Digital Transformation and the Procurement Imperative

Digital transformation is no longer optional for modern organisations, whether public or private. At the heart of this shift is the need to harness new technologies — including cloud computing — to unlock socio-economic benefits, improve efficiency, and meet rising user expectations.

Cloud computing, broadly defined as storing and accessing data and programmes over the internet rather than on local hardware, holds particular promise for procurement and supply chain functions. It can reduce IT expenditure, boost organisational agility, and expand the scope for delivering high-quality services at scale.

Barriers to Cloud Adoption in Procurement

Despite its promise, cloud adoption in procurement faces several well-documented obstacles:

• Procurement process misalignment: Legacy procurement workflows are often built around capital expenditure models, while cloud services operate on an on-demand, subscription basis — requiring new approval and budgeting frameworks.
• Security and trust concerns: Organisations remain cautious about storing sensitive vendor, contract, and financial data in third-party cloud environments.
• Lack of mature technical standards: Interoperability between cloud platforms and existing enterprise systems (such as ERP tools) is still inconsistent across vendors.
• Complex legal terms and vendor lock-in risk: Long-term dependency on a single cloud provider raises concerns about data portability, contract flexibility, and exit costs.
• Skills gaps: Procurement professionals may lack the technical background needed to evaluate, implement, and manage cloud-based supply chain tools effectively.

Overcoming these barriers is essential to improving public and private sector productivity, meeting evolving user demands, and ensuring services remain secure, reliable, and compliant with institutional requirements.

From ERP Systems to Cloud-Native Procurement Platforms

As supply chains grow more complex and products more sophisticated, the procurement function must evolve. Industry analysts have observed a shift away from monolithic, ERP-built reporting tools toward lighter, app-driven user interfaces and cloud-based analytical platforms. These solutions allow procurement teams to purchase through enterprise app stores, access real-time data dashboards, and collaborate across organisational boundaries without being constrained by on-premises infrastructure.

This shift mirrors broader trends in the digital economy, where real-time information access, mobile internet expansion, and affordable cloud capacity are making vertically integrated, connected supply chains increasingly viable — even for mid-sized organisations.

The Role of SaaS in Supply Chain Modernisation

Software as a Service (SaaS) is one of the most impactful cloud delivery models for procurement. In a SaaS model, a third-party provider hosts applications and makes them accessible to customers via the internet — eliminating the need for organisations to manage underlying infrastructure or perform software maintenance themselves.

SaaS sits alongside Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) as one of the three primary cloud service categories. Key advantages for procurement teams include:

• Reduced IT support costs: Hardware and software maintenance responsibilities transfer to the SaaS provider.
• Faster deployment: SaaS platforms can often be configured and rolled out far more quickly than custom-built or on-premises alternatives.
• Information sharing across organisations: Cloud-based solutions facilitate collaboration and data sharing within global supply chains, even between separate entities.
• Scalability: Usage can be scaled up or down based on business needs without significant capital investment.

Powerful SaaS procurement platforms can deliver value — particularly in analytics, supplier relationship management, and procure-to-pay automation — that many in-house solutions cannot match at equivalent cost.

Automating the Procure-to-Pay Process

One of the most tangible benefits of cloud adoption in procurement is the automation of the procure-to-pay (P2P) cycle — the end-to-end process from identifying a purchasing need through to payment of the supplier. Cloud-based P2P platforms reduce time-consuming manual tasks such as purchase order creation, invoice matching, and approval routing, freeing procurement professionals to focus on strategic activities like supplier development and risk management.

When implemented correctly, cloud-enabled procurement tools can also reduce supply chain disruption risk and strengthen supplier relationships — both critical components of a resilient corporate network.

Real-World Example: Flex Pulse

Flex, an American multinational electronics manufacturing services company, developed an internal software-based mobile collaboration tool called Flex Pulse. The platform is designed to monitor all aspects of Flex’s global supply chain in real time, providing up-to-the-minute data analysis and aggregating global events — such as geopolitical disruptions, weather events, or logistics bottlenecks — that could affect supply chain performance.

Flex Pulse illustrates how large-scale manufacturers are embedding cloud and data analytics capabilities directly into procurement and supply chain operations to gain visibility and react faster to changing conditions.

Building the Right Skills and Partnerships

Because procurement teams will often be responsible for selecting, deploying, and governing cloud-based supply chain solutions, this transition demands new competencies. Organisations should consider:

• Upskilling existing procurement staff in cloud economics, SaaS contract evaluation, and data literacy.
• Hiring for hybrid skills — professionals who understand both procurement domain knowledge and cloud technology concepts.
• Engaging strategic technology partners who can act as both a services procurement solution and a managed resource, particularly for organisations without deep in-house cloud expertise.

A trusted technology partner can help navigate vendor selection, contract negotiation, integration with existing systems, and ongoing governance — reducing the risk of costly missteps during the transition.

Key Takeaways

• Cloud computing is driving a fundamental shift in how procurement and supply chain functions operate, moving from capital-intensive ERP systems to flexible, app-driven cloud platforms.
• SaaS models reduce IT overhead, improve information sharing, and support faster deployment of procurement capabilities.
• Barriers including security concerns, skills gaps, and vendor lock-in risk must be actively managed as part of any cloud adoption strategy.
• Automation of the procure-to-pay process is one of the most immediate and measurable benefits of cloud adoption.
• Real-world implementations like Flex Pulse demonstrate how cloud-native tools can deliver real-time supply chain visibility at global scale.
• Procurement teams need new skills and — in many cases — strategic technology partners to execute cloud transitions successfully.

What Is DevOps Outsourcing?

DevOps outsourcing refers to the practice of engaging third-party service providers to design, implement, and manage DevOps pipelines, tooling, and culture on behalf of an organisation. Rather than building an in-house team from scratch, businesses contract specialised vendors to handle continuous integration and delivery (CI/CD), infrastructure automation, cloud operations, monitoring, and security — allowing internal teams to focus on product development.

Demand for these services has grown steadily as organisations face a global shortage of experienced DevOps engineers, increasing pressure to accelerate software delivery, and rising complexity in multi-cloud and hybrid infrastructure environments.

Key Market Drivers

• Talent scarcity: Skilled DevOps engineers remain difficult and expensive to hire and retain, making outsourcing an attractive alternative for mid-market and enterprise organisations alike.
• Speed-to-market pressure: Competitive markets demand faster release cycles. Outsourced DevOps teams bring pre-built toolchains and proven processes that reduce ramp-up time.
• Cloud adoption: Migration to AWS, Azure, and Google Cloud creates immediate demand for expertise in infrastructure-as-code, container orchestration, and cloud cost optimisation.
• Cost optimisation: Outsourcing converts fixed headcount costs into variable operational expenditure, which can improve unit economics for software delivery.
• Security and compliance requirements: Managed DevSecOps services help organisations embed security earlier in the software development lifecycle without building dedicated internal capability.

Notable Players in the DevOps Outsourcing Market

According to the QY Research report (2019), the following companies were identified as active participants in the global DevOps outsourcing service market at the time of publication:

• SwitchFast
• Liaison Technologies
• TechMD
• ServerCentral
• TruAdvantage
• Netcom Solutions
• Stratalux
• IT Force
• IT Svit
• TPX Communications
• TO THE NEW
• Cuelogic
• Huawei

The competitive landscape has evolved considerably since 2019. Hyperscaler professional services arms (AWS Professional Services, Google Cloud, Microsoft Azure consulting) and global system integrators such as Accenture, Cognizant, and Infosys have significantly expanded their DevOps outsourcing offerings. Buyers should evaluate the current market independently.

Market Segmentation

DevOps outsourcing services are typically segmented along two axes:

By Service Type

• Managed CI/CD pipeline services — design, implementation, and ongoing management of build and deployment automation.
• Infrastructure management — cloud provisioning, infrastructure-as-code (Terraform, Pulumi, CloudFormation), and configuration management.
• Container and Kubernetes operations — cluster provisioning, scaling, security hardening, and day-2 operations.
• Monitoring and observability — log aggregation, metrics, alerting, and incident response.
• DevSecOps — security scanning integrated into pipelines, compliance automation, and vulnerability management.

By Organisation Size and Vertical

• Small and medium-sized enterprises (SMEs) often outsource entire DevOps functions due to limited engineering headcount.
• Large enterprises typically engage vendors for specific augmentation — for example, a dedicated SRE team or cloud migration specialists.
• Verticals with strong demand include financial services, healthcare, retail, and SaaS companies operating under strict reliability and compliance obligations.

Regional Market Landscape

DevOps outsourcing demand and supply dynamics vary significantly by region:

• North America remains the largest consumer of DevOps outsourcing services, driven by high labour costs and mature cloud adoption among enterprises.
• Europe shows strong growth, particularly in the UK, Germany, and the Nordics, where GDPR compliance requirements add complexity that specialist vendors help address.
• Asia-Pacific is both a significant consumer and a primary delivery location, with India, the Philippines, and Eastern Europe hosting large pools of DevOps talent used to serve global clients.
• Latin America and the Middle East represent emerging markets where cloud adoption is accelerating and outsourcing demand is growing accordingly.

Ongoing Trends Shaping the Market

• Platform engineering: Organisations are moving beyond ad hoc toolchain assembly toward building internal developer platforms (IDPs). Outsourced platform engineering teams are increasingly in demand.
• FinOps integration: Cost visibility and cloud spend optimisation are becoming standard components of managed DevOps engagements.
• AI-assisted operations (AIOps): Vendors are incorporating machine learning into monitoring, anomaly detection, and automated remediation workflows.
• Shift-left security: DevSecOps practices — embedding security checks at every pipeline stage — are now a baseline expectation rather than a premium add-on.
• GitOps adoption: Declarative, Git-driven infrastructure and application deployment models (Flux, Argo CD) are becoming standard in managed DevOps offerings.

Evaluating a DevOps Outsourcing Partner: What to Look For

When assessing vendors, engineering leaders should consider the following criteria:

1. Toolchain expertise: Does the vendor have demonstrable, certified experience with the specific tools in your stack (e.g., Kubernetes, Terraform, GitHub Actions, Datadog)?
2. Security posture: What certifications does the vendor hold (SOC 2, ISO 27001)? How is access to production environments managed and audited?
3. SLA transparency: Are uptime, incident response, and mean-time-to-resolution commitments clearly defined and contractually enforceable?
4. Knowledge transfer: Does the engagement model include documentation, runbooks, and training to avoid long-term vendor lock-in?
5. Cultural fit: DevOps is as much a cultural practice as a technical one. Assess whether the vendor’s communication style, on-call model, and collaboration approach align with your engineering culture.

Summary

The DevOps outsourcing service market continues to expand as organisations seek faster software delivery, cloud expertise, and operational resilience without bearing the full cost of in-house capability. While the market data cited in early research reports from 2019 provides a useful baseline, the landscape has evolved rapidly — shaped by hyperscaler dominance, platform engineering trends, and the integration of AI into operations tooling. Engineering and procurement teams evaluating outsourcing partnerships should prioritise current market intelligence and rigorous vendor due diligence over dated forecast figures.

DevOps has become one of the defining practices in modern software delivery. At its core, it is about giving developers the autonomy and tooling to move code from source control into a running environment quickly and reliably. But once an application is live — whether in a test environment or production — a broader set of operational concerns takes over: performance monitoring, incident response, log management, and capacity planning.

Teams such as site reliability engineers (SREs) and system administrators handle these concerns, but they are routinely overwhelmed by the sheer volume of data their systems generate. Add to that the additional telemetry flowing from every application moving through a DevOps pipeline, and the data problem becomes acute. This is precisely where AIOps — Artificial Intelligence for IT Operations — acts as a powerful complement to DevOps practices.

What AIOps Does

The primary value of AIOps is breaking down the data silos that naturally form within IT organizations. Different teams — networking, infrastructure, application support, security — each generate their own streams of logs, metrics, and events. Traditionally, that data lives in separate tools and is interpreted only by specialists in each domain.

Before machine learning entered the picture, IT operators typically worked with data in one of three limited ways:

• Targeted lookups: They searched for a specific known error message when a particular business function failed — a workable approach for monolithic applications with well-understood failure modes, but brittle in dynamic environments.
• Domain-scoped monitoring: Each team routinely reviewed only the data from their own area of expertise, making cross-team correlation slow and dependent on manual handoffs.
• Summarized data: When teams did look outside their domain, they usually worked with pre-aggregated data — for example, one data point every five minutes — losing the granularity needed to detect intermittent or subtle problems.

An AIOps platform addresses all three limitations by ingesting high-volume, high-variety operational data from across the environment and applying machine learning to perform continuous trend analysis, anomaly detection, and pattern correlation. Rather than waiting for a human to notice something unusual, the platform surfaces actionable signals through dashboards or feeds them into event management systems for round-the-clock alerting.

As the AIOps platform accumulates history and learns what normal looks like for a given environment, it can also be granted the authority to automatically remediate certain known error conditions — resolving them before they escalate into service-impacting incidents.

How AIOps Helps DevOps Teams Specifically

Machine learning improves DevOps workflows in three concrete ways that map directly onto the three traditional limitations described above.

1. Establishing Baselines in Polyglot Microservice Environments

In a modern microservices architecture, a new service can be conceived, written in any language, and deployed to production without a formal handoff to an operations team. No single person can know every error pattern that might emerge from dozens or hundreds of independently deployed services.

ML-driven AIOps platforms solve this by automatically learning what normal behavior looks like for each new service shortly after it is deployed, then alerting on deviations from that learned baseline. This removes the dependency on pre-written runbooks or tribal knowledge about specific error messages.

2. Cross-Domain Correlation During Incident Response

When a developer is debugging an incident, they often need data from systems outside their immediate area — infrastructure metrics, network logs, database query times — that they have neither the access nor the expertise to interpret efficiently.

AIOps platforms can correlate data across systems using time windows or correlation identifiers (such as a GUID embedded in log entries for distributed tracing) to automatically surface related events from other domains. A developer investigating a slow API response can be shown correlated infrastructure events — for example, a node memory spike or a Kubernetes pod restart — without having to manually query each system.

The same capability works in reverse: if an infrastructure failure occurs, the platform can identify which downstream services — including active build pipelines or running containers — may have been affected.

3. Retaining Full-Fidelity Data Instead of Summaries

Traditional monitoring systems cope with data volume by aggregating metrics into coarse summaries — a single averaged value per polling interval. This means that a transient spike lasting ten seconds in a sixty-second polling window may never be recorded.

Machine learning systems are designed to process large volumes of raw, granular data efficiently. By retaining full-fidelity log and metric streams, AIOps platforms can detect problems that occur frequently but inconsistently — the kind of intermittent failures that summarized data routinely obscures — and surface them for investigation before they become chronic.

Conclusion

AIOps is a relatively young discipline, building on earlier generations of IT operational analytics tools and augmenting them with modern machine learning capabilities. DevOps environments are among the most data-intensive in enterprise IT — generating continuous streams of logs, metrics, traces, and events across tightly coupled but independently deployed services.

Combining AIOps with DevOps practices offers a practical path toward faster incident detection, more efficient root-cause analysis, and ultimately improved service reliability — without requiring teams to hire specialists for every domain or manually correlate data across silos. As both disciplines mature and converge, organizations that invest in this integration are likely to see measurable gains in operational productivity and service quality.

Loom Systems’ AI-powered log analysis aims to give companies a warning when there may be a problem in their system by reading logs and detecting when something is likely to go wrong — and then sends out alerts so DevOps and IT managers can respond before systems go down.

ZDNet talked to CEO Gabby Menachem about the company’s future plans.

ZDNet: Tell me about the origins of Loom Systems.

Menachem: We were founded two years ago and using our own money. We did this, rather than use venture capital funding, because we wanted to be closer to our customers. That way we could better understand the changing needs of companies in this data-driven industry.

We felt that having worked with big data platforms in the past, we have seen how all companies have been occupied with solving problems with data.

From infrastructure to data analysis platforms to automated data science, at Loom we have been building all of these. And with all that experience, what we have come to realise is that, while companies should be able to continuously analyse big data, they are not succeeding in doing that.

That’s because many of them find it just too hard. It’s hard to have the right methodology, it’s hard to analyse it, and it’s hard to develop actual use cases.

So, understanding that, we broke it down into different stages of analysis and decided to build what we think is the world’s first cognitive platform that can analyse the data for you.

For the first two years we worked without any VC funding. We took in only revenue from customers. We built up very quickly and in a very short time got up to 28 employees in offices in Tel Aviv and San Francisco.

Now we are expanding our sales to the US and we have taken in our first VC money. We are growing quickly and now we have over 30 people, and intend to have 50 by the year-end. We now have customers of various sizes, up to some who are in the Fortune 500.

And a big part of that is big data?
Maybe I should start with a specific use case.

Where, at one time, you would have been talking to someone about what the problem was, now you are talking to a digital system instead.

Now instead of talking to people, staff have to do their work on a system and, whatever the problem, they need to solve it in minutes, not the hours or days that they have been used to.

This new strain on IT, together with the fact that the digital medium itself has grown so fast — and not forgetting that it is also a very big part of the revenue for all of these companies — creates pressure. So IT operations has become another place where business needs to invest in order to lower the problems that they are seeing and to mitigate the churn that IT causes.

If IT is such a big part in this, how do you get that across to the people who work in IT? 
I think we are already having some success with that. Gartner recently praised us as a Cool Vendor of 2017 for expertise in performance analysis. That was because we were able to show that we could take the insights of a problem and create a crowdsourced solution that IT people and DevOps people could put on their usual schedule while continuing to contribute to IT and DevOps.

How does this work? The fact that every digital system today creates log messages is the basis of understanding how DevOps people solve issues within businesses. Whenever you have a problem, either you find out about it by yourself or through a customer. You then go into the system and you try to investigate by looking at the logs and trying to guess what went wrong. That usually entails using the methodology that you have. That is usually a person who is trying to find a word, or a structural anomaly in the logs, or specific messages that can tip you off on where the problem originated from.

Now that causality problem — how do you find the root cause of issues? — is a big problem because you need to solve it in minutes. Now, when we looked at these problems we thought: ‘What if we could build a system that could go through that process just as a human would but do it in an automated manner?’.

Now you can just stream logs from all your systems into Loom Systems, and what it does, using AI, is look at all your logs and do this investigation on a continuous basis. So instead of investigating when something goes wrong, it investigates on an ongoing basis and if it finds something it alerts you.

It replaces the way you do monitoring so that, instead of investigating with metrics that you have pre-defined, now you have a system that surfaces interesting events that could be problems in your IT systems. And it does it without you needing to know how to define these problems.

That’s the way to be on top of everything and it mitigates the risk that we talked about earlier.

The second thing is, after you understand this then you can reverse the process so that you are now proactive. Now whenever something happens in the system, you get alerted on it even though the customer doesn’t know about it.

Are your systems applicable to any type of system or application?
Our system is agnostic but we are seeing more and more customers in the financial industry along with telcos. I think that’s probably because in the financial industry you are seeing a lot more innovation and early adopters of technology.

And the telco industry specifically I think, because the margins there are now low so they are looking at a lot more efficiencies and ways to mitigate IT risk. I think that a technology like ours is not only managing to put a cap on the risk that they have, but is also giving them a better estimate for the customer. For example, if you have a problem with your cell phone, that’s not something that customers take lightly these days so if you have issues, customers want them solved very quickly.

Where are you seeing the most interest?
I think ecommerce is a growing space and in a lot of cases, where they used to be doing things that could be handled by a sole IT guy, they are now expanding and are becoming very large systems that are a big source of revenue.

We are also seeing a lot of manufacturing companies that want us to look at the logs from robots. Now, it is all based on secure assembly lines that are working faster than humans could, but it is also creating a big risk because if the line is not working, that hits your revenue. And all that is also working with our platforms.

Our software is there to predict issues and fix them fast. But the pressure is on from companies because they are buying these robots and they need software to manage them but the software is not being built at the same pace as the ability to manufacture.

So where is DevOps in all of this?
With these modern systems, efficiency of software is key and companies have been working on making software work more efficiently. DevOps has been important in the software world but now we are not only seeing it there but everywhere.

Having a good continuous development strategy and integration skills in manufacturing plants is something that companies (like GE for example who have been pushing it for a few years now) are taking into account in their vision for the future.

But can DevOps really be part of manufacturing?
Where I see DevOps in the future is as part of a new methodology. Because software is moving so fast, you need a new way of doing operations.

Now it is becoming so tied to the software that you need to incorporate the developer and we think that [line Of business] is the perfect solutions for that.

Where it used to be aimed at a specific solution, now, because the systems are so complex, just getting an understanding of how a business is performing can be hugely complex. So more and more people have to be put on looking at business data and DevOps data.

Now in that sense, DevOps is still young. A lot of the early issues around DevOps have been solved but now you have new issues like, how to handle all this data and build actions on top of it.

And this just returns us to the basic issue. It is all about the business, so you are not just doing DevOps because it’s new and cool, you are building it into your systems because it can solve business problems.

While it is doing that it is also building you a better service for your customers.