Key Decisions for CIOs Moving Forward

If your organization is considering a container initiative, several practical considerations apply:

• Control costs rigorously. Mark Balch, then VP of product at Diamanti, advised ensuring that both capital and operational expenditure for the container stack — software and underlying infrastructure — are competitive with public cloud equivalents. Traditional enterprise virtualization infrastructure is often too expensive in time, money, and training overhead to simply bolt containers on top of.
• Choose commercially supported platforms. Childers’ advice was unambiguous: do not attempt to build and manage your own container system. “Creating your own platform is extremely difficult and risky and probably not the best use of time for your valuable engineers. Focus your top engineers on the hardest business problem, not the hardest plumbing problems.”
• Start with testing, development, and greenfield workloads. Production adoption for mission-critical applications requires additional investment in security hardening, persistent storage strategies, and operational runbooks. A phased approach reduces risk.
• Plan for organizational change. Container adoption often surfaces cultural and process gaps between development and operations. CIOs should budget for training, workflow redesign, and cross-functional alignment — not just tooling.

The Outlook

Containers have an established and growing role in enterprise software delivery. Adoption has continued to expand since this topic first gained broad executive attention, with Kubernetes now the dominant orchestration platform and container-native tooling maturing across security, networking, and observability. That said, the technology is not universally applicable, and the hype that surrounded early container discourse has given way to a more grounded understanding of where containers genuinely add value.

For CIOs, the right posture is informed pragmatism: understand what containers solve, invest in the platform and people decisions that support them, and integrate them into a broader strategy for continuous delivery and operational efficiency — rather than adopting them as an end in themselves.

This article draws on reporting originally published by CIO Dive in 2017, with quotes from Jay Lyman (451 Research), Chip Childers (Cloud Foundry), Brian Gracely (Red Hat), and Mark Balch (Diamanti).

The Digital Imperative Facing Today’s CIOs

Companies across every industry are under mounting pressure to revamp business operations in order to better serve customers in a digital-first world. In response, CIOs are making sweeping organizational changes: adding new strategic roles, standing up innovation labs, and deploying modern technologies to fulfill mandates handed down by their CEOs and boards.

Social, mobile, analytics, and cloud — collectively abbreviated as SMAC — form the primary digital foundation for most IT organizations. Beyond SMAC, forward-looking CIOs are also evaluating and piloting technologies in artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and blockchain. Used strategically, these technologies carry real potential to fundamentally reshape business processes.

The challenge, however, is avoiding technology for technology’s sake. Deloitte has described the goal as a new “unbounded IT” operating model — one in which IT departments align tightly with business strategy rather than operating as a back-office function. According to McKinsey & Co. research cited at the time, industries remained less than 40% digitized on average, underscoring how much transformation work remained ahead.

“The leading CIOs are finding the right spots in their organization to make strategic hires to drive transformation. And they’re creating an environment where it’s OK to innovate and use that to see how it shapes the agenda. They’re doing all of that in a space that’s wrapped around the customer experience you want to deliver.”

— Victor Fetter, CIO, LPL Financial

Victor Fetter, CIO of LPL Financial: Customer Experience at the Center

LPL Financial supports 700 banks and credit unions and more than 14,000 financial advisors. CIO Victor Fetter has organized the IT function around improving how LPL serves those clients at every touchpoint.

Customer Experience Center

Fetter established a dedicated customer experience center where team members interact directly with financial advisor clients to understand workflow pain points. Findings are reported to LPL’s business groups, which then adjust customer-facing tools, optimize workflows, and enhance data insights based on real feedback.

Strategic Hires: DevOps Lead and Data Lead

Recognizing that transformation requires the right people in the right roles, Fetter made two targeted hires:

• DevOps point person: Responsible for compressing time-to-market for software delivery by introducing DevOps practices across engineering teams.
• Data lead: Tasked with normalizing, extracting, and surfacing data in formats usable by business units and clients — turning raw data assets into actionable intelligence.

“It’s about finding those key roles that you want to have in your organization to really drive change,” Fetter said.

Innovation Lab and Emerging Technology Experiments

When LPL relocated to a new facility in Fort Mill, S.C., Fetter created an innovation lab where engineers can prototype and test emerging technologies in a low-risk environment. One experiment involved integrating Amazon Alexa and other voice assistants to support balance inquiries, trade lookups, and transaction queries — exercises that forced engineers to think carefully about how to rewire service layers so that voice interfaces could connect to LPL’s back-end systems.

LPL was also prototyping a digital dashboard for customer service representatives. When a call arrives, the dashboard surfaces the caller’s identity, applies sentiment analysis to gauge the caller’s tone, and pulls up relevant transaction history — giving the representative context before the conversation even begins. “I think that has the potential to be a game-changer in freeing up time by using information in different ways to provide better service,” Fetter said.

Cultural initiatives — including hackathons and guest speakers such as Constellation Research’s Ray Wang — rounded out an environment that rewards experimentation rather than punishing failure.

Yousuf Khan, CIO of Pure Storage: Running Lean with Cloud and Automation

Pure Storage CIO Yousuf Khan faced a different constraint: a competitive Silicon Valley talent market that made hiring traditional software engineers difficult and expensive. His response was to build a deliberately lean IT function and compensate through cloud software and automation.

A Small Team, a Large Cloud Stack

At the time of reporting, approximately 30 IT staff supported 1,700 employees — a ratio most enterprise IT leaders would consider extremely lean. Khan achieved this by running the business almost entirely on SaaS platforms, including Salesforce.com, Workday, NetSuite, and Marketo, supplemented by a data warehouse built on Pure’s own flash storage technology.

Automation Over Headcount

Rather than compete for scarce software development talent, Khan worked with venture capitalists to identify early-stage software startups building IT automation tools. One example was exploring chatbot-driven help desk ticketing within Slack — allowing employees to create and resolve tickets conversationally, reducing manual overhead for the IT team.

The resulting bandwidth freed his team to focus on data analytics that directly supported business outcomes. “CIOs are business enablers more than ever before,” Khan said.

Cybersecurity as a Talent and Strategy Problem

Khan identified cybersecurity — particularly social engineering attacks such as phishing — as a rapidly growing risk vector. His challenge was twofold: selecting effective tools from a crowded and often confusing security vendor landscape, and finding qualified staff capable of operating those tools.

His approach to the talent gap was internal upskilling: offering existing IT staff the opportunity to train for more specialized cybersecurity roles, including new industry certifications. “Security is one of those areas where every CIO wants to build up their skill set,” Khan said.

Kevin Murray, CIO of Cincinnati Bell: Replacing Legacy Systems with SaaS

Modernizing IT at a telecommunications carrier carrying decades of legacy ordering, billing, and provisioning systems is one of the most difficult transformation challenges in enterprise IT. Cincinnati Bell CIO Kevin Murray was in the middle of exactly that effort.

SaaS as the Default Path

As part of a broader software modernization strategy, Cincinnati Bell was replacing or augmenting homegrown custom software with commercial SaaS applications from vendors including Salesforce.com, TOA, Callidus, Microsoft, and Xactly. The guiding principle was straightforward: “We’ve essentially said SaaS is our preferred path,” Murray said.

FinancialForce Migration: Speed Compared to On-Premises ERP

A concrete example of the SaaS-first strategy: Cincinnati Bell migrated roughly 1,100 employees in its professional services organization to FinancialForce, an ERP product hosted on Salesforce.com. The migration from custom software to FinancialForce took approximately four months — a timeline Murray contrasted with the 12-to-24-month cycles typically required for on-premises ERP deployments.

Additional benefits included automatic periodic upgrades and improved mobile usability, both difficult to achieve quickly with on-premises systems. “It’s a lot faster to implement,” Murray said.

Common Themes: What Digital Transformation Looks Like in Practice

Across all three CIOs, several consistent patterns emerge for IT leaders navigating digital transformation:

• Hire strategically, not just broadly. Targeted roles — DevOps leads, data leads, security specialists — drive more focused change than general headcount growth.
• Align technology choices with business outcomes. Whether it’s a customer experience center, a voice-enabled virtual assistant, or a chatbot-driven help desk, the technology serves a defined business need.
• Prefer SaaS and cloud for speed and flexibility. On-premises custom systems are giving way to commercial cloud platforms that reduce implementation time and maintenance burden.
• Build a culture of experimentation. Innovation labs, hackathons, and internal prototyping programs give teams permission to test ideas before committing to full-scale deployments.
• Treat security as a strategic priority, not an afterthought. Cybersecurity skill-building must be proactive, particularly as social engineering attacks grow more sophisticated.
• Use automation to extend lean teams. Cloud-native automation tools allow smaller IT organizations to support larger employee bases without proportional headcount growth.

Digital transformation is not a single project with an end date — it is an ongoing realignment of people, processes, and technology around the needs of customers and the business. The CIOs described here illustrate that the most effective approaches combine cultural change with deliberate technology choices and purpose-built organizational structures.

Original reporting by CIO.com / ITworld.com, 2017.