Elevating User Experience with Agentic AI 

“With agentic AI reaching a new level of maturity, we’re closer than ever to solving some of the most persistent customer pain points in enterprise environments.”

                                                                        -Liz Centoni, Cisco’s Chief Customer Experience Officer

In an era defined by rapid innovation and AI-driven disruption, delivering an exceptional user experience has become the ultimate differentiator for enterprises. Products and services are no longer enough; what sets businesses apart is how seamlessly, intelligently, and securely they engage with their customers.

From frictionless digital banking to predictive retail experiences, AI is at the centre of them all, and industries are transforming at an unprecedented speed. Yet, despite heavy investments in digital transformation, enterprises struggle with customer satisfaction, leading to higher churn, missed opportunities, and declining customer loyalty.

Why Are Enterprises Still Struggling with User Experience?

Enterprises often struggle to keep pace with the rapidly evolving expectations of today’s customers. What worked yesterday no longer guarantees satisfaction or loyalty today. Recognizing this shift, many organizations are placing AI at the core of their operations to reimagine the user experience.

• Uber Eats has strengthened its search and recommendation system using AI to better match user preferences—factoring in past orders, dietary needs, and cuisine types. This not only helps users make more confident choices but also enables real-time issue resolution, driving higher satisfaction.
• Netflix leverages AI to go beyond content recommendations, personalizing thumbnails and artwork based on what resonates most with each user. This keeps audiences engaged longer while making content discovery effortless.

Despite these successes, delivering a stellar customer service has become challenging than ever due to the following reasons:

• Data Privacy & Security: Customers not only expect deep personalization at every touchpoint but also demand strict protection of their personal data. Striking the right balance between AI-driven customization and robust data security remains a persistent challenge for tech leaders.

• Consistency Across Channels: Modern customers expect seamless, uniform experiences across multiple channels. Delivering uninterrupted multi-channel value without friction is increasingly complex and challenging to manage.
• Balanced Human-Machine Interaction: While AI excels at structuring daily tasks, automating workflows, detecting errors, and reducing delays, human intervention remains indispensable for solving complex problems that require empathy, creativity, and nuanced judgment.
• Legacy System Integration: Many enterprises are burdened with outdated legacy systems. Transitioning to modern architectures while preserving information and avoiding operational disruptions is costly and technically demanding.

Why Traditional AI is Failing in Delivering User Experience

Fueled by data and machine learning, traditional AI has delivered transformational outcomes for enterprises. However, it is no longer enough with fast-evolving technology and shifting market dynamics. Its limitations include:

• Static Models – Traditional AI struggles to adapt to real-world changes without frequent retraining once trained and deployed.
• Rule-Bound & Narrow – It performs well in predefined tasks but fails when context shifts or when faced with unstructured, messy data.
• Reactive, Not Proactive – It can only respond to inputs, lacking the ability to anticipate, reason, or take the next best action autonomously.
• Data Hungry – It demands massive, labeled datasets and ongoing human intervention, driving up cost and complexity.
• Limited Autonomy – It cannot operate independently across workflows or make evolving decisions aligned with business goals.

The result? System fragmentation, workflow errors, and process delays—ultimately leading to a poor customer experience.

This is where next-gen Agentic AI steps in—designed to adapt, reason, and act autonomously, transforming how enterprises operate and scale.

Enter Agentic AI: The Next Frontier in User Experience

Agentic AI is not just another buzzword; it is the next wave of revolution, and leaders have witnessed unprecedented transformation unfold across different industries. Agentic AI is far more evolved as the agents can think, sense, decide, and act autonomously while integrating seamlessly into existing enterprise ecosystems, predicting intent, adapting to complex workflows, and executing actions—while keeping humans at the centre of decision-making.

Enterprises are innovating AI-driven platforms that unlock customer experiences like never before. TechSee’s Visual AI platform, which provides customers with personalized guidance, improves customer satisfaction by 30%. Startek uses goal-oriented Agentic AI for proactive customer support, reducing churn by 25%, & Creole Studios leverages adaptive AI in healthcare and finance, driving a 40% boost in customer loyalty.

With Agentic AI, enterprises can finally move from reactive problem-solving to proactive, personalized, and secure experiences at scale.

Key Features of Agentic AI

• Autonomous & Goal-Driven: Operates with minimal human intervention, going beyond routine tasks to dynamically optimize processes for outcomes like revenue growth, operational efficiency, and customer satisfaction.
• Adaptive & Continuously Learning: Learns from every interaction, outcome, and data stream to evolve in real time—quickly adapting to market shifts, customer behavior, and changing organizational needs.
• Proactive & Context-Aware: Understands intent, environment, and constraints to deliver precise, relevant, and timely actions. Anticipating challenges and opportunities before they surface reduces delays and prevents errors.
• Collaborative Across Workflows: Seamlessly integrates across applications, platforms, and ecosystems to coordinate multiple agents and teams—enabling true end-to-end automation and eliminating system fragmentation.
• Secure, Ethical & Human-Aligned: Safeguards sensitive information with advanced methods like differential privacy and encryption, while ensuring transparency, explainability, and human oversight—so enterprises can trust outcomes to be ethical, compliant, and business-aligned.

Industry Use Cases

Enterprises across industries are transforming operations and unlocking new possibilities with Agentic AI. Here are a few use cases highlighting how Agentic AI drives real impact.

1. Retail

Agentic AI transforms shopping from transactional to experiential. It provides real-time, hyper-personalized recommendations based on mood, behavior, and preferences and analyzes past shopping patterns to answer detailed queries and guide purchase decisions.

• Impact: A report by McKinsey suggests that using agentic AI in retail can lead to a 10-15% increase in sales, a 5-10% reduction in costs, and a significant improvement in customer satisfaction. 
• Example: Sephora uses AI-powered sensors and cameras to track customer behavior, providing insights on foot traffic, dwell time, and product engagement. 

2. Banking & Financial Services

Agentic AI empowers customers with financial guidance tailored to their business needs while ensuring security through built-in encryption and anonymization. It simplifies complex processes like account opening and loan approvals, cutting processing time by 30%.

• Impact: A McKinsey study found that agentic AI can help banks and financial institutions detect fraud more effectively, with a 25% reduction in false positives and a 30% reduction in false negatives.
• Example: Citi Bank is leveraging agentic AI, reducing its processing time by 30% and increasing customer satisfaction by 25%.

3. Healthcare

Agentic AI strengthens patient trust by using data minimization and encryption to protect patient privacy while improving care outcomes. It helps hospitals flag anomalies, schedule appointments, and guide treatment pathways.

• Impact: According to McKinsey, usingAI in healthcare can significantly improve patient outcomes by 30% and reduce costs by 20%.
• Example: Google Health’s Agentic AI system uses advanced encryption and anonymization techniques to protect patient data while delivering personalized care.

Leaders worldwide are harnessing Agentic AI to transform their operations, migrate from legacy environments, elevate customer experiences, and redefine the way employees work without compromising data privacy and security.

Customer Success in Action

Case Study 1

Avid Solutions, a US-based research and development firm struggled with inefficient onboarding, unstructured data, and frequent errors from manual interventions, leading to broken processes, repetitive workflows, high operational costs, and a poor customer experience.

With Agentic AI:

• Errors in project management fell by 10%
• Onboarding time was cut by 25%
• Response times improved by 30%

The impact extended beyond individual customers—driving operational efficiency, customer trust, and long-term loyalty.

Case Study 2

A global bank struggled with customer support inefficiencies and thousands of daily call queries, increasing call centre wait times beyond 15 minutes, fragmented data across systems, and old legacy infrastructures. Static chatbots were only helpful with predefined scenarios and failed when the context shifted. This led to frustrated customers, high churn risk, and mounting operational costs.

With Agentic AI:

• 70% of queries resolved autonomously, reducing human workload.
• Response times cut from 20 minutes to under 1 minute.
• Customer satisfaction (NPS) jumped 35%, while operational costs dropped significantly.

This technology pivot unlocked new revenue opportunities, streamlined workflows that boosted employee productivity, and elevated customer experience.

Challenges of Agentic AI

Despite the transformational results of Agentic AI, enterprises continue to struggle. As autonomous agents gain decision-making power, the agents become far more complex to govern, harder to predict, and carry higher stakes for reliability, security, and ethical alignment.

Data Privacy & Security

With AI’s rapid advancement, data privacy and security remain critical concerns. Agentic AI intensifies these risks, as autonomous agents continuously monitor user activity, operate anonymously, and can even infer or reconstruct personal and proprietary information without explicit identifiers—potentially exposing sensitive data, eroding trust, and triggering serious compliance violations.

Here’s How Agentic AI is Overcoming It:

• Zero-Trust Architecture Integration → Agents operate on the principle of continuous verification and not implicit trust, which forfeits data leaks to users
• Federated & Encrypted Learning → Sensitive data stays local; only anonymized insights are shared across models.
• Policy-Aware Agents → Embedded compliance logic aligns with frameworks like GDPR, HIPAA, SOC 2, and CCPA.
• Differential Privacy & Homomorphic Encryption → Advanced mathematical algorithms guarantee that personal identifiers can’t be reverse engineered and stay hidden.
• Full Accountability → Every agent interaction is logged, time-stamped, and explainable, enabling forensics and governance.

In conclusion,Agentic AI isn’t just automating workflows—it’s redefining data stewardship with privacy-first computation at its core.

Decision-Making Bias in Agentic AI

Like traditional AI, Agentic AI can inherit or amplify biases from training data, leading to skewed, unfair, or non-inclusive decisions. However, since Agentic AI operates with higher autonomy, the impact of such bias can be even more critical.

Here’s How Agentic AI is Overcoming It:

• Bias-Aware Training → Uses diverse, representative datasets and applies fairness metrics during training to minimize hidden skews.
• Explainable AI → Every decision is transparent and traceable, allowing teams to identify and correct biased reasoning.
• Continuous Feedback Loops → Learns dynamically from real-world outcomes and user feedback, adjusting models to reduce bias over time.
• Human-in-the-Loop Governance → Keeps humans in control of sensitive or high-stakes decisions, ensuring oversight where fairness and ethics are critical.
• Ethical Guardrails & Policy Alignment → Embeds enterprise-defined fairness rules, compliance frameworks, and ethical standards directly into the decision-making logic.

In short, Agentic AI safeguards enterprise decisions with built-in bias monitoring, correction, and governance.

Over Personalization in Agentic AI

Agentic AI can sometimes push personalization too far—delivering hyper-specific recommendations or actions that feel invasive, limit discovery, or create “filter bubbles.” This risks user trust, reduces choice, and may conflict with enterprise goals of innovation and inclusivity.

Here’s How Agentic AI is Overcoming It:

• Personalization with Boundaries → Calibrates personalization levels using enterprise-defined thresholds so recommendations are helpful but not intrusive.
• Diversity & Serendipity Engines → Introduces controlled randomness and diverse options to avoid filter bubbles and encourage exploration.
• Contextual Awareness → Balances personalization with situational context (business priorities, compliance needs, cultural nuances) to avoid narrow outcomes.
• User Control & Transparency → Provides explainable reasoning and lets users adjust personalization settings, keeping them in the driver’s seat.
• Feedback-Driven Calibration → Continuously refines personalization based on user satisfaction, engagement patterns, and enterprise KPIs.

In short, Agentic AI solves over-personalization by setting boundaries, injecting diversity, and giving users control—ensuring personalization feels empowering, not limiting.

Indium’s Take on Agentic AI

At Indium, we understand this continuous market shift and have proactively engaged with business leaders to deliver a superior user experience with AI at the core of every customer engagement.

By harnessing Agentic AI, Indium is reinventing banking solutions, saving customers thousands in overdraft fees while enabling better financial health and inclusion. Agentic AI can proactively manage customer accounts to prevent overdrafts, automate trading to intelligent cash flow management and personalize customer interactions.

At the forefront of this innovation is LIFTR.ai—an Agentic AI-powered legacy system modernization platform designed by Indium’s brilliant minds. LIFTR.ai autonomously analyzes complex legacy environments, identifies refactoring issues, eliminates tech debt, and delivers actionable transformation. The result? Faster, more reliable, and seamless applications that elevate user experience and build customer loyalty.

What sets LIFTR.ai apart is its architecture of specialized AI agents working collaboratively. These agents provide real-time insights, enabling enterprises to deliver personalized experiences at scale while accelerating digital transformation.

The Road Ahead

Agentic AI is not just a technological shift—it’s a revolution in customer experience, trust, and personalization. Enterprises that embrace it will lead the future of customer engagement. With LIFTR.ai, that future is not just on the horizon—it’s already here.