Adaptive Manufacturing Powering Factories of the Future

Industry 4.0 technologies like robotics, automation, digital twins, IoT, and AI are reshaping manufacturing operations, but 38% of enterprises are dealing with talent gaps and legacy system integration
Business process services help increase adaptability and address challenges like system silos, IT/OT misalignment, security risks, etc.
Adaptive manufacturing enables factories to respond quickly to supply chain disruptions, fluctuating customer demand, and dynamic market conditions, ensuring resilience and agility

Bridging the Gap to Smart Manufacturing

The manufacturing industry has undergone a profound shift, led by a global transformation driven by 4.0 technologies. The current manufacturing trends are dominated by robotics, automation, digital twins, IoT, and other ground-breaking tech. Additionally, enterprises are increasingly investing in building smart factories shaped by the convergence of automation, AI, and IoT.

However, 38% of enterprises moving towards Industry 4.0 are discovering talent and skills gaps, while a similar percentage experience roadblocks when integrating new technology with legacy systems ¹.

The journey of modern and adaptive manufacturing is incremental and requires a strong alignment of technology, skills, and expertise. To put it simply, manufacturers need all the help and support to build the factories of the future.

Business Process Services in Modern Manufacturing

Manufacturing technologies such as automation, robotics, AI, and big data must be integrated into workflows, operations, supply chains, and customer-facing processes. The role of business process services is to create a cushion between these technologies and physical, day-to-day operations. This serves as a center of excellence, embodying deep expertise and advanced technology to build connected systems in which machines, data, and humans can communicate and operate seamlessly in real time.

Domain experts bridge smart factory gaps during the transformation that manufacturers are unable to fill alone:

Skilled workforces equipped with IoT integration skills, data literacy, and AI/ML proficiency
Big data analytics to address the problem of under-utilized data across supply chain touchpoints
Governance and change management frameworks to increase agility and scale technology adoption
Sustainability and compliance management mechanics
Unified reporting and analytics sans disruptions usually caused by fragmented, legacy systems

The manufacturing space is ever evolving; organizations that understand the human side of Industry 4.0 transformation will be able to respond to market shifts and disruptions.

Scaling Adaptive Manufacturing Beyond Technology

These capabilities enable adaptive factories. But adaptive manufacturing goes beyond deploying advanced technologies. It helps build smart factories that can handle supply chain disruptions, shifting customer demands, and market fluctuations with real-time agility. However, AI and automation alone are not enough. Many enterprises face significant IT/OT misalignment and interoperability issues, like fragmented data, system silos, security risks, and technical debt.

To address this, organizations need a more structured approach. By combining business process services with business process management, enterprises can scale Industry 4.0 initiatives in a controlled, incremental manner.

Tech Mahindra’s Factories of Future

We, at Tech Mahindra, transform manufacturing through our Factory of the Future solutions. They focus on predictive maintenance, sustainable operations, and supply chain agility. Built on AI, IoT, cloud, and cyber-physical systems, they help enterprises to:

Maximize asset utilization and optimize the effectiveness of equipment
Help manufacturers build a zero-harm environment through AI-powered predictive intelligence and risk mitigation
Leverage digital twin, simulation design, and product innovation data to enhance design and engineering
Enable smart production through anomaly detection, corrective intelligence, and quality checks
Upgrade the client factory network infrastructure to handle data explosions caused by digital disruptions
Empower businesses to transition from reactive management to proactive, data-driven operations
Align technology with human capital and enable smooth change management

Building Resilient and Future-ready Manufacturing

At Tech Mahindra, smart manufacturing entails tightly linking technology adoption, functional drivers, and change enablement. We have collaborated with new-age technology partners and leveraged our vast talent pools to build highly customized solutions to support rapid IT/OT convergence in customer organizations. From building resilient supply chains to ensuring reliable aftermarket care, Tech Mahindra plays a transformative role in helping manufacturers stay agile and adaptive.

Learn more about Tech Mahindra’s supply chain management solutions - Supply Chain Management | Tech Mahindra

Frequently Asked Questions

Our FAQ section is designed to guide you through the most common topics and concerns.

BPS enables seamless integration of Industry 4.0 technologies through customized frameworks to support smart manufacturing. From selecting the right technologies and embedding capabilities in process workflows to managing compliance and training skilled workforces, BPS enables digital transformation and change management end-to-end.

Adaptive manufacturing trends are driven by agility, resilience, and sustainability, and shaped by Industry 4.0 technologies, including automation, AI, digital twins, robotics, and connected systems. Adaptive manufacturing is all about building smart factories that result from merging technology with human skills and collaboration.

Manufacturers encounter persistent challenges when adopting Industry 4.0 technologies for adaptive manufacturing, such as talent and skills shortages, integration of advanced technologies with legacy systems, IT/OT misalignment, and fragmented data ecosystems. These hurdles can limit scalability, create security risks, and slow transformation unless addressed through structured governance, process alignment, and change management.

References

Sharma, N. (2025, August 12). 1 in 3 Indian enterprises held back by legacy and talent gaps in AI. Tech Disrupter Media.

About the Author

Mahima Agarwal

SVP, Region Head – Sales, BPS America, Tech Mahindra

Mahima is a Global Sales Leader & Americas Head for Tech Mahindra BPS Americas strategic verticals. She has spearheaded numerous complex digital transformations and secured multimillion-dollar deals across various industries. She is a pivotal, versatile leader adept at blending futuristic vision with deep technical roots to deliver an outsize impact for clients and propel business strategies.

Author(s)

Mahima Agarwal

SVP, Region Head – Sales, BPS America, Tech Mahindra

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The Growing Demand for Data Annotation in the AI EraThe global data annotation services sector is entering a period of exceptional expansion, growing from a valuation of approximately USD 1.30 billion in 2024 to an estimated USD 14.40 billion by 2034, supported by a powerful ~27% annual growth rate between 2025 and 2034.1 This surge is largely driven by AI adoption in sectors such as healthcare and automotive, where large volumes of quality data are required to train models. With organizations expanding their use of GenAI, the demand for reliable data annotation continues to increase.Quality data is fundamental to training and improving AI models. A model performs well only when it is trained on the right datasets. This process requires large volumes of varied data to enable ML systems to learn different patterns and operate effectively across scenarios. With effective training, models produce more reliable outputs and better user outcomes, making the quality of data annotation a critical factor in overall model performance. However, manually annotating data is complex and time-consuming. Automation, on the other hand, helps accelerate model development and allows teams to handle large-scale annotation projects more efficiently.Data processing to strategic enablement, effective annotation is the foundation for every reliable AI model.From Automation to Intelligence: How GenAI is Redefining Value CreationToday, GenAI is increasingly used in annotation workflows as model and technical capabilities improve. Annotation teams now combine manual review with GenAI-assisted labeling to maintain accuracy while keeping projects on schedule .Automation in data annotation: Manual annotation -> Assisted manual annotation -> Rule-based annotation -> Machine learning models and active learning -> GenAI based annotation -> GenAI automated annotation.Tech Mahindra’s annotation programs have successfully used assisted manual annotation, rule-based automation, and machine learning models. Current efforts are expanding toward active learning, GenAI-assisted annotation, and autonomous annotation workflows. While GenAI improves speed and scale, it still faces limitations. Here’s a quick rundown of the key challenges and the areas where it delivers the most value.Data TypeGenAI ChallengesWhere GenAI ExcelsTextAmbiguity, sarcasm, domain jargon, long contextEntity extraction, classification, PII redactionImageHallucination or missing attributes, occlusionsObject detection, tagging, basic captioningVideoScene complexity, fast objects, long contextSegmentation, activity classification, keyframesAudioNoise, dialects, diarization driftASR transcripts, keyword spotting, basic intentAutomation Meets Reality: Key Learnings from a Real Annotation ImplementationAnnotation automation demands successful coordination between people, processes, and technology. The following use case illustrates key challenges in real-world projects and TechM’s structured approach in addressing them.Objective: To identify participants and objects within a given video scene and describe participant activities and object properties like size, shape, color, and build for AI model training. The project scope included data collection and data annotation.TechM's Solution:Data Collection Manual data collection required participants to act out scripted activities that were recorded on video. These activities ranged from relaxing in the living room and cooking in the kitchen to cleaning the house and performing tasks within a defined area.Data AnnotationUsing our data management platform, we built a GenAI model to support automation in the annotation workflow. The model first analyzed the data to identify participants and objects within the scenes, after which an expert human annotation team reviewed the output and corrected any inaccuracies before finalizing the model.Limitations: The complexity of the scenes, limitations in prompting techniques, object misidentification, and inconsistent adherence to process guidelines led to GenAI hallucinations, resulting in false attributes and incorrect descriptions. In addition, human reviewers had to meet strict quality thresholds while identifying subtle model-generated inaccuracies, which increased the operational burden.TechM’s Fix: After identifying gaps, we solved the hallucination and false attribute challenge by streamlining the process.Incorporated client feedback into the process:LLM did not follow the correct spatial order of objects. We introduced additional training and manual annotation to address this.LLM generated unnecessary object attributes; we implemented prompt refinement to resolve this.Missing object attributes were identified using automated scripts that validated JSON output for missing information.Introduced changes to the existing processDeployed hybrid working model: AI + Human evaluationsEnhanced the prompts by providing sufficient data for learningRefined the scripts accordingly to validate missing informationThese refinements improved model performance and reduced reviewer workload, supporting a sustainable workflow.Prompt refinement, validation scripts, and human-AI collaboration turned early challenges into an annotation workflow built to scale.LearningsWith this experience, we learned to always start with human-led manual annotations for complex projects and later introduce automation to check on the efficacy of the model. Also, we recognized the importance of expanding automation only while maintaining annotation quality and delivery timelines.Designing for Impact: Principles Every GenAI Automation Strategy Must FollowFor GenAI automation to deliver real value, organizations must:Have a clear understanding of the use case.Have a sufficient amount of data trained for AI to learn and generate appropriate outputs.Continuously tune prompts to enhance the quality of the outputs.Apply a complexity-based approach, which is ideal for simple, medium, and complex use cases.Start with a pilot to gather the required information and identify potential challenges of the process.The Bottom LineAnnotation workflows are moving toward hybrid models in which automation accelerates the process while human expertise ensures accuracy. This approach helps enterprises build reliable and scalable AI models. With GenAI now in the picture, organizations with strong process control and oversight can augment the annotation process and build better AI models efficiently.

Automation in Pension SystemsFinancial enterprises across ASEAN markets are actively rethinking service delivery and operations at scale. An increase in customers’ digital-first expectations has led to stringent regulatory requirements in the financial services ecosystems—pension fund administrations included. Currently, with a shrinking workforce and the growing need for technological advancements, several financial services across ASEAN are looking to move towards a hybrid BPS-driven operating model to alleviate the heightened strain on pension systems.In the ASEAN finance market, outsourcing and process automation are redefining how institutions operate. Transparent reporting, operational resilience, and responsive servicing are becoming essential capabilities. At the same time, organizations are responding to the global retirement shift1 with greater agility and foresight.This blog explores how traditional legacy systems and fragmented processes are being replaced by modern, integrated business process models.Organizations are quickly responding to the global retirement shift with foresight and better agility.Redesigning Pension Calculation and Customer ServiceTraditional pension calculator engines had limitations with siloed tools and spreadsheets. But with automation, institutions are gradually changing the way retirement and pension administrations1 are handled. Below are the ways how automation has changed the financial sector, particularly retirement and pension administrations:On-demand Ramp-upIn an organization, setting up pension and retirement infrastructure not only needs extra cost but also careful planning. As business needs evolve, building and maintaining pension administration infrastructure can be complex—from setting up operational capacity to onboarding skilled pension administrators and managing benefits processing.On the other hand, outsourcing pension management is cost-effective, as businesses incur costs only as per project or contract. This model allows businesses to scale operations up or down as needed, improving flexibility and cost efficiency.Expert-led, Outcome-driven More than 80% of global organisations are increasing their outsourcing efforts, driven by cost pressures, the adoption of advanced technologies, and the need for specialised expertise.2Financial institutions that outsource employee account transfers, pension calculations, distribution processing, and regulatory compliance to third-party experts do notice significant improvements in process efficiency and customer experiences. For example, the use of automated APIs and pipeline orchestration eliminates reconciliation errors. Likewise, anomalous entries and variance thresholds are immediately flagged using automated controls, which again enables operational excellence.At the core of this shift are expert business service providers who take over the complete process end-to-end, enabling comprehensive data management and thorough accounting services.Alignment with Regulatory ChangesAcross ASEAN, particularly in highly regulated markets such as Singapore and Malaysia, pension regulations continue to evolve. Keeping retirement plans consistently compliant can therefore become a significant operational challenge for financial institutions.Therefore, outsourcing pension regulation to a trusted BPS provider ensures commendable regulatory compliance. It is possible when compliance precision is embedded directly into the pension workflows, enabling actuarial assumptions and contribution validations. With a precise governance model, the goal is simple—the shifting of compliance metrics from reactive to proactive.Transformative Impacts of Automation on Customer ServicingWhether enterprises set up their own pension infrastructure or outsource services, the goal remains the same—earning and maintaining customer trust. AI- and automation-powered service strategies have brought new levels of sophistication to the ASEAN finance landscape. Some of them are mentioned below:Self-Servicing: Shifting to automated business logic helps customers in real-time to instantly access benefits estimates, view contribution history, simulate pension scenarios, and request policy explanations.Omnichannel Engagement: Conversational AI deployed across messaging platforms, emails, voice bots, web chat, and mobile apps reduces average handling time (AHT), increases first contact resolution, and ensures consistent service quality.Workflow Automation: An enterprise that integrates AI- and automation-powered pension lifecycle management improves member interaction and customer satisfaction. For example, a pension withdrawal request automatically triggers eligibility checks, tax computation, and authorization validation. This also reduces escalations and conflicts.Predictive Pension Management: Automation enables predictive engagement through real-time analysis of member behavior, demographic data, and contribution trends. As result, it shows early detection of at-risk members, policy deadline alerts, portfolio recommendations, and so on.Excellent customer servicing is more than efficiency; it is also about offering a responsive, consistent, and reliable experience at scale.Ensuring Success in the ASEAN LandscapeProcess outsourcing and automation can significantly improve operational efficiency and service delivery when organizations focus on the following:Choose Expert Service Partners: Opt for business process service providers with deep expertise in pension management and knowledge of ASEAN markets.Adopt an AI-First Mindset: Consider providers with AI-powered solutions to connect pension calculation engines to customer engagement platforms and regulatory workflows.Ensure Cultural Readiness: Invest in change management by building organizational trust in automated outcomes.Secure Data and Ethical AI Principles: Select organizations that embody strong data ethics and AI principles, focusing on decisions such as pension management affecting people’s livelihoods.ConclusionToday, pension management is no longer considered a back-office function. Automation and regulatory precision are the driving forces behind the ASEAN pension ecosystems. Outsourcing pension administration is increasingly critical, enabling stronger member engagement and greater operational agility. It also ensures end-to-end pension lifecycle management through expert pension administration, compliant pension calculations, readymade audit templates, and robust audit-ready decision paths.Across global financial markets, outsourcing is no longer just a tactical efficiency move. It is increasingly seen as a strategic approach to improving accuracy, continuity, and scalability in customer lifecycle management. Consequently, organizations that invest in advanced automation and AI-powered processes through experienced providers can unlock value faster, build customer trust, and gain a competitive advantage.

As AI becomes the primary interface between institutions and people, one question is becoming impossible to ignore: who does AI truly work for?At 56th Annual Meeting of the World Economic Forum (WEF), MINDS, their flagship program to recognize organizations that are spearheading meaningful AI-driven transformations with tangible results, accolated Tech Mahindra for advancing linguistic and digital equity through AI. Project Indus, our initiative to build a foundational large language model rooted in Indic languages, was acknowledged for reshaping AI adoption in economies that speak regional dialects, which global systems often cannot address accurately. This highlights an important shift in how AI adoption is evaluated globally, not just for its technical sophistication, but also by its ability to serve diverse populations responsibly.India’s Own Foundational ModelWhen we began this journey, the accessibility gap was evident. Most large language models were trained predominantly in English and a handful of high-resource languages. Yet in India, only a fraction of the population interacts comfortably in English. Hundreds of millions communicate in Hindi and other regional languages, many of which have limited digital representation.Project Indus was conceived to address this imbalance. It aims to preserve and digitize underrepresented languages while creating scalable, industry-agnostic language models that can power real-world applications across banking, healthcare, education, and citizen services. This is a civilizational initiative that has developed India's own foundational large language model (LLM) focused on Indic languages. One of Project Indus's key objectives is to preserve Indic languages and dialects that lack significant digital records. It also supports the creation of localized and verticalized, industry-agnostic Indic LLMs, enabling us to forge partnerships with telecoms, hyperscalers, and OEMs to build a platform offering E2E LLM solutions.Bringing Cultural Equity with Regional LLMsToday, many digital public services across healthcare, banking, and government are delivered through AI-powered chatbots and IVRs. However, most of these systems rely on language models trained primarily in English and a small set of Western languages. For example, India has 27 official languages and more than 1,600 dialects, with only 10-20% of the population speaking English. Language models that are not trained in these regional dialects and cultural nuances fail to recognize intent and cultural context and translate inaccurately.With our sovereign AI model, we are trying to bridge this communication gap by enabling users to engage with conversational platforms trained in regional dialects and cultural sensitivities. Capable of supporting both speech and text, this model can adapt to any conversational interface. A defining aspect of it has been its training on datasets obtained from open national databases, literature, archives, and user contributions. Such a unique LLM allows enterprises to retain complete control over data and model behavior, ensuring privacy and ethical AI governance.Built from the Ground Up and Enhancing Accuracy for Regional ContextIn my view, digital inclusivity cannot be achieved by retrofitting global models with translation layers alone. Linguistic diversity must be treated as a foundational design principle.Most popular platforms are trained predominantly in English and other major Western languages. These interfaces perform extremely well in English but lose contextual and translational accuracy when used with diverse languages and dialects. However, our LLM has been trained with linguistic diversity at its core and further refined by native speakers, enabling it to achieve 92% accuracy in Hindi. In comparison, the global benchmark was only 70%.Our Indic LLM today handles 3.8 million queries per month for banking, healthcare, and government organizations. It is a classic case study that shows how AI can be trained and scaled, with people at the heart of its framework. Such an innovative approach has made AI more adaptive, accessible, and preferable, bringing it closer to people.ConclusionDigital inclusion will define the next chapter of AI adoption.As AI systems become embedded in public life, we must ask ourselves whether they reflect the linguistic and cultural diversity of the people they serve. Building sovereign, culturally grounded foundational models is not simply a technological ambition—it is a societal responsibility.At Tech Mahindra, we see ethical and inclusive AI not as a positioning statement, but as a design imperative. The future of AI will not be determined solely by how advanced it is, but by how equitably it is built and deployed.And that, I believe, is the true measure of progress.

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From Automation to Adaptability: How Smart Manufacturing is Powering Factories of the Future

Bridging the Gap to Smart Manufacturing

Business Process Services in Modern Manufacturing

Scaling Adaptive Manufacturing Beyond Technology

Tech Mahindra’s Factories of Future

Building Resilient and Future-ready Manufacturing

Frequently Asked Questions

References

Author(s)

Mahima Agarwal

Our Promise

Featured Report

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From Automation to Adaptability: How Smart Manufacturing is Powering Factories of the Future

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Bridging the Gap to Smart Manufacturing

Business Process Services in Modern Manufacturing

Scaling Adaptive Manufacturing Beyond Technology

Tech Mahindra’s Factories of Future

Building Resilient and Future-ready Manufacturing

Frequently Asked Questions

References

Author(s)

Mahima Agarwal

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Automated Annotations Reimagined: Market, GenAI, and Learnings

Rethinking Pension Administration: Accuracy, Compliance, and Trust at Scale

Achieving Linguistic and Digital Equity Through AI