Cloud costs rarely spiral out of control overnight. More often, they drift quietly and steadily until finance teams are left explaining overruns and engineering teams are asked to “optimize” after the fact.
This reactive approach to FinOps is becoming harder to sustain. Cloud environments today are far more dynamic than the tools and processes designed to manage them. Monthly reviews, static rules, and backward-looking reports simply cannot keep up.
This is where AI-driven FinOps steps in. Not as another dashboard, but as the next evolution of FinOps itself but one that helps teams predict what’s coming, prevent waste before it happens, and continuously improve performance.
From Cost Visibility to Cost Intelligence
Traditional FinOps gives you visibility. You can see where money is being spent, which teams own which resources, and how costs trend over time. That foundation still matters.
But visibility alone doesn’t answer the questions that really matter now:
Where is spend likely to increase next?
Which workloads are behaving differently than expected?
What should teams act on today, not at the end of the month?
AI adds intelligence to FinOps by connecting historical patterns with real-time data. Instead of just reporting on spend, AI helps teams understand why costs are changing and what to do about it.
Predict: Forecasting That Keeps Up with Change
Forecasting cloud spend has always been difficult. Usage shifts with new releases, customer demand, and infrastructure changes, often making static forecasts outdated almost as soon as they’re created.
AI-driven FinOps improves this by:
Continuously forecasting spend using live usage data
Learning from patterns like seasonality and growth trends
Adjusting predictions as workloads and architectures evolve
The result is forecasting that feels less like guesswork and more like guidance. Finance teams gain clearer budget visibility, while engineering teams better understand how their decisions shape future costs.
Prevent: Catching Anomalies Before They Become Problems
In many organizations, cost anomalies are discovered only after the bill arrives. By then, teams are already behind.
AI changes that dynamic. By learning what “normal” looks like for each workload, AI-powered FinOps tools can spot unusual behavior as it happens whether it’s a sudden traffic spike, a misconfigured autoscaling rule, or resources running idle longer than expected.
Even more important, these alerts are contextual. They don’t just flag a spike; they explain where it’s coming from and why it matters. That clarity helps teams respond faster, with less finger-pointing and fewer manual investigations.
Perform: Continuous Optimization, Not Periodic Cleanup
FinOps works best when finance and engineering operate as partners, not gatekeepers and enforcers. AI makes that collaboration easier by translating complex cost data into insights each team can act on.
With predictive insights in place:
Finance teams can focus on planning and accountability, not policing
Engineering teams can design with cost in mind, without slowing delivery
Optimization becomes ongoing, not something squeezed into quarterly reviews
Savings are identified earlier, responses are faster, and performance goals stay intact, all without adding operational overhead.
Case Study: Optimizing Petabyte-Scale Workloads for Cost and Continuity
The value of AI-driven FinOps becomes clear at scale.
A content-intelligence platform processing petabytes of data every day needed to control cloud costs without compromising performance or availability. Manual reviews and static optimization rules were no longer enough.
By introducing predictive planning and real-time anomaly detection, the organization gained early visibility into cost deviations and the ability to act before issues escalated.
The R Systems Approach: AI-Powered FinOps, Built for Continuous Optimization
AI is powerful, but it delivers real value only when embedded into everyday cloud operations.
R Systems brings together AI-driven forecasting and anomaly detection with continuous optimization practices that align finance, engineering, and operations. The focus is not on one-time savings, but on building a FinOps capability that evolves alongside the cloud environment.
The outcome is a FinOps model that is proactive, collaborative, and resilient, designed to keep pace with both growth and change.
As cloud environments grow more complex, the cost of reacting late keeps rising. AI-driven FinOps offers a practical alternative: predict earlier, prevent waste, and perform with confidence.
For organizations that see cloud efficiency as a long-term discipline and not a quarterly exercise, there AI is no longer optional. It is foundational.
Agentic AI has moved quickly from experimentation to expectation. Most enterprises today have pilots in motion, proofs of concept delivering early promise, and leadership teams asking a sharper question: How do we scale this safely, reliably, and with real business impact?
That question is often followed by fatigue. Too many pilots stall. Too many promising demos fail to survive real-world complexity. And too often, the issue isn’t the technology itself.
The uncomfortable truth is this: most agentic AI failures are not technology failures. They are partner failures.
As enterprises move from pilots to production especially within Global Capability Centers (GCCs), partner selection has become a strategic decision, not a procurement one. The difference between experimentation and enterprise value increasingly comes down to who you build with.
Why Partner Choice Matters More Than Ever
Agentic AI is fundamentally different from earlier waves of automation. It introduces autonomy into business workflows, systems that can sense, decide, and act with limited human intervention.
That kind of capability doesn’t scale through tools alone.
Scaling agentic AI requires deep enterprise context, operating-model alignment, strong governance, and ownership of outcomes. Yet many organizations still choose partners based on narrow criteria: a compelling demo, a preferred toolset, or short-term cost efficiency.
Those choices may work for pilots. They rarely work for production.
As organizations mature, a clear realization is emerging: the partner matters as much as the platform or often more.
Innovation Readiness Is Not Optional
Agentic AI is advancing faster than most enterprise operating models can comfortably absorb. New orchestration patterns, reasoning techniques, safety mechanisms, and runtime optimizations are emerging at a pace that outstrips traditional delivery and governance cycles.
In such an environment, partner capability cannot remain static. Enterprises need partners with a sustained capacity for innovation not merely the ability to implement what is already familiar.
The most effective agentic AI partners operate through a mature AI Center of Excellence: one that systematically experiments, evaluates new tools and approaches, and converts what proves viable into production-ready practices before they enter core enterprise systems.
Without this discipline, organizations risk committing too early to architectural choices that do not age well, making choices that introduce technical debt, constrain future evolution, and limit the scope of autonomy over time.
Innovation readiness in agentic AI, then, is not a matter of chasing what is new. It is the ability to distinguish signal from noise, to decide deliberately what belongs in production, and to industrialize proven approaches with consistency, safety, and repeatability.
The Common Partner Pitfalls
Most enterprises don’t choose the wrong partners intentionally. They choose partners that are right for a different stage of maturity.
Some common pitfalls we see:
Tool-first vendors who excel at showcasing AI capabilities but lack experience running mission-critical enterprise systems.
Traditional system integrators with scale and delivery muscle, but limited depth in agentic AI design and orchestration.
Niche AI firms that can build impressive pilots but struggle with integration, governance, and long-term operations.
Delivery partners focused on execution, not accountability leaving enterprises to own risk, outcomes, and scale alone.
Partners who lack domain or functional depth, resulting in agents that understand tools but not the business context, decision logic, or real operational constraints.
None of these partners are inherently flawed. But agentic AI demands a broader, more integrated capability set.
The Agentic AI Partner Readiness Checklist
Before trusting a partner to take agentic AI into production, leaders should ask a simpler, more direct question:
Can this partner scale autonomy responsibly inside my enterprise?
Here is a practical checklist to help answer that question.
1. Enterprise & GCC Readiness
Has this partner run large-scale, production systems and not just pilots?
Do they understand GCC operating models, governance structures, and decision rights?
Can they embed AI ownership into teams, not just deliver projects?
2. Agentic AI Depth
Do they go beyond chatbots and copilots?
Have they designed and deployed multi-agent systems in real environments?
Do they build in human-in-the-loop controls by default?
3. Scalability & Reusability
Do they think in platforms, not one-off agents?
Can their solutions be reused across functions and workflows?
Is observability and lifecycle management part of the design and not just an afterthought?
4. Data & Integration Maturity
Can they work with messy, legacy, enterprise data?
Do they integrate cleanly with core business systems?
Is data governance built into the solution from day one?
5. Security, Risk & Governance
Are guardrails designed in, not bolted on?
Can decisions be explained, audited, and governed?
Are solutions built for regulated, compliance-heavy environments?
6. Outcome Ownership
Are success metrics tied to business outcomes not activity?
Will the partner co-own KPIs, risk, and accountability?
Do they stay invested beyond go-live?
This checklist shifts the conversation from capabilities to credibility.
Why This Checklist Changes the Conversation
Used well, this framework changes how enterprises approach agentic AI adoption.
It shifts the focus from vendors to partners, from pilots to platforms, and from experiments to operating models.
It also makes one thing clear: scaling agentic AI is not a one-time implementation. It is a capability that must be built, governed, and evolved over time.
Organizations that succeed tend to work with partners who understand enterprise realities, operate comfortably inside GCC environments, and engineer autonomy with accountability at the core.
That is where agentic AI becomes sustainable.
The Partner as a Force Multiplier
Agentic AI is not a shortcut. It is a long-term capability play.
The right partner accelerates scale, reduces risk, and protects ROI by ensuring that autonomy is introduced not with disruption but with discipline.
The wrong partner adds complexity, creates fragility, and leaves enterprises managing outcomes they never fully owned.
As leaders move from pilots to production, the question is no longer whether agentic AI can deliver value.
It is whether you have the right partner to deliver it at scale, in the real world, and over time.
Why Domain & Functional Context Make or Break Agentic AI
Agentic AI systems do not simply automate tasks, they make decisions inside business workflows. That makes domain and functional context non-negotiable.
An agent operating in finance, supply chain, customer service, or engineering must understand far more than APIs and prompts. It must respect process boundaries, exception handling, regulatory constraints, and the implicit rules humans apply every day.
Partners without functional or industry depth often build agents that technically work but fail operationally, producing decisions that are correct in isolation yet wrong in context.
The most effective partners combine agentic AI engineering with deep functional understanding, enabling agents to operate with judgment, not just intelligence.
Every few months, someone in the telecom space claims that the self-healing network is just around the corner. This has been happening for years. Yet, many regional operators are still handling incidents manually, with their engineers triaging alarms and switching between legacy dashboards and SNMP traps.
And the problem isn’t that operators lack ambition, or the drive for change – it’s that they don’t trust automation enough. That’s because they’ve learned, often the hard way, that even the smallest glitch can take a stable network down in seconds. This brings us to the real barrier to AI adoption in network operations, not technology, but trust. And honestly, that’s a rational response.
AI’s first job is to earn engineers’ trust, not to replace them
Most automation stories start from an ideal scenario: clean data, cloud-native infrastructure, and teams fluent in DevOps and data science. However, that’s not the reality for most Tier-2 operators. These are lean teams running multi-vendor environments, juggling with limited budgets and decades-old systems.
After over 20 years in telecom, at R Systems we’ve worked with operators who’ve run anomaly detection pilots that technically worked but stayed in read-only mode for months because no one in the Network Operations Center (NOC) trusted the system enough to act on its recommendations. That’s rather a failure of design philosophy, than AI. The automation model might be perfect, but if the trust is low, it won’t go live.
That’s why your first automation should first build trust and then trigger growth and digital transformation. It doesn’t need to be “zero-touch” solution. It needs to be safe andreversible, because engineers trust what they can override.
Start where failure costs are low and wins are visible
From what I’ve seen in most Tier-2 operators, about half the workload of their NOC comes from low-impact, repetitive incidents, like interface flaps, link degradations, or simple routing resets.
These are the perfect starting points for AI. They happen often enough for models to learn quickly, and even if something goes wrong, the impact is minimal. Automating such tasks can cut alert fatigue dramatically, without touching high-risk infrastructure. The goal isn’t to replace engineer teams, but to help them focus on innovation and growth, while allowing AI to handle high-frequency, low-risk tasks.
Reversible automation builds confidence, one task at a time
Every successful small automation builds political capital for bigger steps. Operators gain confidence when they see an AI system take on simple, reversible tasks and get them right.
Features like explain-why outputs, detailed logs, and one-click rollbacks allow engineers to stay in control. This “supervised automation” mindset is how AI earns its place in runbooks and not the other way around. Because when the NOC team feels that AI is a partner, not a blocker, adoption accelerates naturally.
AI in the NOC: how your first 90 days will look like
If you’re wondering where to start, here’s what’s worked in practice:
Step 1: Identify your top 10 high-frequency, low-risk runbooks.
Work with your NOC managers and subject matter experts to pinpoint repetitive incident types that drain the most time.
Step 2: Roll out AI in read-only mode.
Have the Ops / DevOps teams use it for auto-diagnosis and ticket enrichment. This builds trust with zero risk.
Step 3: Move to supervised automation with rollback options.
Let the AI recommend and occasionally execute known-safe actions, with human oversight, to reduce MTTS and false-positive rates.
If you follow this sequence, you can realistically target a 20–30% reduction in incident triage time within 12 weeks, without ever touching core routing policies.
What success looks like
A regional fiber ISP ran a small pilot with AI-based anomaly detection on its edge routers. Before the pilot, the six-person NOC was logging 15+ manual tickets every night.
After the AI grouped and labeled similar alarms automatically, that number dropped to just four incidents requiring human confirmation. The mean time to resolution (MTTR) went down by 28%.
That’s not science fiction, it’s what happens when trust comes before automation.
“Start Small” isn’t playing small
Some leaders worry that starting with small, reversible AI automations means they’ll fall behind the big players. Actually, it’s the other way around. Tier-1s often spend years (and millions) chasing “autonomous” dreams, but you can deliver measurable value in 90 days with a laptop, good logs, and the right mindset.
The key is to think of AI not as a leap of faith, but as a series of safe, reversible steps that gradually earn your confidence and your engineers’.
Because the truth is, AI doesn’t need to replace the human operator to transform the NOC. It just needs to make their 2 a.m. shift a little quieter, a little smarter, and a lot more human.
We have seen the same pattern repeat across insurance clients more times than we can count: a significant investment in a “strategic” BI platform, followed by growing frustration just a few years later. The dashboards still run, but the platform starts to feel heavy. Costs increase. New data sources take longer to onboard. Regulatory requirements evolve faster than the analytics stack can adapt.
For data and BI leaders in insurance, this is not a hypothetical scenario — it’s a familiar one.
The reality is simple: BI tools age faster than most organizations anticipate. Data volumes grow exponentially, operating models change, and regulatory goalposts continue to shift. In our experience at R Systems, the challenge is rarely the BI tool itself; it’s how tightly business logic, governance, and skills are coupled to that tool.
The Reality of Today’s Insurance BI Landscape
There is no such thing as a perfect BI tool — only the right tool for a given context. And in insurance, that context is constantly evolving.
Over the last decade, our teams have worked across a wide spectrum of analytics environments, from mainframe-driven reporting to cloud-native, AI-enabled platforms. Insurance organizations bring unique complexity to this journey: legacy core systems, fragmented actuarial and claims data, strict compliance requirements, and constant pressure to deliver more insight with fewer resources.
Most insurers still rely on a familiar set of BI platforms:
MicroStrategy
Tableau
Qlik
Oracle BI
And increasingly, Power BI
What we see most often is not a clean replacement of one tool with another, but a multi-tool landscape where new platforms are introduced alongside existing ones. This coexistence phase is where long-term success — or failure — is determined.
The biggest mistake organizations make is assuming that today’s “strategic BI choice” will remain optimal as business priorities, data platforms, and regulatory expectations evolve.
A Candid View of the Major BI Platforms in Insurance
MicroStrategy We’ve seen MicroStrategy perform extremely well in large insurance environments that demand strong governance, complex security models, and predictable enterprise reporting. It scales reliably and meets regulatory expectations. At the same time, it can feel restrictive for agile analytics or rapid experimentation, especially when business users seek faster self-service capabilities.
Tableau Tableau consistently drives high adoption due to its intuitive visual experience. Actuaries, underwriters, and analysts value the ability to explore data quickly and independently. Where insurers often struggle is governance at scale — particularly as data sources proliferate and business logic fragments across workbooks. Without strong discipline, performance and lineage challenges emerge.
Qlik Qlik is often underestimated in insurance contexts. Its associative model excels in ad hoc exploration, especially for claims analysis, fraud detection, and investigative use cases. Challenges tend to arise in deeply governed enterprise scenarios or where long-term extensibility and integration with modern data platforms are priorities.
Oracle BI Oracle BI remains a common choice for insurers heavily invested in Oracle ecosystems. It offers robust security and strong integration. However, innovation cycles can be slower, and business-user agility is often limited. Many teams rely on it out of necessity rather than preference.
Power BI and Its Growing Role Power BI has become a significant part of the insurance analytics conversation. Its integration with modern data platforms such as Databricks and Snowflake, improving enterprise governance, and rapidly evolving AI capabilities have made it a strategic option for many insurers.
In practice, we frequently see Power BI introduced alongside existing BI platforms — supporting executive reporting, self-service analytics, embedded use cases, or AI-driven insights — rather than as an immediate replacement. This coexistence reinforces the need for a flexible, decoupled architecture.
The Hidden Risk: Where Business Logic Lives
Across migrations and modernization programs, one risk appears repeatedly: deeply embedded business logic inside BI semantic layers.
When regulatory calculations, actuarial formulas, and financial metrics are hard-coded into a specific BI tool:
Migrations become slow and expensive
Parallel runs are difficult to validate
Flexibility disappears during mergers, acquisitions, or platform shifts
At that point, the BI tool stops being a presentation layer and becomes a structural constraint.
Five Questions We Use to Future-Proof Insurance BI Decisions
Based on our delivery experience, we encourage insurance BI leaders to ask five critical questions before making — or renewing — a BI investment:
How easily can BI tools be swapped or augmented as strategies and vendors change? Rigid architectures increase risk during integrations and modernization efforts.
Can governance models evolve with regulatory and data privacy demands? Many BI failures stem from brittle access controls and manual processes.
How well does the BI layer integrate with modern data platforms and AI services? Cloud-native and AI-enabled analytics are no longer optional.
How is the balance managed between self-service and enterprise control? Too much freedom leads to chaos; too much control drives shadow IT.
Are investments being made in skills and architecture, not just licenses? Tools change, but strong teams and sound design principles endure.
Lessons Learned From Real Programs
In one engagement, we supported an insurer migrating from Oracle BI to Jasper to improve operations. While the target state made sense, a significant amount of critical logic was embedded in Oracle’s semantic layer. Rebuilding these calculations extended the program timeline by nearly 40%.
In contrast, we’ve worked with insurers who deliberately decoupled their transformation and metric layers from the BI tool. When licensing or strategic priorities shifted, they were able to introduce Power BI with minimal disruption. That architectural choice saved months of effort and reduced long-term risk.
Trends Insurance BI Teams Can No Longer Ignore
Across recent insurance RFPs and transformation programs, several patterns are now consistent:
Cloud-native data platforms (Databricks, Snowflake, BigQuery)
Power BI and embedded analytics for agents, partners, and customers
AI-driven insights and natural language querying
Data mesh and data fabric operating models
These are no longer emerging trends — they are current expectations.
Enterprise AI Forecasting Framework – Designed and deployed a centralized, modular AI/ML forecasting architecture to unify forecasting across Finance, Logistics, Procurement, and Sales, replacing fragmented, manual processes with a single source of truth.
Accuracy & Predictive Depth – Achieved up to 80% forecast accuracy across freight costs, transport lead times, and sales, with <20% MAPE for daily and weekly bank balance forecasts—delivering reliable short- and long-term visibility across business functions.
Operational Efficiency at Scale – Automated end-to-end forecasting pipelines, significantly reducing manual effort, minimizing human error, and enabling monthly forecast updates with minimal retraining overhead.
Actionable Business Intelligence – Enabled finance, sales, and logistics teams with real-time, role-specific dashboards to support proactive cash flow management, inventory planning, shipment prioritization, and demand-led decision-making.
Modularity, Scalability & Reuse – Implemented a reusable forecasting framework supporting both univariate and multivariate models, allowing rapid extension to new business use cases, profit centers, and data sources without architectural rework.
Strategic Business Impact – Improved planning precision, strengthened cross-functional alignment, and established a scalable AI foundation to support ongoing digital transformation and enterprise-wide forecasting maturity.
Chronic diseases like diabetes, cancer, and heart conditions often get detected too late. But what if early warning signals were already hidden inside your EMR data?
Our POV on AI-Powered Multimodal Fusion reveals how healthcare providers can move from reactive treatment to proactive, data-driven, and explainable risk prediction, without the need for advanced imaging or expensive diagnostics.
Why This POV Is a Must-Read
Healthcare organizations are sitting on enormous amounts of clinical data but very little of it works together. Our POV uncovers how multimodal AI bridges these silos to deliver:
Earlier detection of diabetes, cancer, and cardiovascular risks
Explainable health insights powered by SHAP and attention mechanisms
Seamless integration with existing EMR systems
Improved clinical decision-making using data you already have
Better population health, lower long-term costs
Who Shouldn’t Miss to Read This POV
Hospital & clinical leaders
Digital health innovators
EMR/HealthTech product owners
Population health & payer strategy teams
If early risk detection, preventive care, and explainable AI are priorities, this POV will equip you with high-impact insights.
From futuristic speculation to everyday reality, smart homes can go way beyond connected devices – they can become intelligent, collaborative, reactive and adaptable environments. This can be achieved using Multi-agent AI Systems (MAS) to unify IoT devices and lay a solid foundation for innovation, for more seamless and secure living.
This remarkable growth of smart homes brings both opportunities and challenges. In this whitepaper, we’ll explore both, moving from the general – market overview and predictions, to specific – blueprint architecture and use cases, using AWS Harmony.
Here’s a breakdown of the whitepaper:
The Smart Homes market landscape: what is the current state and changes to expect
Multi-Agent AI Systems (MAS): how they work and why they’re transforming Smart Homes
The technology behind MAS: capabilities, practical applications and benefits
Smart Homes on AWS Harmony: blueprint of Agentic AI as the foundation for next-gen experiences
Use case for sustainable living: a hybrid Edge + Cloud IoT high-level architecture to implement for energy saving
We are proud to share that Jyoti Dash, our General Manager – Operations, was featured in Times of India on International Day of Persons with Disabilities, sharing her inspiring journey of resilience, determination, and growth.
Under the powerful heading “If you pity yourself, others will too,” Jyoti shared her story:
“I’m physically challenged, and growing up, that made me extremely shy because of which I faced bias early on, whether it was being excluded from school annual functions, sports days, or never being considered for roles like class monitor or head girl. These moments stayed with me, but discovering the arts helped me slowly find my place. Winning several medals taught me that if I put myself out there, I could be seen for my talent and not my disability. When I stepped into the professional world, the bias continued. My first job interview rejected me because they assumed I wouldn’t even be able to type on a computer. I sat for multiple interviews before finally getting selected, but even then, I often remained at entry-level roles because people doubted my leadership potential. My biggest turning point came early at R Systems when I was trusted with a project that required me to travel alone to the US for three months. Being on my own, without anyone to lean on, made me stronger. Soon after, I was given the opportunity to lead a new project that began with just seven people and has today grown to around sixty. Every step of this journey reinforced one important lesson: keep learning. Whether professionally or personally, continuous upskilling has always been my way forward. Most importantly, I learned never to pity myself. The moment I pity myself, I give others permission to do the same.”
We are fortunate to have Jyoti as part of the R Systems team. Her journey with us, from being trusted with that pivotal solo project in the US to leading a team that has grown from seven to sixty members, exemplifies what’s possible when talent is recognized and nurtured without bias. Jyoti’s leadership, dedication, and continuous drive for excellence inspire all of us every day.
At R Systems, we remain deeply committed to our Diversity, Equity, and Inclusion principles. Jyoti’s story reminds us why this commitment matters, both as policy and in practice. We’re glad that she found her place with us, and we will continue working to ensure that every team member can be seen for their talent, grow without barriers, and lead with their full potential.
For decades, telecom innovation has been about connecting people faster, clearer, and more reliably. But today, we’re entering a new era – one where machines can understand people, not just connect them.
Artificial Intelligence (AI) is rapidly transforming telecom networks into intelligent ecosystems that learn, predict, and act. And for Communications and Service Delivery Platform (CSP and SDP) providers, this shift represents a strategic turning point.
At our recent presentation for industry peers, Bogdan Tudan, VP of Telecom, Media & Entertainment explored what’s possible when AI moves from being an “add-on” to becoming an embedded intelligence layer in telecom systems. From self-designing IVRs to fraud-blocking digital guardians, the impact is profound.
Let’s unpack what this means in real-world terms.
1. From Code to Conversation: The Evolution of Call Flow Design
Not long ago, building or updating an IVR (Interactive Voice Response) system was a slow, technical process. You’d discuss call flows with operators, wait days for implementation, and repeat the entire cycle for every minor change.
Today, thanks to Service Delivery Platforms (SDPs), that’s ancient history. Enterprises can already log in, design their own routing logic through a self-care interface, and deploy it instantly.
But what if that process became even simpler — as natural as talking to a colleague?
Imagine designing your call flow not by dragging boxes or reading manuals, but by telling an AI assistant what you want. “Route all calls in Spanish to our Madrid team,” or “Play a service outage message for customers in Zone 4.”
The AI would understand your intent, configure the flow, and show you the result instantly — all while retaining the option to fine-tune manually.
This is where telecom UX meets generative AI (GenAI): making configuration conversational, intuitive, and intelligent.
2. Turning Data into Dialogue: AI-Driven Insights and Optimization
Once the AI assistant knows your call structure, it can go a step further: analyze how well it’s performing.
How many callers reach the right destination?
Where do most calls drop?
Are certain menus confusing customers?
With AI, you don’t just get data — you get recommendations. The system can proactively suggest improvements, much like a digital operations coach.
Consider this scenario: a fiber outage hits a local area. Traditionally, your support lines would flood with calls. But now, you simply tell your AI assistant, “Announce that our team is fixing the issue and service will resume by 5 PM.”
Within seconds, every incoming caller hears a calm, professional update. No manual reconfiguration. No waiting. Just real-time, automated customer care — powered by natural language and intelligent automation.
3. Fighting Fraud with Intelligent Guardians
Of course, telecom isn’t just about connection and convenience — it’s about trust. And that trust is under siege.
Every year, U.S. operators face more than 50 billion scam calls, resulting in over $39 billion in estimated losses. Globally, the threat landscape is just as alarming.
Traditional fraud management tools on SDPs already help — flagging suspicious patterns, blocking one-ring scams, and filtering spoofed calls. But they’re inherently reactive.
So what if AI could listen and understand — in real time?
We’re experimenting with “AI security agents” that monitor flagged calls and detect suspicious behavior based on conversation context. For example:
“May I have your PIN to verify a transaction?”
In that instant, the AI recognizes a likely scam attempt and can respond in multiple ways:
Block the call outright.
Whisper a warning to the user (“This doesn’t sound like a legitimate bank request”).
Flag and record the incident for operator review.
Because AI agents would only monitor suspicious calls — less than 1% of total network traffic — the approach is both scalable and cost-efficient. It’s proactive fraud prevention with minimal processing overhead.
This isn’t science fiction. Several European operators are already piloting AI-embedded gateways that can do precisely this. Within 6–12 months, such solutions could be commercially available — and represent a new revenue stream for security-conscious operators.
4. Outsmarting Scammers — Literally
One of our favorite examples comes from a UK operator who took a brilliantly creative approach to scam prevention.
When a scam call was detected, instead of simply dropping it, the system redirected the call to an AI-generated persona — a cheerful “grandmother” who would keep the scammer talking endlessly.
This conversational decoy wasted the scammer’s time and resources while protecting real customers. The longest recorded call? 15 minutes.
Sometimes, intelligence doesn’t just stop bad behavior — it makes it unprofitable.
5. The Road Ahead: AI as a Telecom Multiplier
AI’s potential in telecom extends far beyond automation. It’s about embedding understanding and context into every network layer:
Intelligent call routing that designs itself.
Predictive maintenance and self-healing systems.
AI-driven fraud and risk detection.
Conversational analytics for customer experience.
As generative models mature, we’ll see CSPs and SDPs evolve into adaptive service ecosystems — networks that not only deliver connectivity but continuously learn and optimize.
At R Systems, we see AI not as a technology trend, but as the next step in digital product engineering for telecom. By merging GenAI, SDP capabilities, and domain expertise, we’re helping operators move from reactive operations to predictive intelligence — and from service providers to true experience orchestrators.
Because in the future of telecom, machines won’t just connect us. They’ll understand us.
As marketed, Spinnaker is an open-source, multi-cloud continuous delivery platform that helps you release software changes with high velocity and confidence.
Open sourced by Netflix and heavily contributed to by Google, it supports all major cloud vendors (AWS, Azure, App Engine, Openstack, etc.) including Kubernetes.
In this blog I’m going to walk you through all the basic concepts in Spinnaker and help you create a continuous delivery pipeline using Kubernetes Engine, Cloud Source Repositories, Container Builder, Resource Manager, and Spinnaker. After creating a sample application, we will configure these services to automatically build, test, and deploy it. When the application code is modified, the changes trigger the continuous delivery pipeline to automatically rebuild, retest, and redeploy the new version.
What Spinnaker Provides?
Application management and Application Deployment are its two core features.
Application Management
Spinnaker’s application management features can be used to view and manage your cloud resources.
Modern tech organizations operate collections of services—sometimes referred to as “applications” or “microservices”. A Spinnaker application models this concept.
Applications, Clusters, and Server Groups are the key concepts Spinnaker uses to describe services. Load balancers and Firewalls describe how services are exposed to users.
Application
An application in Spinnaker is a collection of clusters, which in turn are collections of server groups. The application also includes firewalls and load balancers. An application represents the service which needs to be deployed using Spinnaker, all configuration for that service, and all the infrastructure on which it will run. Normally, a different application is configured for each service, though Spinnaker does not enforce that.
Cluster
Clusters are logical groupings of Server Groups in Spinnaker.
Note: Cluster, here, does not map to a Kubernetes cluster. It’s merely a collection of Server Groups, irrespective of any Kubernetes clusters that might be included in your underlying architecture.
Server Group
The base resource, the Server Group, identifies the deployable artifact (VM image, Docker image, source location) and basic configuration settings such as number of instances, autoscaling policies, metadata, etc. This resource is optionally associated with a Load Balancer and a Firewall. When deployed, a Server Group is a collection of instances of the running software (VM instances, Kubernetes pods).
Load Balancer
A Load Balancer is associated with an ingress protocol and port range. Traffic is balanced among the instances present in Server Groups. Optionally, health checks can be enabled for a load balancer, with flexibility to define health criteria and specify the health check endpoint.
Firewall
A Firewall defines network traffic access. It is effectively a set of firewall rules defined by an IP range (CIDR) along with a communication protocol (e.g., TCP) and port range.
Application Deployment
Pipeline
The pipeline is the key deployment management construct in Spinnaker. It consists of a sequence of actions, known as stages. Parameters can be passed from one stage to the next one in the pipeline.
You can start a pipeline manually, or you can configure it to be automatically triggered by an event, such as a Jenkins job completing, a new Docker image being pushed in your docker registry, a CRON type schedule, or maybe a stage in another pipeline.
You can configure the pipeline to emit notifications, by email, SMS or HipChat, to interested parties at various points during pipeline execution (such as on pipeline start/complete/fail).
Stage
A Stage in Spinnaker is an atomic building block for a pipeline, describing an action that the pipeline will perform. You can sequence stages in a Pipeline in any order, though some stage sequences may be more common than others. There are different types of stages in Spinnaker such as Deploy, Manual Judgment, Resize, Disable, and many more. The full list of stages and read about implementation details for each provider here.
Deployment Strategies
Spinnaker supports all the cloud native deployment strategies including Red/Black (a.k.a Blue/Green), Rolling red/black and Canary deployments, etc.
What is Spinnaker Made Of?
Spinnaker is composed of a number of independent microservices:
Gate is the API gateway. All the API calls from UI (Deck) and other API callers go to Spinnaker through Gate.
Orca is the orchestration engine. It handles all ad-hoc operations and pipelines.
Clouddriver is responsible for all mutating calls to the cloud providers and for indexing/caching all deployed resources.
Front50 is used to persist the metadata of applications, pipelines, projects and notifications.
Rosco is the bakery. It helps to create machine images for various cloud vendors (for example GCE images for GCP, AMIs for AWS, Azure VM images). It currently wraps Packer, but will be expanded to support additional mechanisms for producing images.
Igor is used to trigger pipelines via continuous integration jobs in systems like Jenkins and Travis CI, and it allows Jenkins/Travis stages to be used in pipelines.
Echo is Spinnaker’s eventing bus. It supports sending notifications (e.g. Slack, email, Hipchat, SMS), and acts on incoming webhooks from services like GitHub.
Fiat is Spinnaker’s authorization service. It is used to query a user’s access permissions for accounts, applications and service accounts.
Kayenta provides automated canary analysis for Spinnaker.
Halyard is Spinnaker’s configuration service. Halyard manages the lifecycle of each of the above services. It only interacts with these services during Spinnaker start-up, updates, and rollbacks.
By default, Spinnaker binds ports accordingly for all the above mentioned microservices. For us the UI (Deck) will be exposed onto Port 9000.
What are We Going to Do?
Set up your environment by launching Cloud Shell, creating a Kubernetes Engine cluster, and configuring your identity and user management scheme.
Download a sample application, create a Git repository, and upload it to a Cloud Source Repository.
Deploy Spinnaker to Kubernetes Engine using Helm.
Build a Docker image from the source code.
Create triggers to create Docker images when the source code for application changes.
Configure a Spinnaker pipeline to reliably and continuously deploy your application to Kubernetes Engine.
Deploy a code change, triggering the pipeline, and watch it roll out to production.
Note: This blog post uses various billable components in GCP like GKE, Container Builder etc.
Pipeline Architecture
To continuously deliver application updates to users, companies need an automated process that reliably builds, tests, and updates their software. Code changes should automatically flow through a pipeline that includes artifact creation, unit testing, functional testing, and production rollout. In some cases, they want a code update to apply to only a subset of their users, so that it is exercised realistically before pushing it to entire user base. If one of these canary releases proves unsatisfactory, the automated procedure must be able to quickly roll back the software changes.
With Kubernetes Engine and Spinnaker, we can create a robust continuous delivery flow that helps us to ensure that software is shipped as quickly as it is developed and validated. Although rapid iteration is the end goal, we must first ensure that each application revision passes through a series of automated validations before becoming a candidate for production rollout. When a given change has been vetted through automation, we can also validate the application manually and conduct further pre-release testing.
After the team decides the application is ready for production, one of the team members can approve it for production deployment.
Application Delivery Pipeline
We are going to build the continuous delivery pipeline shown in the following diagram.
Prerequisites
Fair bit of experience in GCP services like:
GKE (Google Kubernetes Engine)
Google Compute
Google APIs
Cloud Source Repository
Container Builder
Cloud Storage
Cloud Load Balancing
Knowledge in K8s terminology like Services, Deployments, Pods, etc
Familiarity with Kubectl and Helm package manager
Before Starting just enable the APIs needed on GCP
Go to the Console and scroll the left panel down to Compute->Kubernetes Engine->Kubernetes Clusters.
Click Create Cluster.
Choose a name or leave as the default one.
Under Machine Type, click Customize.
Allocate at least 2 vCPU and 10GB of RAM.
Change the cluster size to 2.
Enable Legacy Authorization while customizing the cluster.
Keep the rest of the defaults and click Create.
In a minute or two the cluster will be created and ready to go.
Configure identity and access management
Create a Cloud Identity and Access Management (Cloud IAM) service account to delegate permissions to Spinnaker, allowing it to store data in Cloud Storage. Spinnaker stores its pipeline data in Cloud Storage to ensure reliability and resiliency. If our Spinnaker deployment unexpectedly fails, we can create an identical deployment in minutes with access to the same pipeline data as the original.
1. Create the service account:
$ gcloud iam service-accounts create spinnaker-storage-account --display-name spinnaker-storage-account
2. Store the service account email address and our current project ID in environment variables for use in later commands:
$ exportSA_EMAIL=$(gcloud iam service-accounts list --filter="displayName:spinnaker-storage-account"--format='value(email)')$ exportPROJECT=$(gcloud info --format='value(config.project)')
3. Bind the storage.admin role to our service account:
4. Download the service account key. We will need this key later while installing Spinnaker and we need to also upload the key to Kubernetes Engine.
$ gcloud iam service-accounts keys create spinnaker-sa.json --iam-account $SA_EMAIL
Deploying Spinnaker using Helm
In this section, we will deploy Spinnaker onto the K8s cluster via Charts with the help of K8s package manager Helm. Helm has made it very easy to deploy Spinnaker, it can be a very painful act to deploy it manually via Halyard and configure it.
Use the Helm command-line interface to deploy the chart with the configuration set earlier. This command typically takes five to ten minutes to complete, so we will be providing a deploy timeout with ` — timeout`.
The above command exposes the Spinnaker UI onto the local machine that we’re using to run all the commands. We can use any port of our choosing instead of 8080 in above command. Now the UI can be opened onto the url http://localhost:8080.
Building the Docker image
In this section, we will configure Container Builder to detect changes to the application source code, if yes then build a Docker image, and then push it to Container Registry.
For this step we will use a sample app provided by the Google community
4. Set the username and email address for Git commits in this repository. Replace [EMAIL_ADDRESS] with Git email address, and replace [USERNAME] with Git username.
$ exportPROJECT=$(gcloud info --format='value(config.project)')$ git remote add origin https://source.developers.google.com/p/$PROJECT/r/sample-app
8. Push the code to the new repository’s master branch:
$ git push origin master
9. Check that we can see our source code in the console.
Configuring the build triggers
In this section, we configure Google Container Builder to build and push your Docker images every time we push Git tags to our source repository. Container Builder automatically checks out the source code, builds the Docker image from the Dockerfile in repository, and pushes that image to Container Registry.
In the GCP Console, click Build Triggers in the Container Registry section.
Select Cloud Source Repository and click Continue.
Select your newly created sample-app repository from the list, and click Continue.
Set the following trigger settings:
Name:sample-app-tags
Trigger type: Tag
Tag (regex): v.*
Build configuration: cloudbuild.yaml
cloudbuild.yaml location: /cloudbuild.yaml
Click Create trigger.
From now on, whenever we push a Git tag prefixed with the letter “v” to source code repository, Container Builder automatically builds and pushes our application as a Docker image to Container Registry.
Let’s build our first image:
Push the first image using the following steps:
1. Go to source code folder in Cloud Shell.
2. Create a Git tag:
$ git tag v1.0.0
3. Push the tag:
$ git push --tags
4. In Container Registry, click Build History to check that the build has been triggered. If not, verify the trigger was configured properly in the previous section.
Configuring your deployment pipelines
Now that our images are building automatically, we need to deploy them to the Kubernetes cluster.
We deploy to a scaled-down environment for integration testing. After the integration tests pass, we must manually approve the changes to deploy the code to production services.
Create the application
1. In the Spinnaker UI, click Actions, then click Create Application.
2. In the New Application dialog, enter the following fields:
Name: sample
Owner Email: [your email address]
3. Click Create.
Create service load balancers
To avoid having to enter the information manually in the UI, use the Kubernetes command-line interface to create load balancers for the services. Alternatively, we can perform this operation in the Spinnaker UI.
On the local machine where the code resides, run the following command from the sample-app root directory:
$ kubectl apply -f k8s/services
Create the deployment pipeline
Now we create the continuous delivery pipeline. The pipeline is configured to detect when a Docker image with a tag prefixed with “v” has arrived in your Container Registry.
1. Create a new pipeline named say “Deploy”.
2. Go to the Config page for the pipeline that we just created and click Pipeline Actions -> Edit as JSON.
3. Change the directory to the source code directory and update the current pipeline-deploy.json at path spinnaker/pipeline-deploy.json according to our needs.
$ exportPROJECT=$(gcloud info --format='value(config.project)')$ sed s/PROJECT/$PROJECT/g spinnaker/pipeline-deploy.json > spinnaker/updated-pipeline-deploy.json
4. Now in the JSON editor just copy the whole file spinnaker/updated-pipeline-deploy.json.
5. Click on Update Pipeline and we should have an updated pipeline config now.
6. In the Spinnaker UI, click Pipelines on the top navigation bar.
7. Click Configure in the Deploy pipeline.
8. The continuous delivery pipeline configuration appears in the UI:
Running the pipeline manually
The configuration we just created contains a trigger to start the pipeline when a new Git tag containing the prefix “v” is pushed. Now we test the pipeline by running it manually.
1. Return to the Pipelines page by clicking Pipelines.
2. Click Start Manual Execution.
3. Select the v1.0.0 tag from the Tag drop-down list, then click Run.
4. After the pipeline starts, click Details to see more information about the build’s progress. This section shows the status of the deployment pipeline and its steps. Steps in blue are currently running, green ones have completed successfully, and red ones have failed. Click a stage to see details about it.
5. After 3 to 5 minutes the integration test phase completes and the pipeline requires manual approval to continue the deployment.
6. Hover over the yellow “person” icon and click Continue.
7. Your rollout continues to the production frontend and backend deployments. It completes after a few minutes.
8. To view the app, click Load Balancers in the top right of the Spinnaker UI.
9. Scroll down the list of load balancers and click Default, under sample-frontend-prod.
10. Scroll down the details pane on the right and copy application’s IP address by clicking the clipboard button on the Ingress IP.
11. Paste the address into the browser to view the production version of the application.
12. We have now manually triggered the pipeline to build, test, and deploy your application.
Triggering the pipeline automatically via code changes
Now let’s test the pipeline end to end by making a code change, pushing a Git tag, and watching the pipeline run in response. By pushing a Git tag that starts with “v”, we trigger Container Builder to build a new Docker image and push it to Container Registry. Spinnaker detects that the new image tag begins with “v” and triggers a pipeline to deploy the image to canaries, run tests, and roll out the same image to all pods in the deployment.
1. Change the colour of the app from orange to blue:
$ sed -i 's/orange/blue/g' cmd/gke-info/common-service.go
4. Click Pipelines to watch the pipeline start to deploy the image.
5. Observe the canary deployments. When the deployment is paused, waiting to roll out to production, start refreshing the tab that contains our application. Nine of our backends are running the previous version of your application, while only one backend is running the canary. Now we should see the new, blue version of our application appear about every tenth time we refresh.
6. After testing completes, return to the Spinnaker tab and approve the deployment.
7. When the pipeline completes, application looks like the following screenshot. Note that the colour has changed to blue because of code change, and that the Version field now reads v1.0.1.
8. We have now successfully rolled out your application to your entire production environment!!!!!!
9. Optionally, we can roll back this change by reverting the previous commit. Rolling back adds a new tag (v1.0.2), and pushes the tag back through the same pipeline we used to deploy v1.0.1:
$ git revert v1.0.1$ git tag v1.0.2$ git push --tags
Now that you know how to get Spinnaker up and running in a development environment, start using it already. In this blog, we have done everything from installing a K8s cluster on GCP to deploying an End to End Pipeline just like that in a production environment. Hope you found it helpful.
