AI should be positioned as a co-pilot: Priya Subramani, Freshworks

Customer service is experiencing a watershed moment with AI, as businesses embrace intelligent solutions to enhance efficiency, enable hyper-personalisation, and redefine customer expectations. AI agents can be deployed to handle routine inquiries and provide quicker resolution while real-time analytics and automation streamline workflows, reduce response times, and ensure seamless customer interactions across channels. It’s leading to the democratisation of customer experience capabilities for smaller businesses by offering cloud-based, plug-and-play solutions that reduce infrastructure costs. 

However, it’s not all as seamless as one would think. 

“SMBs often struggle with the initial investment and ongoing maintenance in areas such as hardware, software, and cloud costs. Integration complexity — especially for custom-built or legacy solutions — along with the need for AI-skilled personnel, data compliance, and privacy issues, presents additional challenges,” shares Priya Subramani, VP & GM of Customer Experience Products at Freshworks.

To get started, SMBs should focus on scalable AI tools such as chatbots, low-code/no-code solutions, sentiment detection and prioritisation, ticket categorisation, and other support operations automation. Subramani further advises the measurement of impact and gradual expansion to other areas with high ROI potential. SMBs can start with pre-trained AI models. Ensuring data security and privacy from the beginning is critical for establishing a strong foundation. Transparency, explainability, and auditability across initiatives are some of the most important factors that can help build confidence and facilitate progress. 

Subramani feels that having the basics in place can help businesses across categories leverage AI to elevate customer service experiences significantly. Mid-size and larger enterprises benefit from predictive and proactive support as well as from helping their human agents become more productive and efficient.

Retail and ecommerce businesses benefit from personalised shopping experiences and automated processing of frequent contact scenarios like order-related queries, returns, refunds and recommendations. Fintech companies like PhonePe and Razorpay have been early adopters of chatbots, and there is a huge opportunity in automating areas like claims processing, fraud detection and an overall improved experience. 

Healthcare, travel & hospitality, and edtech industries can use AI Agents to assist with appointments and resolving inquiries, automating the bookings and reservations process, providing personalised recommendations and handling customer inquiries in various languages without the need to staff with agents who can speak these languages.

Speaking about the challenges, she explains, “Many companies face challenges when integrating AI into customer service due to poor data quality, lack of real-world insights, and misalignment with customer needs.”

“To avoid common pitfalls, businesses must prioritise high-quality data, develop industry-specific AI models, and create iterative feedback loops for continuous improvement as well as invest in explainable and responsible AI solutions to build confidence and realise the full potential of what AI can do to transform their business,” she adds. 

There are also widespread concerns about AI replacing human agents. Businesses can navigate this fear of displacement by training and upskilling the teams on how to effectively collaborate with AI. Subramani shares a key insight: “AI should be positioned as a co-pilot that automates routine tasks while allowing human agents to focus on complex customer needs.”

“Employees should be trained to leverage AI-generated insights to improve response quality and efficiency. Upskilling initiatives should emphasise the value of human-AI collaboration, where AI manages automation while human agents provide empathy-driven service,” she explains.

To measure AI’s impact on customer service ROI, Subramani suggests tracking key metrics such as Net Promoter Score (NPS) and Customer Satisfaction (CSAT) to gauge customer sentiment, First Contact Resolution (FCR) and Average Resolution Time to assess efficiency, and Customer Effort Score (CES) to evaluate ease of interaction. The impact on response times and abandonment rates should also be looked into. 

“Beyond these metrics, businesses must consider qualitative factors such as brand perception, emotional connection, and personalisation. AI should enhance, not replace, human empathy, and a balanced approach combining data-driven insights with qualitative feedback helps companies optimise AI investments while strengthening customer trust and loyalty,” she adds.

It’s also just as important to ensure that the AI models being used are ethical, fair and unbiased in their performance, which requires the use of good, unbiased, high-quality, diverse and detailed training and data testing.

Looking ahead, AI capabilities will advance beyond rule-based automation into agentic AI platforms capable of autonomously breaking down tasks, delegating actions, and managing complex customer service operations. Subramani expects a shift: Instead of the need for manual programming of each step, AI will progressively handle entire workflows with minimal human intervention, and the systems will, over time, become capable of self-evolution where they improve their performance by learning from every interaction.

“Companies should prepare by integrating AI-driven automation into their CX strategies, investing in platforms that support multi-agent collaboration and task delegation. Ensuring data quality, continuous AI training, and ethical AI deployment will be crucial to maintaining seamless and personalised customer interactions. Businesses that proactively embrace these intelligent, self-governing AI systems will lead the future of customer experience by delivering highly efficient, frictionless, and deeply personalised support,” she concludes.