AI Automation Archives - BotCore

How Are Chatbots Boosting The CRM Adoption Rates

Rakesh Reddy — Fri, 08 Mar 2019 18:36:00 +0000

How Are Chatbots Boosting The CRM Adoption Rates

CRMs and the adoption problem

Less than 40% of businesses have a CRM adoption rate over 90% – CSO Insights.

One of the biggest challenges organizations face with Customer Relationship Management (CRM) platforms is the low usability and adoption rates. In our experience, this is caused primarily due to two significant reasons:

Manual data entry: According to HubSpot, manual data entry is the no. 1 CRM adoption challenge. Sales and customer support representatives are often bogged down by the extensive manual entry of customer data. This not only affects their productivity but also demotivates them to use CRM more often.
Poor UX: CRMs with poorly designed UX also result in decreased user adoption, productivity, and loss of sales opportunities which negatively impacts revenue.

A sales representative’s time is invaluable to the company and cutting down on the time spent on manual tasks like data entry allows representatives to close more deals and perform better.

One effective way to tackle these challenges easily is by integrating a chatbot with your CRM system. CRM chatbots can automate and simplify data-entry, provide faster access to data, enhance the user experience, boost adoption and empower sales reps to focus more on selling.

How Chatbots Help Boost CRM Adoption

1. Automate data entry

The CRM bot mines audio calls whenever a sales rep talks with a prospect or account executive speaks with a customer. It captures keynotes, details, insights from the prospect/customer and enters the data automatically into the CRM system, which sales reps have been manually entering. Bots can go to the lengths of logging all the customer information like budget, cost, challenges, and objections about a product while representatives are on a sales call with the customer.

2. Simplify data entry

Updating customer records will be made simpler with bots as sales representatives can achieve this without logging into the CRM system every time an update has to be done. Users can chat with the CRM chatbot in natural language and provide voice or text commands to update the records with the relevant details. Ex: “Change lifecycle stage of Customer X from lead to opportunity”, “Add customer Y’s phone number – 1234567890”.

3. Access data faster

In addition to simplifying and automating data entry, CRM chatbots also enable users to access customer information via chat. Users can ask natural language questions like “What is the deal size of customer A”, “When is the follow up due for customer B” etc. right within their actively used messaging app. This conversational workflow eliminates the need to continuously navigate through the complex UX of a CRM system for gaining customer/lead insights.

According to the input received, chatbots can send data in simple text or multimedia formats like graphs or pie charts, etc.

4. Personalized Alerts

Driven by artificial intelligence, CRM bots will be able to send alerts to sales representatives and account executives about pending action items, KPIs and even sales insights that come in handy during interactions with customers.

Integrate Chatbots With Your CRM systems For Better Adoption and productivity

Chatbots solve the two biggest problems that hinder your CRM adoption rate – manual data entry and poorly designed UX. Chatbots act as a single point of contact for your sales reps for data entry and data access. Users no longer have to go through the clunky UX of a CRM every time they need something from the system – a quick chat does it all.

The simplification and automation of data entry frees up sales reps who will have more time to handle critical and value-adding tasks that greatly improve productivity and revenue.

Acuvate helps organizations build CRM chatbots with our enterprise chatbot builder platform – BotCore. We support integrations for a plethora of CRM systems including Microsoft Dynamics 365, Salesforce, Zendesk, Oracle, etc. If you’d like to learn more about this topic and how our clients are benefitting from CRM bots, please feel free to get in touch with one of our chatbot consultants for a personalized consultation.

The post How Are Chatbots Boosting The CRM Adoption Rates appeared first on BotCore.

Top 5 Myths About AI You should stop believing

Abhishek Shanbhag — Mon, 11 Feb 2019 19:46:00 +0000

Top 5 Myths About AI You Should Stop Believing

As much as AI and its abilities are being spoken about and speculated today, there are quite a few myths that are doing the rounds as well. While some believe that it to be a deus ex machina, a tool that can resolve seemingly unsolvable problems, there are others who see it as just hype that will eventually die down. The reality about AI however, lies somewhere in between.

With the growing need for technology to make processes more efficient and provide companies with a competitive advantage, it is crucial to understand how AI can create value in your business and what its limitations are.

Alexander Linden, a Vice President Analyst at Gartner says “Business leaders are often confused about what AI can do for their enterprise. This is understandable, as there are many definitions and variants of AI that are present in the general discourse”. Misconceptions and a lack of understanding about AI is an impediment for IT leaders who are trying to incorporate AI in their organizations.

In this article, we bust the 5 myths that are often associated with AI and make a case for why you should stop believing them NOW.

The top 5 AI MYTHS

1. There Is No Need For AI Strategy

As the abilities of AI are evolving, organizations should consider tapping into its capabilities and understand the potential impact this technology may have in addressing the organization’s business needs.

Deliberately refraining from using AI is akin to forgoing the next phase of automation and could put your organization at a competitive disadvantage.

Even if you choose to abstain from adopting an AI strategy for your company, this decision should be made on pragmatic grounds supported by research and robust data. The need for AI in your organization should also be regularly evaluated and modified to fit the organization’s evolving needs.

2. AI Is All About Automation

While it is true that AI can automate several manual tasks, its range of capabilities are not always synonymous with automation itself. Automation is a subset of AI.

According to an extensive survey by Deloitte Research of AI applications in various industries, AI applications fall into three categories: product, process, and insight.

Product applications use AI to provide a better experience for the end user, either by enabling “intelligent” behavior or by automating tasks that a human user often performs.
AI is used in Process applications to enhance, scale up, or automate business processes.
Insight applications on the other hand use AI, machine learning and computer vision to analyze data and glean insights in order to drive better business decisions.

In only some of the cases is AI really used to automate human work. More often, AI is used to perform tasks that are unattainable by human cognitive abilities. While automation refers to the completion of a task without human interference, AI is used to power machines, making them capable of thinking or at least making intelligent decisions based on a series of predefined models and algorithms.

3. AI, Machine Learning And Deep Learning Are The Same

AI, Machine Learning and Deep learning are often interchangeably used or misunderstood to be the same, despite being fundamentally different.

Artificial Intelligence is the human-like intelligence exhibited by machines that encompasses various human cognitive abilities.
Machine Learning is a set of algorithms that allows a program to generate results accurately without the results being fed into the program explicitly. ML is in fact one of the tasks that AI performs by continually learning from the data fed into it and can be referred to as a sub-discipline of Artificial Intelligence.
Deep Learning, however, refers to the algorithms used to solve problems based on neural networks designed to mimic the neurons in the human brain. DL is one of the specialisations of ML and in turn, one of the aspects of AI.

4. AI Will Replace Humans

A common AI myth is that it can outsmart and replace humans at some point. This misconception is also one of the major barriers to AI adoption.

While it is true that AI makes machines extremely intelligent, we need to understand that machines cannot acquire such a potential all by themselves. The capabilities of a machine is limited to the data that is fed to it by a human being and the actions that they have programmed the machine to carry out.

It is important to identify the obvious benefits AI and ML add when it comes to automatically identifying patterns from an expansive amount of data with little to no human intervention. However, the algorithms and models that make this feat possible, have to be built by humans. So essentially, AI only can get as smart as a human mind can make it.

Another crucial feature that sets human intelligence well above AI is that humans are capable of recognizing when there is a problem or redundancy with a certain approach they are taking. AI models on the other hand, tend to pursue the best possible answer out of nearly infinite possibilities, even if it leads to them never exiting the process.

5. AI Is A Cost-intensive Undertaking

AI is looked at with apprehensive often, based on the assumption that it is going to be a costly investment for the company. However, high cost is not the case for AI in general. There are many AI tools that are available for businesses that do not demand an exorbitant investment to implement AI solutions. Some important AI solutions which are cost effective and also yield a huge ROI and several business benefits include chatbots, RPA, modern intranets with AI capabilities and advanced analytics.

On the other hand however, AI development does require expertise in programming languages and development practices. While hiring data scientists for this job may rack up costs, organisations can consider training the existing resources to effectively implement the algorithms themselves.

conclusion

It is necessary that companies take concerted initiatives to develop comprehensive strategies that accommodate AI and prepare themselves for futuristic environments that are compatible with AI powered technologies. It is imperative for companies to act on these initiatives before any new market disruptors jeopardise the competitive edge the organization may hold in the industry.

This demands the leadership of organisations to have a pragmatic and precise understanding about AI’s capabilities and its trajectory into the future. Having a strong academic foundation and practical experience in AI among the leadership allows organisations to avoid misinterpretations and misleading myths about AI.

Adopting AI just for just a few functional areas will be ineffective in making a great impact on the organization as a whole. Hence, companies must try to incorporate an effective blend of embedded, edge and centralized intelligence systems across all functions and teams.

Adopting AI and related technologies in building an intelligent business environment will strengthen the alliance of humans and intelligent machines and produce a powerful workforce for the future. Companies must acknowledge that humans and machines will continue to be the indispensable building blocks of the new workforce and plan to utilise their combined strengths effectively.

Acuvate believes that organizations need AI to accelerate their digital transformation journey. Our range of solutions and services around conversational AI, digital workplace, business intelligence and RPA are built with a strong AI foundation. If you’d like to learn more about AI and its capabilities, please feel free to get in touch with one of our AI experts for a personalized consultation.

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