AI for Operational Efficiency: Strategies, Use Cases, and Best Practices

Adopting "smart" or "intelligent" automation seems like an unstoppable trend because it promises to save money, minimize human error, and produce faster results. The impact on workers and the workplace will be dramatic. 

In fact, 82% of businesses experience increased operational efficiency with AI. Goldman Sachs reports that "AI could easily automate two-thirds of occupations. The current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees' time today."

One of the most exciting promises of artificial intelligence is its potential to revolutionize our work. But with all the hype, it's important to ask: How does AI translate to a more productive workforce? Here are the answers:

• Customer service agents answered 13.8% more inquiries per hour.
• Business pros wrote 59% more work-related documents per hour.
• Programmers coded 126% more projects per week.
• 88% of staff who use AI and automation tools for their jobs say that the new technology improves their productivity.

Want to know more about how AI helps across industries? This blog covers the advantages of artificial intelligence (AI) for various industries and its implementation for operational efficiency.

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How does AI enhance operational efficiency? 

Employees are burdened with administrative paperwork, customer service departments are backlogged with calls, and sales teams manually follow up with clients. These common challenges can be overcome with a business's adoption of artificial intelligence (AI).

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Improve customer experience 

The impact of technology on customer service is entirely subjective and hard to measure. For starters, customers may not consciously seek out businesses with better technology.   

A third (34%) of CX leaders believe that increasing efficiency is the main benefit of AI technology in customer experience, and 29% of them believe that problem-solving capabilities are the main benefit. Only 18% see improved data analysis as the main benefit of AI in customer service, and 14% see increased personalization and engagement as the main AI benefit.

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With customer experience technology, the possibilities of personalization are endless. Even more important, these advances will help businesses free up their most valuable resource–their employees —to allow them to handle complex needs, which ultimately strengthens your long-term relationship with your customers. 

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Efficient and always-on customer service 

Consider this, a virtual assistant that answers customer queries and anticipates their needs, converses in a natural tone, and stands available around the clock. 

What was once the wildest dream of customer management leaders is now a reality at their fingertips. 

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AI voice agents like Synthflow AI use Natural Language Processing (NLP),machine learning, and generative AI to respond to open-ended questions, handle multi-turn conversations, and adapt conversations according to user inputs.

Synthflow AI agents are quite different from traditional chatbots because:

• They can understand the human context and can be spoken to directly
• They are capable of customer retention should the conversation go south
• They can make decisions and set appointments when necessary
• They're empathic and have advanced reasoning skills

AI voice agents can understand customers' needs and provide instant responses or resolve the query entirely at any time of the day or night, on any channel and in any language. 

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Automating routine tasks to free up resources

McKinsey reports that analysis of more than 2000 work activities across 800 occupations shows that about half of the activities carried out by workers could be automated. 

These technologies are already generating value in various products and services, and companies across sectors use them in processes such as product recommendations, finding anomalies in production, identifying fraudulent transactions, and more. 

An analysis conducted by McKinsey found that the most sophisticated deep learning techniques utilizing artificial neural networks could generate between $3.5 trillion and $5.8 trillion in annual value. This accounts for approximately 40% of the total value all analytics techniques produce.

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How AI is driving efficiency across industries

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AI's biggest strengths are its ability to automate tasks, reduce errors, and make data-driven decisions at scale. From predictive analytics to natural language processing (NLP), AI-powered applications enable faster and more accurate decision-making. 

These capabilities are undeniably valuable. AI-driven solutions have proven their worth in finance, healthcare, and manufacturing sectors by enhancing customer service, improving decision-making, and optimizing supply chains. 

AI in Insurance 

According to Bain&Company, insurance businesses worldwide have a $50 billion financial opportunity from generative AI to harness it in ways that could boost their revenues by as much as 20% and cut their cost by up to 15%. 

In an insurance team, AI can directly tackle many of the most common customer service communications you encounter. Implementing AI provides an opportunity to improve customer service. 

Calls to an insurance call center typically breakdown as follows:

• 55% are about obtaining information
• 35% are about executing a transition
• 10% are about resolving an issue

Instead of 80% of these calls handled by humans, conversational AI will reduce it by just 10%. The idea of implementing automation is to make call centers smarter and faster. It can be used for risk detection, predictive modeling, and customer service automation tasks. 

The effects will likely surface in both human and digital-led channels. For instance, it will reduce low-value interactions with tied agents and focus on self-service and tailored conversations at scale. 

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For instance, Helvetia in Switzerland has introduced a generative AI-powered service for direct customer support, answering questions on Insurance and pensions. Meanwhile, HDFC Ergo in India has launched a center that uses generative AI to create hyper-personalized customer experiences.

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AI in healthcare 

According to SpruceHealth, a typical healthcare provider handles 53 patient calls per day and spends 66 minutes on phone calls overhead per day. Medbelle, a leading healthcare provider in the UK, has shared that manual processes lead to patients waiting 1-2 days to answer simple queries.

With the rise of conversational AI technology, tools like AI voice bots are transforming these time-consuming processes, offering instant, scalable solutions for patient interaction.

Here are some of the fantastic use cases of conversational AI in healthcare:

• Appointment scheduling:  AI can automate repetitive tasks of appointment management, unlocking a new era of efficiency. An intelligent conversation AI platform can help:

• Scheduling, rescheduling, or canceling appointments drastically reduces manual input and potential human errors. 

• Offering an intuitive chat interface or voice assistant for seamless appointment booking.

• Minimizing wait times by automatically displaying available time slots during the booking process.

• Personalizing the experience by allowing patients to select their preferred dates, times, and doctors.

• Patient care management: Healthcare's fragmented nature makes it difficult for consumers to obtain a complete view of their health information throughout the entire care journey. AI offers a pivotal touchpoint between patients and healthcare professionals. It goes beyond enabling communication—it's about empowering individuals.

This technology lets patients quickly request prescription refills, view test results, and access detailed medication information. 

• Information gathering and document retrieval: Healthcare providers are overwhelmed by the sheer volume of administrative tasks they must manage. According to the American Academy of Family Physicians (AAFP), doctors dedicate approximately 50% of their work hours to administrative duties, spending up to 4.5 hours daily on EHR systems during and after work hours.

Conversational AI can engage in one-on-one interactions with patients, gathering all necessary information during the conversation. The AI can access existing records or generate new documentation to update patient files or create new cases through seamless integration with the organization's systems and processes.

• Invoice payment and claims: The conversational AI solutions streamline this process by creating auto-invoices, forwarding invoices to the insurance department, and sending regular payment reminders. 

For patients, these tools help:

• To promote transparency, provide clear treatment cost estimates and billing details, and address patients' follow-up questions.

• Streamline secure payment processes by offering multiple payment options and enabling installment plans.

• Assist patients with insurance-related queries, including claim statuses and reimbursement rates, while identifying errors in claim requests and accelerating their resolution.

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AI for sales

Sales reps spend only 30% of their time selling during an average week. Many high-performing sales leaders feel like there just aren't enough hours in a day to do all of the things they need to do.

Here are the key use cases of conversational AI in sales:

• Scheduling appointments: Conversational AI can be programmed to send automatic messages to keep prospects engaged and move them through the sales funnel. This process reduces the chances of errors, such as overbookings or miscommunications, which are common in manual systems. 

For instance, Synthflow AI helped Medbelle  handle inbound and outbound inquiries and online appointment booking. With Synthflow, Medbelle has booked 2.5x more qualified appointments and reduced no-shows by 30%.

• Nurturing leads: Traditional methods qualify leads based on superficial factors such as job title or company size. Conversational AI, however, digs much deeper into analyzing leads' actual interactions with the brand. 

Depending on the responses and other non-verbal indicators (reply frequency, sentiments, length of responses, etc.), the agent can perform lead qualification and scoring to aid salespersons. 

• Upselling and cross-selling opportunities: AI agents seamlessly integrate upselling and cross-selling into the conversation without interrupting the customer's journey. It analyzes the interaction context, and AI agents introduce suggestions as part of the conversation, making it look more helpful rather than pushy.  

For example, if a customer buys a camera, the AI can probe better contextual dialogues to understand the needs of the customer, be it wildlife or amateur photography enthusiasts. It can suggest lenses based on their style of photography rather than simply suggesting generic camera accessories. It can also follow up after a few months to suggest new accessories or battery replacements to upsell a product.

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AI in banking 

According to Gartner, nearly 60% of banking CIOs plan to deploy AI tools within the following year. Another Nvidia research suggests that 30% of financial enterprises enable AI to increase annual revenues by more than 10%, while over a quarter stated that AI reduces annual costs by more than 10%.

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Here are the key use cases of conversational AI in banking:

• 24/7 personalized assistance: Every week, banks hear from thousands of customers by phone and online chat. While 40-50% of those chats are quickly resolved by the current classic chatbot, many people still need to speak with a live agent for help.

One of the standout benefits of AI chatbots and voice bots is their 24/7 availability. This availability helps users get answers, resolve issues, or address complex queries at any time.

• Customer onboarding and KYC verification: The lengthy and time-consuming paperwork is a big turn-off for customers. Because of this, 40% of banking customers abandon their onboarding. Of this, 39% say the process takes too long, and 34% drop off because too much personal information is asked. 

But thanks to artificial intelligence in KYC, the problem has been resolved to a great extent. An AI chatbot can help reduce false positives by up to 80-90%. This means that where it could take 10 working days to complete the process, AI can complete it in 3.

• Payments and Transactions Reminders: AI answering services are increasingly being integrated into banking systems to automate the process of payment processing and reminders. They can send reminders through various channels, including SMS, voice calls, and emails, ensuring customers are constantly informed about upcoming or overdue payments.

Nevertheless, users can also schedule transactions by interacting with payment bots in real-time. This reduces the manual workload on banks and assists customers in paying their payments on time.

Implementing AI for operational efficiency: Step-by-Step Guide 

Implementing AI automation in your business is like preparing a gourmet meal–it requires careful planning, precise creativity, and a sprinkle of creativity. Follow this step-by-step guide to ensure a seamless transition and unlock the full potential of AI-driven efficiency. 

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Step 1: Assess your needs 

Before you deploy AI in your business, evaluate your current processes with a magnifying glass. Identify bottlenecks, repetitive tasks, and communication gaps slowing you down. 

Common business pain points include:

• High cart abandonment rates
• Inefficient appointment scheduling
• Slow response times in customer service

This assessment will reveal the best use cases for AI workflow automation and help you choose the right tools that fit your needs. 

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Step 2: Select the right AI tools 

Once you have evaluated your current operations and pinpointed the areas causing difficulties, the next step is selecting the appropriate tools to effectively resolve your particular requirements. It's important to consider key factors before making a choice:

• Define your objectives: Clearly define what you want to accomplish with AI. Are you aiming to improve customer service, optimize inventory management, enhance personalization, or something else? Defining your objectives will help you narrow down your choices. 

• Scalability: Choose tools that can handle growth of your business. For instance, Synthflow AI scales as your business grows. Whether you need to make a hundred calls or a thousand AI calls, it can scale within seconds. 

• Integration with existing system: Ensure that your AI tool can seamlessly integrate with your existing systems such as your website, CRM, or inventory management software. This compatibility is essential for a smooth transition and operational efficiency.

• User-friendly interface: Choose an AI tool that your team can easily navigate and use without needing any technical guidance. Complex tools require training, which can delay implementation. 

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Step 3: Integrate AI with your current system

With the right AI tools in hand, it's time to fit them into the current system. This might involve API interactions, system configurations and data migrations to work together seamlessly. 

Collaborate with the technical team or the tool vendor's support team to ensure everything fits together like a well-oiled machine. Test the integrated system thoroughly to catch and fix any compatibility hiccups. 

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Step 4: Train and onboard your team 

Invest time and resources in team member's training and onboarding to ensure everyone knows how to use the tools effectively.  Provide clear documentation, hands-on training, and video tutorials. Foster open communication and encourage the team to provide feedback. 

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Step 5: Monitor and optimize AI workflow 

Once AI workflow automation is all set and running, keep a watchful eye on performance and efficiency. Establish key performance indicators (KPIs) to track the spot and progress and spot areas for improvement. 

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To assess the ROI accurately, follow these KPIs: 

• Call handling time: It is the total time an agent spends managing a customer call, from the moment they pick up until the call is fully resolved. This KPI directly impacts customer satisfaction since faster resolution means happier customers. 

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Call Handling time=Talk Time+Hold Time+After-Call Work

• Customer satisfaction (CSAT): It is a key customer experience metric used to measure how satisfied customers are with a product, service, or interaction. It is typically collected through surveys that ask customers to rate their satisfaction on a scale.

• Operational cost: Operational cost refers to the ongoing expenses required to run a business or organization. These costs are necessary for day-to-day operations and include both fixed costs (which remain constant) and variable costs (which change based on business activity). 

After the implementation of AI tools, you should see a reduction in your operating costs. 

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Why choose Synthflow AI?

Synthflow's no-code, drag-and-drop interface allows users to set up a voice agent in minutes without needing any technical knowledge or coding ability. It can seamlessly handle a large volume of calls across all timelines and languages. 

Features:

• Zero latency
• Text-to-speech and advanced transcription features
• 11labs integration – clone voices and automatically schedule appointments into your calendar
• Robust customer support
• Advanced call sorting, logging, and transferring
• Generate responses and action them live in call
• Resell Synthflow – fully white-labeled version available

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Want to try a free demo to see how you can replace your traditional call centers with AI voice bots? Sign up here. 

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