Advancements in AI-ML and its wide adoption across industries have led to improved productivity and innovation in the last five years. The impact of AI simply cannot be overstated.
Coupled with the introduction of large language models in recent years, AI-ML have transcended everyday business operations to countless examples of AI solving real-world problems.
HCLTech has already helped our global clients address global problems by implementing new-age technologies like AI-ML, data analytics, IoT, cloud and many more.
Having moved in the right direction so far with success across industries, HCLTech has recently unveiled an easy-to-use ML platform that is meant for technical and non-technical users. This platform – and the application of AI – is targeted toward easy access of AI and ML, saving costs and enhancing efficiency while dealing with day-to-day and real-world problems.
Key takeaways on AI solving real-world problems
- AI/ML, amplified by GenAI, has moved from PoCs to measurable impact across disaster response, healthcare, education, agriculture and public safety
- In climate resilience, AI improves early-warning accuracy and speeds response, from flood alerts to water-use optimization
- In healthcare, AI agents and modern data stacks cut admin load, strengthen cyber defenses and elevate patient/caregiver experience
- Education gains include secure remote assessment at scale and adaptive, always-on tutoring and feedback
- Climate-smart agriculture uses AI for sensing, forecasting and automation to protect yields and resources
- Scalable platforms like HCLTech AI Force democratize AI, lowering cost-to-serve and accelerating model deployment for both technical and business users.
AI in natural disasters: Predicting, preventing and responding faster
Today, natural disasters are much more severe than even a decade ago because of climatic change.
In fact, a UN report mentioned that climate and weather-related disasters grew five times in the last 50 years.
"Addressing the growing waves of floods, cyclones, droughts, rising temperatures and wildfires demands sharper prediction and prevention tools. AI-ML has been instrumental in these efforts; explore our climate-tech solutions overview for the latest strategies.
AI-powered cameras provide real-time footage (data) from disaster-prone areas, which can be processed further to derive insights.
Using such data, the Google Flood Hub provides free flood alerts up to seven days in advance in 80 countries.
Beyond flooding, the world is on the precipice of a freshwater disaster.
Through a $15 million investment over five years, HCL Group is helping address this and create a first-of-its-kind innovation ecosystem for the global freshwater sector, called the Aquapreneur Innovation Initiative.
It’s committed to accelerating multi-stakeholder collaboration and innovation in the global freshwater conservation space.
In addition, HCLTech’s AquaSphere initiative harnesses AI to guide sustainable water use, cutting waste and boosting conservation. Learn more about how AI water management is transforming stewardship from source to tap.
AI applications in healthcare: Enhancing patient care, security and operations
Challenges in this sector have no end and include capturing accurate data, maintaining data privacy and security, ensuring the compatibility of medical equipment, lack of trained professionals, fragmented patient care and compliance hurdles.
From fraud detection to data leaks, customer-relationship management to automated patient care and data safety, artificial intelligence in healthcare continues to expand its clinical and administrative reach. With predictive analytics, deep learning and neural networks, for example, healthcare organizations are detecting anomalies and shielding their operations from sophisticated cyberattacks.
Moreover, AI and generative AI, combined with data-modernization initiatives, are delivering the scalability today’s hybrid data architectures demand. Powered by IoT and cloud solutions, AI is today easing healthcare operations. Improving remote patient and employee care experiences are also a priority for healthcare organizations.
HCLTech has developed a generative AI agent that is revolutionizing patient care by answering complex clinical and administrative queries in seconds. The chatbot’s intervention helped save 40% time for healthcare workers.
AI applications in education: Securing exams and personalizing learning
The news of mass cheating during board exams in the Indian state of Bihar shocked people of the country last year. But the problem is not confined to India alone. A 2023 International Center for Academic Integrity study found 64% of high school students in the US admitted to cheating. The surge in remote learning has created fresh testing hurdles; our overview of tech-enabled assessment explains how AI closes those gaps.
Addressing this challenge, HCLTech developed an AI-assisted remote proctoring solution for one of its clients in the US. This solution increased efficiency of examinee-to-proctor ratio by five times, reduced cost per stream analytics by less than $0.5 and enabled to process 10,000 video streams simultaneously worldwide, among other benefits.
The COVID-19 pandemic allowed the online education system to experiment with AI and augmented technologies in a variety of ways.
AI now enriches the classroom through interactive visualizations, automated lesson creation, personalized learning paths, task automation and real-time, data-based feedback. By offering adaptable access, flagging classroom vulnerabilities and providing 24 × 7 conversational tutors, AI-powered learning is closing long-standing access gaps while supporting secure, decentralized assessment systems.
AI applications in agriculture: Driving climate-smart, data-driven farming
The UN’s annual report on climate change mentioned that up to 84% of drought-related economic impacts occur in the agriculture sector.
Australia is among the countries affected by flooding as well as drought. HCLTech delivered a cloud-based, AI-driven Intelligent Data Platform that enabled real-time insights and data-driven decision-making to an Australian government organization responsible for delivering retail water supply and wastewater services.
Elsewhere, AI has been instrumental in running self-driving tractors, combining harvesters, drones and robots for crop inspection and watering and spraying insecticides on the plants at the right time. Companies like Plenty and AppHarvest are using AI and data to adjust the indoor farming environment for optimal nutrition and flavor.
AI in smart-city policing and traffic optimization
From predictive analytics and AI-enabled cameras to drones, biometrics and facial recognition, next-gen tools now converge to tackle crime and optimize traffic. These innovations are forming the backbone of safe smart cities, where unified platforms blend real-time mobility analytics with proactive public-safety responses.
Mexico, for example, has deployed technology solutions to improve safety standards in various cities. In 2020, the Crime Index by Numbeo ranked Mexico as the 34th most dangerous country in the world.
This is when the government partnered with HCLTech to turn this around by adopting a ‘smart city’ model, supported by robust AI-enabled surveillance capabilities and an integrated IoT ecosystem. By mid-2023, the country’s position in the crime index improved by six places as the adoption of technology – and the impact of AI – helped the country become safer.
Ethical and Responsible AI: Building trust and compliance
Why it matters: Key risks to manage
- Bias and fairness: Historical skews in data or proxy features can produce disparate outcomes; require detection, mitigation and ongoing monitoring
- Explainability: Stakeholders need model rationale (global and local) to meet audit, safety and end-user expectations
- Data provenance and lineage: Clear traceability from source to prediction underpins consent, usage rights, reproducibility and incident response.
2024–25 regulatory milestones
- EU AI Act: Risk-tiered obligations, such as data governance, transparency and human oversight, with phased applicability from 2024–25
- US Executive Order on Safe, Secure, and Trustworthy AI: Directives on safety testing, watermarking, privacy-preserving tech and critical-use safeguards
- India—Digital Personal Data Protection (DPDP) Act & draft rules: Consent, purpose limitation, breach duties and Data Fiduciary accountability shaping AI data use
HCLTech governance: Responsible AI in practice
- Responsible AI Council (RAC): Cross-functional body that defines policy, approves high-risk use cases and oversees red-team/testing outcomes
Scaling enterprise AI-ML: From pilot to ROI
The reality check
- Production gap: Analyst research indicates many AI efforts stall, with Gartner reporting that 30% of GenAI projects will be abandoned for 2025
Typical blockers
- Fragmented data silos and unclear ownership
- Limited MLOps skill sets and brittle pipelines
- Uncertain unit economics, such as cloud costs and inference spend
- Governance friction, including risk, security and legal, slowing approvals
A pragmatic framework for success
- Data fabric with governed, discoverable domains and quality SLAs
- Central feature store for reuse, drift checks and lineage
- Cross-functional AI/ML CoE (product, data, risk, platform) to prioritize use-cases, templates and SRE-style ops
- Standardized MLOps (CI/CD for models), blue/green deployments, shadow testing and continuous monitoring
- Clear decision rights and a lightweight intake/approval path for regulated and safety-critical use-cases
Measuring ROI that matters
- Time-to-insight: from data arrival to action
- Cost-per-prediction and cost-per-use-case
- Throughput and reliability, including SLOs such as latency and availability
- Business KPIs, such as fraud caught, SLA adherence, waste reduced and revenue lift
- Model vitality, including reuse rate of features/components, retrain frequency and without regressions
The road ahead: Democratizing AI with ML platforms
Besides the specific use of AI to solve real-world problems across industries, moving forward, organizations need greater access to easy-to-use AI-ML platforms and services to solve the world’s most pressing challenges while improving business operations.
Here, a platform like HCLTech’s AI solutions and data engineering and AI services can be helpful as it simplifies the process of scaling AI-ML and empowers people to create deployable and easy-to-use ML models. The application of AI – and, frankly, the impact of AI - enable expert data scientists and citizen data scientists (working in code) as well as business users (low code/no code) to avail these models.
