We extend our warm congratulations to UiPath on this groundbreaking launch, a moment that redefines not just automation but how businesses operate, innovate, and compete.

When true partners share the same direction and purpose, they don’t just keep pace, they help define what’s next. That’s the kind of partnership we are proud to have with UiPath.

This isn’t just another product release; it’s a shift in what automation can achieve. Agentic Automation raises the bar from simple task execution to orchestrating outcomes that matter, reshaping how businesses drive value across teams and systems. So, what exactly sets Agentic Automation apart, and why should it be on every enterprise leader’s radar?

What Makes Agentic Automation Different and Why It Matters

Until now, most automation was:

• Task-based → Automating repetitive steps, one process at a time
• Rule-driven → Bots followed fixed instructions
• Siloed → Automation often lived within one department (finance, HR, etc.) with limited connection to others
• Rigid → Changing or scaling automation required developer time and testing

With Agentic Automation, that’s changing:

• Smart, self-directed agents
  Traditional bots follow a strict set of rules: “If X happens, do Y.” If something unexpected comes up, they stop or fail.
  Agentic Automation introduces agents that understand context so they can adjust their actions based on what’s happening around them.
  Example: Imagine a customer service agent who not only answers a billing question but also notices the customer qualifies for a discount and applies it automatically. That’s the kind of smart flexibility we’re talking about.
• Goal-driven orchestration
  Before, automation was like handing a worker a checklist. They’d tick off tasks one by one without thinking about the big picture.
  Now, companies can set a business goal like faster customer service or better compliance, and the system figures out the best mix of steps across teams and tools to make it happen.
  Analogy: It’s like going from following turn-by-turn directions to setting a GPS destination and letting it plot the smartest route.
• Multi-agent collaboration across the enterprise
  In the past, automation stayed inside one team, rarely connecting across departments.
  Agentic Automation creates a network of agents across functions, sharing data, coordinating actions, and improving the end-to-end process.
  Example: An order management agent communicates with inventory, shipping, and customer service agents to ensure the customer gets real-time updates and smooth delivery.
• Composable, flexible architecture
  Older systems were rigid and often required businesses to replace or rebuild tools.
  With Agentic Automation, companies plug into their current systems, enabling faster deployment and scaling with less risk.
  Analogy: It’s like adding apps to your smartphone. You don’t need a new phone, just connect what you need.
• Composable, flexible architecture
  Older systems were rigid and often required businesses to replace or rebuild tools.
  With Agentic Automation, companies plug into their current systems, enabling faster deployment and scaling with less risk.
  Analogy: It’s like adding apps to your smartphone. You don’t need a new phone, just connect what you need.

What This Means for Business Leaders vs. Technical Teams

• For business leaders and budget owners:
  → Go beyond small cost savings to transformational impact
  → Reduce cost-to-serve, improve speed and service, and free up teams for higher-value work
  → Build a future-ready, adaptive organization
• For CTOs, CIOs, and technical leaders:
  → Simplify design, deployment, and management with low-code tools
  → Unlock advanced use cases without adding technical complexity
  → Maintain governance, compliance, and transparency at scale
  → Shift focus from maintaining bots to driving continuous innovation

What This Means for Roboyo Clients (and What’s Changing)

At Roboyo, we’ve helped clients:

• Automate high-volume tasks like invoice processing, claims handling, and onboarding
• Build automation centers of excellence to manage governance
• Deliver fast ROI with tools like RPA, process mining, and document understanding

But with Agentic Automation, the landscape shifts:

• We’ll help clients design multi-agent ecosystems where AI agents, robots, and people collaborate across the enterprise
• We’ll guide clients to move from tactical wins to strategic outcomes like improving the full customer journey or creating predictive operations
• We’ll help embed governance, trust, and ethical guardrails to ensure responsible, compliant use of AI at scale
• We’ll support change management, team training, and enablement, ensuring people are ready to work alongside intelligent agents and thrive

With multiple UiPath MVPs on our team and as their Global leading Partner, Roboyo is uniquely positioned to help clients turn this next wave of automation into measurable competitive advantage.

Final Thought

The truth is, even the best technology doesn’t transform companies. People do.

We’re proud to stand shoulder to shoulder with UiPath, working with passion and integrity to help our clients push the boundaries of what’s possible and deliver meaningful outcomes like faster innovation, greater resilience, improved efficiency, and a sharper competitive edge.

👉 If you’re interested in learning how Agentic Automation can help boost your revenue, increase operational efficiency, and strengthen your market position, please contact us today. We’d love to explore how this breakthrough can impact your business.

For years, AI has promised transformation, but in 2025, that promise is finally being fulfilled. The difference? AI is no longer a bolt-on experiment or department-level pilot. It’s being embedded into the core architecture of businesses, reshaping how companies operate, compete, and deliver value.

This year marks the pivot from potential to profit. The shift is no longer about experimenting with AI in isolated use cases, it’s about orchestrating people, processes, and data across the enterprise to drive measurable outcomes.

How AI Capabilities Have Evolved

Year	2020-2023	2024	2025
FOCUS AREA	RPA, Task automation	Generative AI, Copilots, Low-Code + AI tools	Embedded AI, Agentic AI, Hyperautomation (BOAT)
ENTERPISE IMPACT	✔️ Cost reduction – Siloed Automations – Limited Scale	✔️Productivity gains – Isolated Workflow improvements	✔️System-level Intelligence ✔️Profit-Driving Outcomes ✔️Resilience at Scale

“AI today is a different conversation, and companies that treat it as “more of the same” risk falling behind.”

— Sai Vijayendra Madiga, Global Practice Lead in AI

The Shift: From Promise to Profit

AI has matured from an experimental toolset into an enterprise-grade operating model. What’s driving the shift?

• Hyperautomation (BOAT): Business Orchestration and Automation Technologies unify disconnected tools into a connected ecosystem where people, data, and systems align for better outcomes.

• Embedded AI in core workflows: No more scattered bots, AI now lives inside the processes that matter most.

• Agentic AI at scale: Systems that not only automate but dynamically reason, decide, and adapt.

Common Pain Points Holding Companies Back

Despite this progress, many organizations are stuck in the gap between ambition and execution:

• 30% stall at POC: Never making it past initial experimentation by the end of 2025 (source: Gartner)
• Fragmented tech stacks: AI initiatives that don’t talk to each other.
• Siloed Data: Break down barriers with centralized governance
• Misaligned strategy: Automation driven by IT, disconnected from business goals.
• Change resistance: Teams overwhelmed by complexity, unsure how to scale.

These roadblocks slow transformation, but they can be overcome with the right partner.

Why Roboyo

The challenge isn’t just adopting technology, it’s turning it into transformation. That’s where Roboyo comes in.

We help enterprises bridge the gap between ambition and execution, accelerating the journey from POC to scaled success. As a Premier Partner across leading platforms like Appian and UiPath, we bring clarity, precision, and industry-specific expertise to every engagement.

Here’s what sets Roboyo apart:

• Accelerating stuck POCs into scalable enterprise programs
• Mapping automation to real business outcomes, not just technical deployments
• Designing integrated solutions across hybrid tech landscapes
• Providing continuous optimization and change management to maximize long-term value

We don’t replace Appian or UiPath. We unlock their full potential.

Invest in Impact

Investing in enterprise AI is no longer a future ambition. It’s today’s competitive advantage. It’s about embedding intelligence into the core of your business, unlocking new value streams, and building resilience for the future.

At Roboyo, we help organizations move beyond the hype and deliver real, sustained impact. Whether you’re stuck at POC or ready to scale, we’ll help you design, deliver, and optimize transformation across your enterprise.

👉 Book a conversation with us today and take the first step toward reshaping your business with AI.

Surveys consistently show a wide gap between pilot projects and deployed solutions – for example, Gartner predicts 30% of Generative AI projects will be abandoned after proof of concept by end of 2025 due to poor data quality, inadequate risk controls, escalating costs or unclear business value.

This widespread struggle, often called ‘PoC paralysis’, highlights the challenge of transitioning from promising pilot projects to fully scaled AI solutions. To break through this barrier, business leaders must understand the common pitfalls and adopt a deliberate strategy for scaling Gen AI into production.

In this blog, I will explore the key reasons why Gen AI pilots fail and outline best practices for turning promising prototypes into impactful, scalable deployments.

The PoC Paradox: Why Gen AI Pilots Fail to Launch

Even with today’s buzz, most Gen AI experiments struggle to become enterprise-ready applications. Several recurring factors explain why so many proofs-of-concept (PoCs) fail to graduate into production systems:

1. Misaligned Use Cases
   Gen AI’s versatility often leads organizations to prioritize “cool” over “critical.” Gartner’s research shows that stalled Gen AI projects often lacked alignment with measurable business outcomes. Without a clear link to operational efficiency, revenue growth, or risk reduction, PoCs risk becoming science experiments rather than strategic investments.
2. Data Readiness Gaps
   Gen AI thrives on high-quality, contextual data. However, many organizations cite poor data quality or governance as the primary barrier to scaling. Hallucinations, biases, and unreliable outputs often trace back to fragmented or incomplete datasets.
3. Technical and Cultural Silos
   MIT Sloan’s 2023 research found that 70% of Gen AI initiatives struggle with integration into legacy systems and workflows. Worse, only 22% of IT leaders collaborate closely with business units during PoC design. This disconnect results in solutions that lack operational relevance and executive buy-in.
4. Underestimating Scalability Costs
   Scaling Gen AI models requires significant computational resources. Gartner predicts that through 2024, 50% of enterprises will face budget overruns due to unplanned cloud costs. Without careful cost planning, even promising pilots can become financially unsustainable.

From PoC to Production: A Strategic Roadmap

Start Small, Think Big
Focus on “quick win” use cases with high business impact and low complexity. For example, a global retailer client of ours reduced customer service costs by 30% by piloting a Gen AI chatbot for routine inquiries before expanding to other functions.

Invest in Data Strategy & Foundations
There is no AI Strategy without data strategy. Map data ecosystems early. Ensure datasets are clean, labeled, and ethically sourced. Companies with mature data governance frameworks are more likely to scale Gen AI successfully.

Embed Cross-Functional Collaboration
Break down silos by forming agile teams of business leaders, data scientists, and IT specialists. Case studies show that co-developed Gen AI solutions are more likely to secure executive buy-in and user adoption.

Design for Scalability
Partner with cloud providers to architect modular, cost-efficient infrastructure. Use MLOps tools for continuous monitoring and model retraining.

Prioritize Governance and Ethics
Proactively address risks like bias, security, and regulatory compliance. Implement robust auditing frameworks and ethical AI guidelines. According to a 2024 IBM study, 67% of C-suite leaders view governance as a top driver of Gen AI trust.

Conclusion

Generative AI is no longer just a futuristic experiment – it’s a transformative tool for businesses ready to scale beyond proof of concept. However, the high failure rates of AI pilots serve as a cautionary tale: simply building a cool demo is not enough. Success hinges on aligning projects with clear business value, investing in data readiness, and designing for scalability.

For C-suite executives and IT leaders willing to take a strategic, cross-functional approach, the rewards of Gen AI adoption are immense. The winners in the AI-driven era won’t be those who build the most prototypes—they’ll be the ones who turn them into enterprise powerhouses.

Is your organization ready to make the leap?

Why Agentic AI and Automation Matter Post-S/4HANA Migration

S/4HANA gives you a modern ERP system with real-time data processing, an intuitive web interface, advanced analytics, and streamlined operations. But with agentic AI and automation, you can take things to the next level. Here’s how:

1. Smarter Decision-Making with Agentic AI-Powered Insights

Imagine having a system that doesn’t just store data but actually helps you make better decisions. Agentic AI-driven analytics in S/4HANA can proactively analyze trends, predict outcomes, and autonomously recommend the best course of action. Whether it’s forecasting demand, managing risks, or improving customer experiences, AI ensures you’re always a step ahead.

2. Automating Repetitive Tasks for Maximum Efficiency

Nobody enjoys spending hours on repetitive tasks, especially when key users and Business Analysts should be focused on day-to-day operational workstreams and business growth initiatives. With RPA (Robotic Process Automation) and agentic AI-driven workflows, you can:

• Cut down on manual errors
• Speed up business processes
• Free up productive time for more strategic and future growth driven work

Take invoice processing, for example. Automating this task in S/4HANA can reduce cycle times and improve cash flow management—no more delays or human mistakes.

3. Smarter Finance and Accounting Operations

Agentic AI doesn’t just crunch numbers—it transforms financial processes. From automating reconciliations and fraud detection to ensuring compliance, automations driven by AI-powered tools help keep your finance operations running smoothly.

4. A More Resilient and Efficient Supply Chain

Agentic AI and automation can revolutionize supply chain management by:

• Predicting demand to prevent shortages or overstocking
• Automating inventory management to reduce waste
• Optimizing logistics to cut costs and improve delivery times

By integrating AI with S/4HANA, you can build a more agile, cost-effective, customer friendly and resilient supply chain.

5. Better User Experience with AI-Powered Assistants

Chatbots and virtual assistants aren’t just for customer service—they can make life easier for your employees, too. AI-powered assistants with S/4HANA can handle routine HR tasks, answer payroll queries, and help onboard new employees, all while reducing administrative burdens.

6. Proactive IT Operations and Security

Agentic AI-driven automation helps keep your IT operations running smoothly. With AIOps (Artificial Intelligence for IT Operations), your system can:

• Proactively detect anomalies before they become major issues
• Minimize downtime with early issue identification
• Automatically resolve issues through self-healing capabilities
• Strengthen cybersecurity by detecting vulnerabilities in real time

7. Seamless Testing with Test Automation

After migrating to S/4HANA, ensuring continuous system stability and high-quality performance becomes crucial. Test automation plays a key role here. By automating testing processes, you can:

• Speed Up Validation: Automated tests quickly verify configurations, customizations, and integrations, ensuring everything works as intended post-migration.
• Cut Down on Errors: Automated tests reduce human error and are ideal for repetitive, high-volume testing scenarios, such as regression and performance testing.
• Cover More Ground: Automated tests allow for wider test coverage across different scenarios and configurations, ensuring that nothing is overlooked.
• Work Smarter, Not Harder: By reducing manual testing efforts, your team can focus on higher-value tasks, such as improving business processes or analyzing AI-driven insights. Plus, test automation keeps your S/4HANA environment resilient and responsive to new changes, upgrades, or patches, without disrupting business.
• Stay on Track with Monthly Releases: Test all your enhancements and core business processes in the same weekend as the monthly release, instead of dragging it out for weeks and overloading key business users.

Rethinking SAP: From Cost Center to Growth Engine

For too long, SAP systems have been seen as technical resource drains—necessary but costly. But with agentic AI, automation, and test automation, S/4HANA can shift from being just an IT expense to a business accelerator. By automating tedious tasks, unlocking predictive insights, and enabling smarter workflows, SAP becomes a strategic tool that drives revenue, improves agility, and fuels innovation.

How to Get Started

• Identify the Key Areas to Automate – Start small by targeting processes that will benefit the most from AI and automation—like finance, supply chain, or customer service.
• Tap into SAP’s Built-In AI Capabilities – S/4HANA includes powerful AI and machine learning tools like SAP AI Core and SAP Intelligent Robotic Process Automation (iRPA). Leverage these features to get quick wins.
• Integrate Third-Party AI and Automation Solutions – For even more functionality, consider integrating third-party AI and RPA solutions such as UiPath, or AI-powered analytics from Google, AWS, or Microsoft.
• Build a Data-Driven Culture – AI is only as good as the data it works with. Ensure your S/4HANA system is optimized with high-quality, structured data to get the best results.
• Continuously Enhance, Monitor, Test, and Improve – Agentic AI, automation, and test automation aren’t “set it and forget it” tools. As new changes and enhancements roll in, it’s essential to regularly assess performance, track ROI, test for system integrity, and fine-tune your models to maximize value.

Wrapping Up

Migrating to SAP S/4HANA is just the first step. To really unlock its potential, businesses need to embrace agentic AI, automation, and test automation to drive efficiency, improve decision-making, and gain a competitive edge. Think of S/4HANA as your foundation—AI, automation, and robust testing are the tools that will help you build something truly game-changing.

So, are you ready to rethink SAP as a growth enabler rather than just a tech system? Join me, Siva Prasanna Vanapalli, for our upcoming webinar, Sustaining SAP S/4HANA Success Post-Migration on March 12. I guarantee, you’ll get the insights you need to start future-proofing your S/4HANA investment!

The buzz around Gen AI is impossible to ignore and businesses across almost every industry are eager to tap into its potential to enhance operations, boost innovation, and deliver personalized customer experiences. But as organizations rush to implement these powerful tools (at great cost), one critical question often gets overlooked: Is your data ready?

A recent Harvard Business Review Analytic Services survey highlights the gravity of this challenge. Among 646 professionals involved in data decision-making, 39% cited data issues as the top challenge in scaling up Gen AI. Moreover, 30% of organizations that started but later stopped Gen AI projects pointed to data challenges as the primary reason for abandoning their efforts.

These figures reflect a reality I’ve seen time and again: while Gen AI offers enormous promise, it’s only as good as the data it’s trained on.

Maximizing ROI

When it comes to automation, the goal is always to get the most bang for your buck, and Gen AI projects are no exception. A well-trained AI model, powered by clean, relevant data, is key to reducing mistakes. The fewer the errors—whether it’s wrong predictions, flawed designs, or inaccurate outputs—the less time you’ll spend fixing them. And as the saying goes, time is money!

But it’s not just about minimizing mistakes. High-quality data also helps cut down on unnecessary redundancy and leads to smarter decision-making. With better insights, you can use your resources more effectively. All of this adds up to a stronger return on investment, ensuring that your investment in Generative AI truly pays off.

According to the Harvard survey, the key difference between business leaders who have several established Gen AI use cases and those with less or none is their data readiness. Leaders have invested in data foundations that are well-prepared for Gen AI integration. In fact, 56% of these leaders are focusing on improving their data to scale up their AI efforts, setting them up for greater success.

Start with Your Data

At Roboyo, we explored this very topic in our Guide Through the Hype Report last year. My advice then, and now, is simple:

“Start with your data governance and AI strategy before you spend time and money trying out the latest Gen AI tools.”

Why? Because without clean, accurate, and relevant data, Gen AI will deliver only generic outputs—not the tailored insights and solutions your organization needs. To unlock its potential, you must first address the foundation: your data.

The Road to Readiness

Preparing your data for Gen AI isn’t just a technical task, it’s a strategic and cultural transformation. Here’s how to begin:

1. Prioritize Data Governance
   Start by setting clear processes and assigning data ownership roles to make sure your data is consistent, high-quality, and compliant. Data quality frameworks and validation tools—especially those with automation to catch errors early—are key to keeping things on track.
2. Reframe How Your Organization Thinks About Data
   Set up a change management program to help re-train your organization. This could be as simple as offering basic training, getting leadership on board, or encouraging more departmental collaboration. The key is to help everyone see that data is the backbone of AI, and that building a data-driven culture together is crucial for long-term success.
3. Keep Humans in the Loop
   While Gen AI can automate and generate insights, human oversight around data is critical to refine and contextualize outputs. AI works best when paired with human judgment.
4. Start with a Clear Strategy and Use Case
   Before diving into Gen AI, define a specific use case and establish a data strategy, ideally led by your Chief Data Officer or IT department. While experimentation is valuable, starting without a clear plan for your data often leads to wasted resources and missed opportunities.
5. Consider the Value of a Data Platform
   You don’t need a dedicated data platform to make Gen AI work but having one can make a huge difference in how efficient, scalable, and effective your results are. As we’ve already mentioned in this blog, data is the backbone of Gen AI. It’s what powers the ability to generate meaningful, contextually relevant, and creative outputs. To get the most out of it, the right data platform is essential. It should give you easy access to diverse, high-quality, and well-structured datasets, that are clean, unbiased, and representative of the problem you’re trying to solve.

The Future of Gen AI

Data challenges should never deter you from exploring Gen AI; rather, they should serve as a wake-up call. The success of any AI initiative depends on the quality of the data it relies on.

At Roboyo, we’ve seen firsthand how businesses can overcome these challenges by focusing on the fundamentals. A strong foundation of clean, structured, and well-governed data enables Gen AI to go beyond generic results and deliver the tailored solutions that organizations truly need.

So, before you leap into the latest Gen AI tools, take a step back. Assess your data readiness, establish robust governance, and align your AI projects with a clear strategy. Gen AI’s potential is immense—but to harness it, you must start with your data.

Ready to take your Gen AI projects to the next level? Together, let’s build a data foundation that sets your organization up for success. Book a quick call with our team today.

Continuous Value Creation: Automation and AI’s Role

One of the hallmarks of successful digital strategies is their ability to continuously deliver value. Organizations are consistently introducing new features and adapting services to remain relevant and transformative. Intelligent Automation and AI amplify this value by streamlining repetitive tasks, enhancing efficiency, and enabling organizations to focus on innovation.

For example, automated workflows and AI-powered analytics allow businesses to:

• Accelerate decision-making by reducing manual intervention and leveraging predictive insights from AI.
• Improve accuracy by eliminating human error in routine processes while using AI to detect anomalies in real time.
• Adapt swiftly to market changes by scaling processes dynamically and applying AI to identify emerging trends.

However, as automation and AI expand across business processes, they also increase the potential attack surface. Managing the security of these advanced technologies must be a top priority to maintain trust and continuity of business processes.

The Rising Imperative of Cybersecurity

Digital ecosystems thrive on interconnectedness, but this very strength creates vulnerabilities. With ransomware threats surging and the complexities of managing multiple interlinked systems, businesses must adopt a proactive security posture. Here’s why cybersecurity is critical:

1. Ransomware Evolution: Cybercriminals are leveraging sophisticated techniques to exploit vulnerabilities in digital platforms. Protecting sensitive data through robust encryption and regular vulnerability assessments is non-negotiable.
2. Complex Interlinkages: Dynamic digital environments often require integration across numerous systems. While this enhances operational efficiency, it also means a single breach can compromise an entire network.
3. Data Privacy Regulations: Compliance with regulations such as GDPR or CCPA necessitates stringent security measures. AI-powered automation tools that enforce these rules can simplify compliance while safeguarding sensitive information.

AI plays a pivotal role in mitigating these risks. For instance, implementing AI-driven security monitoring and incident response ensures rapid detection and containment of threats, reducing the potential impact on business operations.

Building a Human+ Advantage: The Role of Training

While technology serves as the backbone of cybersecurity, its success often hinges on human factors. That’s why, at Roboyo, we emphasize fostering a culture where a hybrid Human+Digital workforce thrives. By developing a “human firewall” through comprehensive employee training, organizations can significantly enhance their defense against evolving cyber threats. Leveraging automated and AI-enhanced training platforms makes this process both efficient and scalable:

• Security Awareness Programs: Regular, AI-curated training modules educate employees on recognizing phishing attempts, malware risks, and other threats.
• Accountability Mechanisms: Automated reminders and compliance checks ensure employees adhere to security best practices.
• Simulated Threat Scenarios: AI-driven tools that simulate real-world attacks enable employees to respond effectively, reducing the likelihood of breaches.

By aligning employee behavior with security requirements, companies can create a resilient culture that complements their technological defenses.

Actionable Steps for 2025

As we move through 2025, businesses must align their strategies with robust security frameworks, AI integration, and compliance standards. Here’s how:

Integrate Automation, AI, and Security: Leverage AI-powered tools that monitor and enforce security protocols across systems, providing predictive insights to proactively prevent breaches. Integrating Agentic AI into cybersecurity frameworks can also significantly enhance an organization’s security posture.

Adopt Zero Trust Principles: Implement identity verification and least privilege access to minimize potential breaches, enhanced by AI’s ability to detect unusual access patterns and automate threat detection.

Invest in Employee Training: Utilize AI-enhanced learning platforms to continuously update security knowledge and practices.

Conduct Regular Audits: Use automation and AI to schedule and execute compliance checks, ensuring systems remain secure and up to date. Agentic AI can also be leveraged for automated incident response.

Collaborate with Vendors and Partners: Work closely with stakeholders to understand their security measures and ensure seamless integration with your tools.

Conclusion

The convergence of cybersecurity, automation and AI, and compliance offers unparalleled opportunities for growth and innovation. However, it also demands a vigilant approach to risk management. By prioritizing value creation, integrating automation and AI, and fostering a security-aware workforce, businesses can confidently navigate the challenges of cybersecurity and compliance in 2025 and beyond. The question is no longer whether you should prepare but how effectively can you adapt to stay ahead.

If you’re ready to take your cybersecurity and compliance strategies to the next level, book a meeting with Roboyo’s experts today. Our team is here to help you navigate the complexities of security and automation, ensuring your organization stays resilient and ahead of the curve.

Q: Let’s cover the basics first, Siva. Where are we with the SAP migration deadlines?

Siva: Deadlines can really depend on how ready an organization is and how complex the migration process will be. That said, SAP has set 2027 as the target for moving to S/4HANA, with extended support available until 2030. While that might feel far off, migrations typically take anywhere from 18 to 42 months depending on how many systems or sites to be migrated, so my advice? Start the process sooner rather than later. It’ll save you from scrambling at the last minute!

Q: Why is testing so crucial in SAP migration projects?

Siva: Testing is the backbone of any successful SAP migration. It ensures that the new environment functions as expected, integrates seamlessly with other existing or new systems, and supports the organization’s unique business processes. By identifying issues early, testing minimizes risks such as business disruptions, data inconsistencies, and compliance violations.

Q: What are the biggest challenges organizations face during SAP migration testing?

Siva: In my experience, there are a few key challenges that tend to come up during SAP migration testing. If I had to highlight the main ones, I’d say…

• Extensive Testing Requirements: Functionality within SAP apps and eco systems is huge and extremely complex. SAP migrations require multiple testing layers, including unit testing, integration testing, regression testing, performance testing, and user acceptance testing (UAT). It’s a lot to cover!
• Integration: While SAP itself is huge, the integration points are countless. Big organizations would typically have 1000+ integration points, and all of them need to be tested as part of a successful migration journey.
• Ensuring Data Integrity: Verifying that the migrated data is accurate is essential but can be really time-consuming and prone to errors.
• Minimizing Downtime: Extended testing phases can impact business operations, so finding ways to minimize downtime is crucial.
• Validating Customizations: Custom workflows often need specialized testing to ensure they still work as expected after the migration.
• Complex Business Processes: End-to-end processes often span multiple modules, meaning you need thorough testing to make sure everything integrates properly.
• Handling Frequent Updates: Monthly patches, regular updates, and changes or enhancements require continuous validation to ensure everything stays on track.
• User Acceptance Testing (UAT): Getting users involved in UAT can be a significant time investment, but it’s necessary for a successful migration.
• Compliance: Ensuring all regulatory and governance requirements are met adds another layer of complexity to the process.

Q: How can test automation help address these challenges?

Siva: There’s no denying that test automation is a game-changer for SAP migration projects. For one, it speeds up the entire testing process, allowing teams to validate functionality much quicker than manual methods ever could, and in most cases, it helps to deliver migrations quicker than planned. It also boosts accuracy—automated scripts eliminate the risk of human error in repetitive tasks.

Another huge advantage is cost efficiency. By reducing the need for manual effort, automation helps cut down on testing costs. It also ensures comprehensive coverage, making sure all critical scenarios are thoroughly tested, so nothing slips through the cracks. And when integrated with Continuous Integration/Continuous Deployment (CI/CD) pipelines, it enables continuous testing throughout the project, keeping everything on track and error-free from start to finish.

It’s pretty clear I’m a big fan of this approach!!!

Q: Can you share any examples of how automation solves specific testing challenges?

Siva: Absolutely!

• Extensive Testing Requirements: Automated frameworks can run thousands of test cases at once, ensuring complete coverage without requiring manual intervention.

• Data Integrity: Automation tools can efficiently and accurately validate migrated data, comparing the source and target systems at scale.

• Downtime: By running tests in parallel, automation reduces validation time, helping minimize any disruptions to business operations.

• Validating Customizations: Tools like process mining can map out workflows and automatically generate test cases to validate custom functionalities.

• Complex Business Processes: Automation makes end-to-end process validation easier by simulating real-world scenarios across different modules.
• Frequent Updates: Automated regression testing ensures that the system remains stable, and functions as expected after each monthly or on-demand update.

Q: What role does Roboyo play in helping organizations simplify SAP migration testing?

Siva: At Roboyo, our mission is simple: we’re here to make SAP migrations as smooth and stress-free as possible with our Shift-left testing approach. (Quick plug: I did a webinar with UiPath about this a while back!). We’re all about helping our clients get the most out of their investment.

We use advanced tools like UiPath to handle the heavy lifting, and with Agentic AI now in the mix, we’re able to enhance the process even more. With UiPath’s Autopilot™, we can boost testing from start to finish by converting manual tests into automated UI or API tests, using low-code or coded options. It also helps us generate test data, refactor automation, fix validation errors, generate expressions, perform fuzzy verifications, and auto-heal tests during runtime.

UiPath’s advanced orchestration capabilities make it easy to connect all these phases seamlessly and by automating functional and regression testing, we slash testing cycles while maintaining high accuracy and stability. We also rely on automation to validate data, ensuring data integrity and minimizing errors throughout migration.

When it comes to user acceptance testing (UAT), UiPath helps us simulate real-world user interactions by reusing already developed assets, creating a realistic testing environment and ensuring a smooth post-migration experience for end users.

Compliance is a top priority for us, too. UiPath’s robust security framework includes defense-grade security, Role-Based Access Control (RBAC), end-to-end encryption, and Veracode-certified code. Trusted by over 50 government agencies, it gives our clients peace of mind knowing their data and processes are secure and fully compliant.

Q: What advice would you give to organizations planning SAP migration?

Siva: My top piece of advice is to invest in test automation right from the start of the migration project. It helps with shift left to reduce risks and ensures your system can scale for future updates. Also, it’s crucial that your team has a strong understanding of both SAP systems and automation technologies, as they go hand in hand. If there are any knowledge gaps, consider upskilling your team—it’s an investment that will definitely pay off in the long run.

Conclusion

SAP migration testing is essential for successful system transformation. Sure, there are challenges along the way, but test automation makes it a lot easier. By working with Roboyo, organizations can access best in class automation tools and expert guidance, ensuring a faster, smoother, and more reliable migration.

Big thanks to Siva for sharing these great insights! If you’re planning an SAP migration, don’t hesitate to get in touch or check out Siva’s webinar to learn how Agentic AI can revolutionize your SAP migration testing.

Test automation isn’t just an option, it’s the key to unlocking next-level success.

This experience got me thinking about the 5 Degrees of Agentic Automation, a framework that maps out the evolving relationship between humans and machines. It’s not just about what automation can do, but about how it’s designed to collaborate with us—balancing innovation with trust, accountability, and ethics. Let’s take a closer look at these five degrees and what they mean for the future.

The 5 Degrees of Agentic Automation

1. Traditional Automation

At this foundational level, there’s no true agency or decision-making on the machine’s part. Automation here is straightforward: machines follow static, pre-programmed rules, and humans remain in full control.

A common example in travel is manually entering flight details or scanning luggage tags. These systems are reliable but rigid—they work well for routine tasks but lack the ability to adapt if something unexpected happens. Traditional automation is often seen as a stepping stone, offering consistency and efficiency but requiring humans to step in for anything outside the norm.

2. AI-Augmented Automation

This is where machines start to show basic agentic behavior, offering tools that assist humans in performing specific tasks. The focus here is on making human jobs easier, faster, and more efficient, but the machine’s role is still limited to specific scenarios.

For instance, self-service kiosks at airports help passengers scan boarding passes or check in luggage, while AI-powered chatbots handle straightforward queries on airline websites or apps. Generative AI, like ChatGPT or Google Gemini, often comes into play here—providing instant answers to passengers’ frequently asked questions. While helpful, these systems are largely reactive and require human intervention for anything complex.

3. Augmented Automation (Task Specific)

This level represents a significant leap forward. Machines now exhibit task-specific agentic behavior, actively collaborating with humans in real-time. The interaction is dynamic, with machines handling increasingly complex tasks while humans oversee or assist as needed.

In aviation, this might look like AI systems rerouting flights to avoid weather disruptions or optimizing airplane fuel consumption for more sustainable travel. Generative AI models personalize the travel experience further, suggesting custom itineraries based on a passenger’s preferences or streamlining rebooking processes after a cancellation. This level of automation fosters a true partnership between humans and machines, enhancing both efficiency and personalization.

4. Plan and Reflect

At this stage, machines operate with constrained autonomy, managing tasks independently within defined boundaries. Human involvement shifts from direct control to supervisory roles, ensuring oversight during critical moments.

Consider airplane autopilot systems. These systems can manage most of the flight—maintaining altitude, speed, and navigation. However, pilots remain in charge during crucial phases like takeoff and landing or in unexpected situations. This level of automation relies heavily on trust and is designed to handle predictable scenarios while leaving the unpredictable to human experts with years of training and real-world experience.

5. Autonomous Automation

The final degree of Agentic Automation represents full autonomy—machines operating independently with minimal to no human intervention. At this level, machines can make decisions, adapt to changes, and even solve unforeseen problems.

Imagine a fully autonomous airport, where AI systems handle everything from scheduling flights to managing security and even piloting planes. While the potential for efficiency, safety, and scalability is immense, this level also requires significant safeguards, rigorous testing, and a foundation of trust. Without human involvement, the stakes are higher, making transparency and ethical considerations non-negotiable.

Beyond Automation: Keeping Humanity at the Core

What makes this framework truly powerful is its human-centric approach, something we at Roboyo call Human+. As automation progresses through these degrees, it’s not just about what machines can do, it’s about how they empower us.

At every stage, human judgment, ethics, and empathy must remain central. Machines can perform tasks, but humans bring the critical thinking and emotional intelligence needed to guide them responsibly. The goal isn’t to replace humans but to enhance our capabilities, enabling us to focus on meaningful, impactful work while automation handles repetitive or complex tasks.

The Future of GenAI and Agentic Automation

The possibilities for Generative AI and Agentic Automation are endless. In the travel industry, these advancements could lead to a future where journeys are seamless, personalized, and even more sustainable. Beyond aviation, industries like healthcare, manufacturing, and finance are already exploring how these technologies can revolutionize the way we live and work.

But with great potential comes great responsibility. As leaders, we must ask ourselves:

• How do we ensure that automation serves humanity, not the other way around?
• How do we balance efficiency with trust, reliability and accountability?
• How do we design systems that are both innovative and ethical?

Let’s Continue the Conversation

So, what level of automation excites you the most? How do you see Generative AI and Agentic Automation reshaping your field?

We at Roboyo would love to hear your thoughts – book a meeting with one of our team here.

Here’s how AI-powered automation can help your business plan for a better Q1, even amidst the holiday hustle:

1. Real-Time Data Insights for Smarter Automation

The holiday season generates a wealth of data on customer behavior, sales performance, and marketing effectiveness. Automation tools driven by AI can:

• Analyze customer preferences in real-time: AI-powered automation systems can process large volumes of data to uncover trends, such as the most popular products or services.
• Automate performance tracking: Advanced analytics platforms can automatically provide insights into the effectiveness of campaigns, feeding directly into dashboards for decision-making.
• Enable predictive automation: AI models can forecast trends and demand for Q1, allowing automated systems to adjust inventory levels or marketing priorities accordingly.

These capabilities ensure that your business stays agile and well-informed, even during peak holiday activity.

2. Automated Inventory Management

One of the biggest challenges during and after the holiday season is managing inventory effectively. AI-powered automation technologies excel at:

• Demand forecasting: Automated systems use AI algorithms to predict future inventory needs based on historical and real-time data.
• Supply chain optimization: AI-integrated platforms can automate supply chain workflows, ensuring timely restocking of high-demand products.
• Return process automation: Post-holiday returns can be managed efficiently with AI tools that automate workflows, from processing returns to restocking items or issuing refunds.

By automating inventory management, businesses can save time, reduce errors, and meet customer demand seamlessly.

3. Turning Holiday Shoppers into Loyal Customers

The influx of holiday shoppers is a prime opportunity to build long-term customer relationships. AI-powered automation can enhance this effort by:

• Automating customer segmentation: AI can group customers based on behavior, allowing automated systems to deliver personalized post-holiday campaigns.
• Driving personalized marketing: AI automation platforms can automatically send tailored messages, discounts, or product recommendations to customers, increasing engagement.
• Building loyalty programs: AI can design and automate the execution of rewards programs, tracking customer interactions and providing timely incentives.

These automated efforts maximize customer retention and drive sustained growth into Q1.

4. Streamlining Employee Workflows with AI Automation

The holiday rush often leaves employees juggling multiple priorities, which can impact productivity in the new year. AI-powered automation technologies can lighten the load by:

• Automating repetitive tasks: From order processing to report generation, AI-driven automation handles time-consuming activities, freeing employees to focus on strategic work.
• Optimizing workforce scheduling: AI systems can automate shift planning, ensuring proper staffing without overburdening employees.
• Providing actionable insights: Automated tools can generate performance metrics and suggestions, enabling employees to make informed decisions quickly.

By automating these processes, businesses can maintain employee morale and efficiency as they transition into Q1.

5. Financial Planning Through Automated Insights

AI-powered financial automation tools are invaluable for planning budgets and cash flow for Q1. These technologies can:

• Automate revenue projections: AI systems use holiday sales data to estimate future revenue streams, feeding insights directly into financial dashboards.
• Streamline cost analysis: Automated tools can identify cost-saving opportunities, such as optimizing shipping methods or renegotiating supplier contracts.
• Facilitate scenario planning: AI-powered automation can simulate various business scenarios, allowing leaders to prepare for potential challenges in Q1.

With automated financial insights, businesses can make strategic decisions with confidence.

6. Enhancing Customer Support with Automation

The holiday season can overwhelm customer support teams, but AI-powered automation ensures smooth operations and a seamless experience:

• Automated chatbots and virtual assistants: These tools can handle common inquiries, reducing response times and freeing up human agents for complex issues.
• Sentiment analysis automation: AI tools can analyze customer feedback in real-time, providing actionable insights to improve service quality.
• Proactive issue resolution: Automated systems can predict potential issues, such as delivery delays, and proactively notify customers with solutions.

Automation in customer support not only improves holiday experiences but also sets the stage for stronger customer loyalty in Q1.

Conclusion

The holiday season isn’t just about maximizing short-term gains, it’s an opportunity to set the stage for a successful Q1. By leveraging AI-powered automation technologies, businesses can enhance efficiency, gain actionable insights, and improve both employee and customer experiences.

As you prepare for the holidays, consider integrating AI-driven automation tools into your operations. Not only will they help you manage the festive chaos, but they’ll also provide the foundation for a strong start to the new year, turning seasonal success into sustained growth.

Reach out to the Roboyo team today to find out how!

In my experience, there’s one big factor that often determines the outcome: how well the CEO and CIO work together. It sounds simple, but it’s crucial. Based on my experience, when the CEO and CIO are aligned, companies have an estimated 70% or higher success rate in achieving their digital transformation goals. Without that alignment, things get shaky fast. A CEO who just “supports” the CIO could see the success rate fall to around 50%. And when the CIO tries to go it alone? Then the success rate sinks well below that. 

So, why is it so important that these two leaders are in lockstep? Let’s break it down.

The Changing Role of the CIO: From IT Expert to Business Driver

First, it’s important to understand that the role of the CIO has changed in a big way. The CIO used to be the go-to tech wizard, handling IT infrastructure and applications with deep technical knowledge. Today, though, the CIO is a business leader as much as a tech expert. They’re responsible for making the case for digital investments and showing the board how these projects will drive ROI. In other words, the modern CIO has to connect technology with business outcomes—and communicate those connections clearly.

This shift has turned the CIO into a true strategic partner for the CEO. They’re no longer just in the background supporting the business; they’re helping to shape it. For this relationship to work, the CEO and CIO need an open, ongoing conversation, built on respect and a shared understanding of each other’s strengths. Without that, digital transformation can easily fall short.

Championing Transformation Across the Business

Here’s where the CEO’s role is irreplaceable. While the CIO might bring technical expertise, the CEO is at the heart of digital transformation because they’re responsible for inspiring a company-wide culture shift. Digital transformation isn’t just a tech upgrade; it’s a new way of doing business. For the transformation to work, everyone—from the board to frontline employees—needs to buy into this new vision. 

And it’s up to the CEO to “own” this vision. They need to communicate why these tech investments are being made and what they mean for the company’s future. This isn’t a one-and-done conversation, either; it’s about consistently reinforcing the message and driving that shift in mindset. When the CEO is fully on board, the whole organization is more likely to follow suit, creating the cultural support the CIO needs to get things done. 

Communication and Mutual Respect

For a digital transformation to be successful, the CEO and CIO need to build a partnership based on open communication and mutual respect. CEOs know what the business needs to achieve and where the company needs to go. CIOs know what technology can do to help make that happen. By respecting each other’s areas of expertise and staying in sync, the CEO and CIO can develop a clear, actionable strategy that aligns with both business goals and technological possibilities. 

This partnership has to be ongoing. Digital transformation is a journey that requires consistent collaboration and alignment. When the CEO and CIO are in constant communication, they can adapt together to new challenges and ensure the transformation stays on track. 

Why Alignment Matters 

Our data on success rates shows just how much CEO-CIO alignment impacts outcomes: 

• Over 70% Success Rate When in Sync – When the CEO and CIO are fully aligned, companies see a much higher success rate in achieving digital transformation goals. This level of collaboration allows them to make joint decisions quickly, solve problems together, and drive the organization forward as a united front. 
• 50% Success Rate with CEO Support Only – When the CEO is supportive but not fully engaged, we see the success rates drop to around half. This can lead to digital initiatives that lack strong executive backing and struggle to gain momentum, resulting in limited progress and internal friction. 
• 30% Success Rate When the CIO Goes Solo – When the CIO tries to lead the transformation alone, more often than not the success rate plummets. Without the CEO’s active involvement, these efforts often face internal resistance and resource challenges that the CIO alone can’t overcome. A transformation led by one person, no matter how skilled, can’t effectively change an entire organization

Building a Lasting CEO-CIO Partnership 

For companies to succeed in today’s digital-first world, the CEO and CIO need to work together, sharing a vision and a commitment to driving meaningful change. Digital transformation isn’t just about implementing new technologies; it’s about reshaping the way an organization works and delivers value. When the CEO and CIO are aligned, they create a company that’s agile, tech-savvy, and ready to tackle the challenges of the future. 

From my experience, nothing sets a company up for transformation success like a strong, trusting partnership between the CEO and CIO. When they’re working together, a company can do more than just adapt to change—it can lead it.

Partner with Roboyo to strengthen your CEO-CIO partnership and drive lasting change – book a meeting with our team today.