1. From Digital-first to AI-first: Organizations approaching AI implementation only as software deployment are facing challenges in extracting meaningful value. AI requires a shift in mindset from digital first to AI first, so that systems are not just AI-enabled but become AI-native. To unlock its true value, AI must be wired deeply into the core of the organization, into the workflows and the decision-making process so that systems are learning continuously and adapting by using its intelligence.

For instance, while working with a large banking customer we re-designed workflows to optimize them. One of the objectives was to decouple different channels to facilitate agentic AI to perform jobs such as updating a customer profile across channels and have a seamless view of customer engagement.

2. From bottom line to top line growth: It is time to reframe the AI value proposition as a core lever to accelerate business growth by empowering teams and processes. Focusing on AI as a tool for cost-savings and productivity limits the scope to create value. AI discussions focused on the bottom line also create uncertainty as it leads to the inevitable question of reducing employees. Often there are unrealistic expectations from cost-cutting gains which are hard to realize at scale, while growth-focused use cases tend to deliver more tangible impact.

Our experience find that when AI conversations are about empowering teams and creating possibilities such as hyper-personalized marketing and dynamic pricing mechanisms, AI experiences faster uptake. Initiatives tied to top-line growth—such as customer engagement, marketing automation, or contact center scalability—face fewer barriers as these use cases do not threaten existing jobs. Instead, it expands capacity and reach. For example, AI-powered agents may have lower conversion rates than humans, but they can operate at scale with no limit on outreach volume. Even marginal gains across a larger funnel contribute meaningfully to revenue.

3. From Large Language Models to Small Language Models: Organizations that have better navigated the AI curve understand the AI models must be tailored to the needs of the organization, and the accuracy of the outcomes is closely linked to the data it is fed. While it is tempting to rely on off-the-shelf LLMs, it is difficult to create differentiated value through these models. Small language models on the other hand are customized, vertical-specific, and tailored to the needs of the organization deliver impactful outcomes.

Working with a large B2B marketplace, we understood that our client needed a retrieval augmented generated (RAG) model that is built and trained on its platform-specific requirements. This is where the data strategy comes into play, and we helped the customer re-architect to meet AI-specific goals.

In another case, we have helped a banking customer to prepare the data for AI by starting with data classification. We embedded intelligence into the process to classify compliant-bound data and less sensitive data and built a solid foundation for the next steps. Once data was categorized, we could strategize how to harness the data and explore use cases for automation, analytics and AI in a structured and compliant manner. We empowered the customer to harness even non-critical data to identify opportunities for automation and enhance efficiency.

Thriving with an AI DNA

Implementing AI goes beyond layering intelligence on top of existing systems. Organizations must embrace an AI-native mindset and implant AI into the DNA of the organization—to include efforts towards re-designing the architecture, re-engineering workflows, and embedding intelligence into decision making.

Infusing AI into the way organizations think and act creates new possibilities. Forward-looking companies are rolling out AI deployments as a strategic initiative with a long-term vision of how it will shape business outcomes, while building robust data foundations with thoughtful implementations. By empowering employees to work alongside AI, organizations are crafting winning strategies by amplifying human ingenuity with AI.

BRIDGING GAPS IN DATA ASSURANCE – SIX CHALLENGES AND HOW TO SOLVE THEM

1 Poor Quality Data undermines trust and decision-making

When data is flawed, the best laid plans go haywire, however meticulous the execution may be. This can have far reaching consequences in different industries—in audit and tax, poor data quality can lead to non-compliance and misreported filings while poor quality data in insurance hampers risk assessment.

How NewVision Fixed: Implement quality gates across the data lifecycle for continuous data validation to catch errors early, build confidence and prevent downstream impact.

2. Lack of sufficient test data limits coverage and risk detection

Siloed data sources and regulatory restrictions such as HIPPA and GDPR create data paucity. It is difficult to get sufficient data such as frauds, anomalies in claims or production defects. Test coverage cannot simulate real-world complexities and becomes incomplete without large and diverse data sets.

How NewVision Fixed: Use AI-generated synthetic data to overcome lack of data diversity. AI uses advanced technologies such as LLMs and Generative Adversarial Networks to mimic the structure and property of real-world data without compromising privacy or data confidentiality.

3. Tool-specific automation leads to siloed validation, redundant effort, and fragmented visibility

While tool-specific point solutions accelerate tasks in isolated cases, it does not support holistic data validation. For example, ETL, BI and application teams will test data separately resulting in redundant  efforts, gaps in testing standards, delayed error detection, root cause analysis and impact assessment.

How NewVision Fixed: Implement unified testing automation framework across UI, API and data layers for reliable and scalable testing framework that cuts across tools, environments and workflows. It provides end to end visibility ensuring traceability, robust governance and higher re-usability of testing assets.

4. Manual report validation is slow, error-prone, and unscalable

Manual data checking across dashboards, applications and spreadsheet is time consuming and unscalable. As applications grow, complexities increase giving rise to inconsistent validation and absence of traceability.

How NewVision Fixed: Implement automated reporting with custom tooling and strategies with tailored QA frameworks mapped with unique business logic, thresholds and data relationships.

5. Overdependence on UI testing leads to slower feedback and fragile tests

UI testing cannot validate underlying data in the pipelines, ETL, API and databases, or test the business logic. UI test validates only what is visible on the screen.

How NewVision Fixed: Embrace a testing pyramid optimized for data-heavy applications with 70% data layer automation, 20% API, and 10% UI. Most data-heavy applications embed business logic at the ETL, aggregation and transformation stages, so focus testing on data ingestion, schema validation, integrity, consistency across sources, data drift.

6. Misaligned team skill sets result in poor automation adoption and inconsistent quality

Businesses often falter in acquiring the right automation and testing skills, leading to inconsistent test coverage and unreliable automation. For example, QA professionals with expertise in UI testing lack exposure in API testing and data validation.

How NewVision Fixed: Deploy a test skill pyramid including UI, API and data layers, supported by a structured organizational model to facilitate collaboration, ownership and continuous learning. A Center of Excellence provides training; organizes re-usable libraries and best practices; and maintains testing frameworks and tool alignment.

Strong Data Test Strategy is Key to Building Business Trust

Business decisions are only as good as the insights that power it. Today, trustworthy data is not just essential, but the cornerstone of a high-performance organization. If data is the new oil, trust is the new gold.

A digital strategy sans data assurance strategy will wobble as it scales and send ripples across the organization. As organizations scale AI-powered digitalization from pilot to production, the time to get started with data  assurance is now, sooner than later.

If you want to know more about data assurance or how NewVision can transform the data testing strategy, write to us at contact@newvision-software.com.

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Use Cases Where Agentic Automation is Creating Impact

From customer care, supply chain and cyber security, Agentic AI is the most watched application in AI development. According to Deloitte’s State of Generative AI report, 26% respondents have stated that their organizations are actively exploring Agentic AI applications and use cases.

NewVision’s team of experts have helped clients build autonomous multi-agent systems by adding role-specific agents such as customer-care specialists or financial analysts and by integrating with external APIs such as financial market data. Below we share our perspectives on new possibilities with Agentic AI in different applications.

Customer Support: A new level of efficiency with NLP capabilities, context-aware information retrieval and intelligent escalation with Agentic AI will allow customer service organizations to focus on higher value tasks such as empathy, escalations and relationship building.

Financial Services: Real-time monitoring of transactions to flag anomalies, security breaches and compliance, while faster data reconciliation of financial transactions will empower human analysts to focus on better strategic planning, risk mitigation and investment opportunities.

Supply Chain Management: High level of operational efficiency, cost savings and better preparedness with demand forecasting, dynamic route optimization and close co-ordination across the supply chain and logistics and transportation sector to navigate disruptions due to geo-political risks, natural calamities and volatility.

Cyber Security: With increased cyber security attacks, Agentic AI elevates the defense system with continuous monitoring and remedial action such as shutting down a vulnerable port, a compromised system, or denying access to intrusion. At the same time, it helps to fortify IT systems with predictive analysis and recommendations.

Scripting a Successful Autonomous Strategy  

Agentic AI is the most interesting amongst GenAI applications enabling AI to access multi-modal types of information and the ‘agency’ to act independently by orchestrating complex workflow, coordinating with other agents, while learning continuously based on feedback loop.

Agentic AI is a shift from a reactive mode to proactive mode and so organizations are seeking to create competitive differentiation by focusing on areas that are uniquely critical to the success of business. A strategic approach wherein automation is viewed as a continuum is necessary to systematically harvest the benefits of RPA, while experimenting with new capabilities of Agentic AI.

The move from automation to autonomy is inevitable and forward-thinking organizations are already exploring how AI agents can augment their teams. However, Agentic AI faces the same set of challenges as GenAI such as regulatory uncertainty, building trust and transparency, data deficiency and talent issues and must design a comprehensive strategy that carefully considers the challenges while navigating the journey from automation to autonomy.

Digitalization is entering into a new phase of evolution—one in which AI is continuously learning and achieving new levels of autonomy across the organization. Even though Robotic process automation (RPA) has dramatically increased efficiency and enabled businesses to automate high-volume repetitive tasks, there are inherent limitations when multiple processes are involved. Agentic AI is turning these limitations on its head injecting flexibility, adaptability and enhanced capability to deal with complex scenarios, which often entails judgement, and to take actions independently based on intelligent analysis and continuous learning.

Agentic AI will have massive ramifications in traditional systems and processes, specifically in service interactions. Within the next four years, i.e. by 2029, 80% of customer service issues will be resolved independently without human intervention, according to Gartner.

The Shift from Rule-Based Bots to Intelligent Agents

The contrast between RPA and Agentic AI could not be more stark—while RPA works in the background Agentic AI will bring autonomy to the front and center of organizations. RPA deployment is defined via coding, and it interacts with the software through a user interface—similar to how humans interact. While the core of Agentic AI is designed with a human-like architecture which enable it to process unstructured data, apply reasoning and execute tasks in complex workflows with precision.

To put it simply, Agentic AI extends the capabilities of RPA by embedding intelligence into the automation process. It mimics human cognitive capabilities and operates in complex environments to take independent decisions, much like a human brain. The autonomy of AI systems marks a profound shift in enterprise technology empowering organizations to be more intention driven.

Cloud Migration Challenges

Migrating workloads to the Cloud is fraught with challenges, and organizations that understand these challenges are better equipped to deal with the migration. Despite the obvious, often the absence of a comprehensive Cloud migration strategy leads to sub-optimal outcomes. A comprehensive strategy should include defining the objectives, selecting the right public Cloud provider and the identifying the right workloads to move to the Cloud.

Organizations often falter in making an accurate estimate of Cloud migration. Given that there are multiple pricing strategies and different types of services, organizations make mistakes in making the right choices and experience escalating costs. There are also hidden costs in data migration, such as, the need to upgrade connectivity to accommodate higher bandwidth requirements.

Designing a Cloud architecture that aligns with the existing IT systems while meeting with the goals of migration is challenging, as it must take into account legacy systems and dependencies that were not designed for Cloud integration.

Cloud Migration Roadmap for Businesses

Below we share some practical tips and actionable insights, based on our expertise and experience, on how to better prepare the organization and thus, smoothen the journey to the Cloud.

• Take the plunge: While planning is crucial, getting started is the key. Too often, organizations get mired in execution details which bogs down the process and stalls the migration program. NewVision experts find that savvy adopters start with a small team of engineers opening an account with Microsoft Azure or on AWS Cloud, and trying out services from the console. This sets the pace within the organization as engineers become familiar with the environment to spin up instances, try out different regions and get an opportunity to understand the different tiers of services. Most public Cloud providers offer a free tier which is good to get started, and many organizations actually start production workloads from the free tier before upgrading to different instances and services.
• Executive Sponsorship for Cloud:Needless to say, strong executive sponsorship bolsters the program, providing the much-required momentum and fostering collaboration across stakeholders to galvanize the strategic move to the Cloud. Usually, the Cloud initiative is headed by the CIO or the CTO with the backing of the CEO and the CFO. Sometimes in large-scale migrations it is the business head such as the COO, and sometimes even the CEO leads from the front to facilitate the alignment of technical and business objectives; managing the short-term and long-term goals; and stakeholder expectations. Given that Cloud migration has many complexities, the executive office of the Cloud Center of Excellence comprises representatives from legal, procurement, risk management, CISO, heads of infrastructure and delivery along with Cloud evangelists.
• Set Migration Goals: Organizations must have clear goals and expectations which will help benchmark efforts and take corrective action to steer the course of migration successfully. The objectives could be business reasons such as cost savings, enhanced customer experiences, improved agility or it could be technical benefits such as improved scalability or enhanced security. At the same time, it is important to ensure that there is alignment of business and technical goals. For instance, the need for enhanced customer experience and high performance may clash with the objective of cost-efficiency, as meeting the former objective will require auto-scaling and fail-over strategies which will be more costly. Managing stakeholder expectations is a key requirement in setting migration goals.
• Reach out to Cloud Migration Experts: Set up a dedicated session with an experienced Cloud migration services provider to get clarity on many critical aspects which will affect the decision making. It is important to address all the doubts, so take a systematic approach in collecting the queries of different stakeholders and have a detailed discussion with an experienced provider. These sessions will provide clarity on the big picture, guidance in navigating licensing and pricing issues, security and compliances, architectural best practices, all of which will help you make informed decisions.
• Assessment of IT Estate: Conduct a comprehensive evaluation of your organization’s IT landscape. Start by creating a detailed inventory of all systems and applications. Document key details such as version numbers, usage patterns, interdependencies, and business criticality. Identify strategic applications by mapping their criticality to business operations and examining interdependencies, performance, and security implications. This thorough analysis will reveal which applications should be prioritized for migration. For instance, workloads experiencing variable peak loads or sudden traffic bursts are ideal candidates for the Cloud due to its inherent scalability. Additionally, applications nearing end-of-life can be modernized by moving them to the Cloud, reducing legacy maintenance burdens.
• Establish a Governance Framework: A Cloud governance framework is essential to ensure that migration goals are met. Apart from aligning security and compliance objectives, the governance framework must establish policies and guidelines for financial management, operations, data management, performance and asset management. Typically, the framework will define the outcomes in the desired areas without specifying the procedures, as technologies and approaches change rapidly. For instance, data governance policy can mandate encryption to store sensitive data without specifying which tool to use. The governance framework is also essential to sort out overlapping objectives, such as, security and data management; operations management and cost control.
• Communication Strategy: Put in place a strong communication strategy as Cloud migration will affect all stakeholders in the organization, and therefore a solid communication plan will help smoothen the process. First, identify all internal stakeholders and communicate about the need, objectives and migration roadmap. It is also important to keep external stakeholders such as migration partner and collaborators on the same page. Next, identify what needs to be communicated to each stakeholder. For example, CXOs will be interested in knowing about the legalities, possible business disruptions and ROIs while HR and operations will be keen to know more about change management, employee onboarding, business continuity in terms of access to resources, etc. At the same time, educating stakeholders about the nuances of the new model is an important communication objective. For instance, procurement must become adept with managing an operating expenditure, while legal will need to understand the implications of a borderless organization.
• Talent Management and Skill Mapping:Prepare the workforce with skillsets for migration using an integrated approach that includes skill evaluation, mapping of existing skill sets, upgrading with training, strategic hiring and leaning on an external partner for skills that are difficult to bridge. An experienced partner brings technical expertise and helps to make informed choices to meet technical and business goals.
• Establish a Cloud Center of Excellence: The Cloud Center of Excellence is a cross-functional team of experts. It is the hub of activity and will oversee practically everything from defining the Cloud strategy, selecting the right public Cloud platform, assessment of IT estate, defining the migration roadmap, managing the execution including developing a governance framework, and providing training and support.

Cloud Migration Consulting Services

As an experienced consulting and solutions provider, NewVision Software has enabled complex migrations, based on which we have developed institutional assets and knowledge repository to provide a strong operational foundation for successful migrations. This includes a migration methodology for executing legacy migrations in a methodical way with a robust set of tools to automate and accelerate common migration scenarios.

Customers have successfully migrated workloads to Microsoft Azure and are harvesting the inherent benefits of the platform, including agility, scalability and quick access to the advanced technology resources. These capabilities are empowering customers to experiment often, innovate at speed and take up competitive positions in the market. Working with an expert like NewVision to define a Cloud migration strategy has facilitated customers to prepare the organization well, and look beyond the nitty gritty of migration and focus on growth and differentiation.

Lake-Centric Approach: Fabric is built around OneLake—Microsoft’s Data lake solution—akin to how Microsoft’s 365 applications are automatically tied with OneDrive. Given that it is a single fabric that spans multiple Cloud, it eliminates data silos and empowers users with a unified data storage capability.

Designed for AI-experiences: Native integration with Azure’s Co-pilot at every level of the architecture empowers users to draw insights in conversational language without doing complex coding. This empowers employees with different personals to utilize GenAI capabilities to summarize reports and insights, build machine learning models, develop dataflows and pipelines, generate code and visualize results in custom conversational language experiences.

Empowering Data Personas: Aimed at facilitating data-based decision making, Fabric is integrated with Microsoft 365, providing a single copy of data to empower users—from business users, analysts, data scientist, machine learning specialist to developers—to draw insights.

Cost reduction: The unified data experience from resource provisioning, integration, and tool utilization using a single pool of compute for the entire data workload results in reduced cost and effort, as the single compute eliminates costs of idle resources provisioned for different resources.

Employing Microsoft Fabric to Benefit NewVision Customers 

As an early proponent of Microsoft Fabric, NewVision Software has exploited these capabilities to benefit customers in myriad ways. One example is how NewVision has boosted post-merger integration capabilities using Microsoft Fabric to integrate disparate data systems and drive operational efficiencies to unlock the synergies of mergers.

Specifically, NewVision worked with a leading US-based orthodontist support platform to pursue its growth strategy via acquisitions. With more than 2.5 million patient visits spread across 72 locations, success was crucially linked to quick integration of acquired practices.

NewVision designed a robust data platform to cater to the current and future requirements by implementing a data agnostic layer on top of a data warehouse hosted in Microsoft OneLake, using Microsoft Fabric data pipelines to ingest and transform data quickly. This has empowered users to leverage Power BI to draw insights and pursue its aggressive acquisition-led growth strategy.

If you want to know more about Microsoft Fabric or how we empowered the orthodontist platform’s with data integration

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