Businesses increasingly shift focus towards scalable conversations with customers, leveraging advanced conversational AI platforms to meet consumer expectations. As a result, the Conversational AI market is set to grow 30% annually until 2024, with more use-cases emerging to solve critical challenges.
Conversational AI platforms offer a programmatic and intelligent way of offering highly personalized and insightful conversations with customers.
As 66% of customers start with self-service mediums before connecting with live agents, it is essential to engage with them early on. Conversational AI can help boost acquisition by around 10-15% simply through its transformational capability to offer scalable personalized experiences.
What’s critical in conversational AI is selecting the right solution that drives business results. While each platform will have to be customized, ensuring that it can manage conversational volumes and be scaled easily will be a top priority. With new technologies being introduced rapidly, enterprises need a reliable and flexible solution that can manage their needs.
Conversational AI and Its Growing Importance
Conversational AI integrates technology innovations such as NLP, intent recognition; voice optimized responding, contextual awareness, and machine learning. Through omnichannel engagement, it opens new opportunities for companies that want to imitate human interaction at scale. They can be designed to speak in multiple languages and interact with customers with nearly indistinguishable cadence and tonality to that of a human.
The benefits and use-cases of conversational AI are some main reasons why it is growing in importance worldwide. Let us explore some of the top benefits of adopting the right conversational AI platform –
1. Customer Service Automation – Conversational AI platforms are ideal for customer service interactions that require voice or preliminary data inputs. They can automate support, acquisition, and retention-based functions with ease.
2. Onboarding for Customers and Employees – Onboarding with the right information can be automated as well using voice or chat inputs. Conversational AI can be designed to be highly compliant while being personalized to offer a familiar experience.
3. Data Integration and Analysis – Conversational AI solutions allow for extensive voice analysis and call meta-analysis to generate new insights based on consumer sentiment. It offers a completely new data vector in the CRM and sales analysis domain.
4. Lead Verification/Qualification Automation – Leads generated can be followed up with the right verification calls automated through conversational AI. The AI bot can analyze real-time communication and verify key details required for qualification.
5. Managing Account Information for Users – This is a key feature for banking, finance, and insurance firms that offer routine information to customers concerning their account details. Balance, payments, premiums, and other details can be scaled to millions of simultaneous calls.
Ways to Determine Right Solution for Businesses
With 57% of global enterprises believing conversational AI platforms to be highly ROI positive, the right approach is necessary to determine the perfect solution. The ideal strategy to determine the right fit is to map key requirements and identify gaps within current solutions that can be turned into use-cases.
1. Auditing Existing Customer Engagement Infrastructure
It is ideal for auditing existing technology solutions when focusing on existing requirements. This can include chatbots, AI tools, data analytics, CRM, and other critical solutions. Companies can identify key gaps within their technology ecosystem, such as –
- Lack of automation within processes
- Inability to manage large volumes of customer interactions
- Lack of omnichannel presence across platforms
- Poor visibility into customer conversations
- Lack of scalability and personalization in engagement
Additionally, enterprises should ask themselves – what is conversational AI – as it relates to Additionally, enterprises should ask themselves what conversational AI is as it relates to their needs. This can help firms think outside of traditional integrations and explore new use-cases that can drastically improve output and productivity.
2. Developing a Requirement List of Critical Features
A core requirements list should be developed when considering conversational AI platforms. This will refine the search further and provide context to solution areas necessary. It will also help firms gain insights from internal teams about the best applications for conversational AI platforms.
An extensive technical sheet can identify key requirements, such as automated onboarding, IoT voice-response analysis, and routine calling. Challenges such as scalability, personalization of services, and flexibility in adoption can remain prioritized as firms find the right conversational AI platform.
Nearly 67% of firms, having the right tools to deliver personalization continues to be a significant challenge
Again, having a technical requirement sheet ready, with integrations, APIs, NLP, and language requirements listed can help significantly.
3. Engaging with Conversational AI Platform Providers
There are specific parameters that enterprises should review when engaging with Conversational AI platform providers.
- Solution offering, features, and customization capabilities.
- Requirements matching and feature extension ability.
- Scalability of solution, limitations, and challenges to adoption.
- Industry use-cases, successes, ready solutions vs need to build.
- Quality of voice conversations, closeness to real human engagement.
- Data analytics, AI and NLP algorithms, and machine learning capabilities.
- Pricing efficiencies and onboarding seamlessness.
These parameters should help companies compare multiple providers and find the right fit for their specific requirements.
4. Developing Use-cases and Scaling Up
Focusing on the right use-cases that can be developed will be key. Companies can invest budgeted resources in developing use-cases with single or multiple conversational AI partners. While use cases such as outbound calling, KYC, and lead qualification offer maximum returns, companies can explore new solutions through the power of conversational AI.
A critical way for firms to know if they’ve opted for the right conversational AI platform is scalability. If solutions are buggy or need frequent patches, then they are not scalable or flexible by design. The right fit for enterprises will be the ones that can be scaled up across functions or customer domains and continue to generate transformational value.
Conversational AI platforms are emerging as the next stage in comprehensive digital transformation. As a result, companies are looking for the right ways to find the ideal solution-fit for their unique requirements. From banking to travel, firms across industries are already engaging with conversational bots and can transform their results by following the above approach.
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