How to invest in chatbots today while technology is still evolving

8 min readNov 30, 2020


Carefully planned value-capturing practices can help companies gain significant benefit from chatbots

Photo by Alessio Ferretti on Unsplash

Conversational AI or Chatbots are thriving and proving indispensable for businesses aiming at providing superior customer service and improving sales. Blazing advancements in NLP technology (natural language processing) with the massive jump in digital data and the use of smart devices have amplified AI and machine learning’s perception and cognition capabilities. Thus, making the bots robust to handle a wide range of tasks previously handled by humans from answering customer queries, brand awareness, lead generation, research & survey, to actually making a sale.

Humans have also become more comfortable in interacting with the machines via voice, video, and messaging, thus making bots an essential consumer communication channel. Its usually seen that consumers prefer to use chatbots when in need of quick answers to simple questions. According to Salesforce research in 2019, 58% of users say chatbots have changed their expectations of customer service, while 77% of customers say chatbots will transform their expectations of companies in the next five years.

In a world where uncertainty and winner-takes-all-or-most has become a norm; and consumers’ expectations have raised multifold, organizations need tools to provide seamless customer experience while also reducing cost and increasing revenues.

What exactly is Conversational AI

Chatbots intelligence complexity can range from Transactional bots to intelligent conversational bots.

Transactional Chatbots are mostly utilized in areas such as restaurants, and delivery services, who know in advance what common solutions a customer may require.

Intelligent conversational AI systems or chatbots employ AI + Human-Agent model. It uses AI to understand the context and respond in a human-like manner. If an interaction takes a turn that the AI can’t handle, the system falls back on a human agent. Cognitive intelligence in these chatbots can potentially handle the nuances of customer interactions such as emotions, sentiment, and intent with ease.

A conversational bot allows a business to provide personalized attention to millions of clients simultaneously, in real-time, ensuring customer loyalty, and build a relationship. It also continuously learns from the granular data generated from the conversations, turning it into insights about consumer behavior and consumption patterns.

Chatbots have proven to be an effective customer communication channel over apps, websites, and email, etc. It has shown to connect with 2–5x more customers than previously done over email and provide a genuine sense of interaction with a company or brand in a much more meaningful way than, say, accessing the information on a web page or completing an order online. In other words, chatbots can seriously boost engagement. And for organizations, it means to scale and adaptability at fraction of cost.

Conversational bots are used in both organization’s internal and customer-focused applications. Chatbots in internal enterprises centric applications takeover laborious and monotonous tasks in the organizations such as payroll FAQs, speeding up recruitment and on-boarding, and employee grievance redressal. Slack bots such as Birdly and Howdy, are such examples, which is used in filing expense reports and as digital coworker respectively.

Customer-focused chatbots metamorphosed customer-centric and intelligent sales and marketing activities of the organization. The new generation of empathetic AI agents with anthropomorphic characteristics excel in customer service applications ranging from generating leads, aiding marketing campaigns, to preventing cart abandonment for E-Commerce Brands. These chatbots with or without a UI are also directly available to consumers via apps integrations like WhatsApp, Facebook, etc. A few examples of conversational chatbots are Clara, Mitsuku, Fin, and Facebook M.

Chatbot Trends & Growth Areas

The conversational artificial intelligence (AI) market is currently at $3.2 Bn and is projected to reach $15.0 Bn in 2024. Going forward chatbot adoption will see a spike in areas ranging from banking and finance, health care, food delivery, to legal services. As more and more consumers are demanding 24/7 service for assistance, especially in pandemic times, companies are rapidly looking to deploy chatbots and virtual assistants to be available for consumers at the tip of their fingers. An estimate from BI Intelligence states that the adoption of chatbots could save the healthcare, banking, and retail sectors about $11 billion annually by 2023.

Chatbots are mostly engaged in marketing, customer service, ITMS, and payments. But, clearly, the segment that is poised to gain the biggest benefits is customer service. According to Juniper’s research, the introduction of chatbots will save 2.5 billion customer service hours by 2023.

The conversational AI startup space has seen an exponential rise in startups in a variety of industries. Some chatbot startups are strong in funding and have the potential to revolutionize the industry, like — Dixa (Funding: $52.9M), Dialogflow (Funding: $8.6M), Avaamo (Funding: $23.5M ), Uniphore (Funding: $15.8M), Kasisto (Funding: $72M), Pypestream (Funding: $42M), (Funding: $45M), and (Funding: $11M).

notable chatbot platform startups in 2020, Image credit- author, sources-google

Don’t be carried away by the hype

While the demand for chatbots is increasing astronomically, it is important for companies to understand how they would utilize them. Chatbots, like any system within an organization’s IT infrastructure, must be utilized strategically. According to research done by Forrester, 57% of companies are using chatbots or plan to use them many have failed to use them correctly and strategically. The reasons for this shortfall could be anything from chatbot selection to overestimating the complexity of modern chatbots. While chatbots are getting increasingly advanced, a gap still exists between chatbot theory and chatbot practicality. Though chatbots have the potential to revolutionize businesses, they are only beginning to slowly alter business operations in a significant way.

Core Practices for Implementing and Scaling Chatbots

Image credit — author

1. Right Strategy is the Key

Chatbots are crucial in the organization’s digital journey and in delivering next-gen intelligent customer service. It is essential to align the chatbot automation strategy with an enterprise-level roadmap. IT leaders should have a clear understating of business requirements and target audience along with chatbot platforms readiness to support the functionality. Failing to understand user requirements and their interaction journey with the company will lead to poor customer service, mostly likely exasperating the customers. It is also vital for the IT leaders to understand technology scalability, build-reuse, modularity, effort and agility, and support services when evaluating a chatbot platform.

2. Cross-Functional Teams Collaboration

Chatbot automation should be a companywide strategy. Gaining confidence across all the business functions including IT teams, such as legal and compliance, HR, operations, etc., via continuous learning and collaborative work is essential. This will help the organization to understand the risk associated and improve technology effectiveness and adoption. IT Leaders need to ensure that support staff is made aware of the benefits of chatbot deployment and train adequately.

3. Enterprise Integration

Understating the organization’s current ability to integrate its enterprise systems and CRM tools with chatbot tools is essential to streamline bot operations. When organizations operate on legacy systems, chatbot implementation without an organization-wide digital strategy and necessary APIs for integration would create a piecemeal approach. This would undermine the chatbot’s full potential for mining data from various sources. Apart from this, establishing standard protocols and repeatable methodologies for maintenance and updates are also essential to provide a seamless end-user experience.

4. Learning & Tracking

There is no magic wand, conversational AI prerequisites training, and learning. AI-based chatbots require time to train and learn, other than the infinite use cases that can be enabled. A clear plan on learning, sources of knowledge, integrations, testing, and improvements should be present. Tracking of progress, tweak if needed and elimination of bias from the bot is essential. One of the worst mistakes of chatbot one can make is when the bot uses the same answers over and over again. In addition to continuously learning from non-progress events, the chatbot should be designed to be user-centric and content-driven. Continuously training AI models by gathering millions and billion data points can make chatbots more accurate.

5. Chatbots Needs a Right Persona

A well-trained AI conversational agent is intelligent and smart enough to engage with users in an empathetic conversation and understand the contextual meaning of the conversations. It should be able to speak your audience’s natural language, ask relevant questions, become an advisor to customers, and solve an actual problem.

But, a majority of current mainstream chatbots used to increase customer engagement and cut costs are rigid, prescriptive, and are unable to work outside of their scope. The inability to express the emotions of the customer especially in a case when the chatbot cannot solve a customer’s problem ends up in user annoyance and cessation of use.

Research shows that when chatbots consider customer’s emotions and understand conversation context, they tend to significantly gather higher customer satisfaction with the service. Research also suggests that customer reaction to error is significantly influenced by perceived competence and trust in the agent.

6. Human-Machine Symbiosis

Companies implementing chatbots many find total returns have fallen short of their estimated targets. Two main reasons for this are, first, organizations usually fail to consider how automating the customer-facing processes will affect upstream or downstream handoffs and connections, which can introduce new inefficiencies, capping the value delivered by automation.

Second, companies often limit the scope of automation to point solutions aimed at eliminating work. Given that most companies are working to make the same basic efficiency improvements, setting a chatbot, and counting on it to complete the tasks is a mistake. They can handle basic tasks but will irritate the customer quite frequently without constant supervision by the human team. Nonetheless, a chatbot+human team can prove to be more effective than a bot-only or human-only team in delivering superior productivity.

7. Value Unlocking by Increased Focus on Customer Experience

By designing chatbot automation in the context of experiences, organizations can unearth hidden opportunities to carve out differentiation, remove points of friction, and gain multiple wins in the form of superior experience, cost and value, and engagement.

This can be mainly achieved by the customer experience design (CXD) framework. CXD is a holistic approach that helps in choreographing systems thinking in the company to truly understand the pain points of the customer journey. This process mainly focuses on mapping the end-to-end customer journey; setting a standardized operational blueprint for key transitions and handoffs, critical skills evaluation, and escalations; assessing technology advancements at every stage; and focus on change management by identifying and supporting impacted customers right at the inception stage.

The Future of Chatbots Is Bright

With customer expectations becoming more and more fluid and growing because of new business challenges caused by Covid-19, chatbot technology has reached another steppingstone in its ascent, forming a vital part of organizations digital customer service strategy, helping companies serve both customers today and to position for the future. Along with this comes a ratcheting up of the urgency to scale the chatbots among those still early in their adoption journeys to realize the full technology impact.





Business Analyst and Startup enthusiast focused on Artificial Intelligence and Deep Learning