5 Thoughts on Conversational AI We’re Thankful For | Whiz AI

5 Thoughts on Conversational AI We’re Thankful For

Nov 26, 2019

Dear whizdom readers,

In this dedicated season of giving thanks, we thought it’d be a good idea to compile 5 powerful thoughts on conversational AI that we’re thankful for. We curated a selection from industry thought leaders that is both informative and varied in perspective. Let’s end the year with a bang, and hit the ground running in 2020!

Thought #1

from Gartner’s “Hype cycle for Artificial Intelligence 2019” (Published: September 12, 2019)

Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, and AI PaaS / cloud services as enterprises ramp up their adoption of AI.

“Most organizations’ preference for acquiring AI capabilities is shifting in favor of getting them in enterprise applications. Intelligent applications are enterprise applications with embedded or integrated AI technologies to support or replace human-based activities via intelligent automation, data-driven insights, and guided recommendations to improve productivity and decision making.”

We’re thankful to our client in life sciences, a multinational specialty pharmaceutical company, for challenging our limits as we built a robust conversational AI platform for their sales and commercial teams. Whiz.ai is pre-trained on life sciences data like HCOs, NPI, Hx/Dx codes, and also understands analytics such as weekly run rates, rolling averages, and market shares to synthesize mountains of data into the most meaningful insights for users. With their support, we have incorporated the power of AI, data visualization and natural language processing to add business value in the form of prescriptive analytics, intelligent robotic process automation and advanced user experiences via voice and text on any device.

Thought #2

from Techrepublic’s “Why Conversational AI Will Become a C-Suite Priority in 2020” (Published:November 10, 2019)

Conversational AI is providing new ways for business users to interact with enterprise applications. For the past few years, many boardroom discussions may have echoed with ideas and plans for a digital transformation, thanks to the advent of the internet followed by the penetration of smartphones. Today, these discussions are moving toward building a conversation first enterprise.

“Conversational artificial intelligence (AI) is on the rise, and both Gartner and Accenture believe the integration of conversational AI will emerge as a top priority for the c-suite by 2020. A recent Gartner reportfinds that by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis.”

So far we’ve seen quite a lot of success in the life sciences and manufacturing industries and we’re thankful to them for placing their trust in us. Sales, marketing, finance and executives are our most popular business users. With the ability to just ask – speak or text – we’ve been able to increase adoption and ROI of existing enterprise applications and provide device-agnostic instant access to contextual insights.

Thought #3

from “The rise of conversational AI assistants and the demise of the chatbots” (Publish date: November 11, 2019)

No sales rep is glued to their desks 9-to-5. They lead the most hectic lives shuffling between tools and teams to derive insights about prospects with most potential to convert, reviewing reports for insights that can help build the right schedule to achieve their weekly sales goals.

“Some 80% of the global workforce (3 billion people) work in a desk less environment, while ~1% of the world’s software is designed with this workforce in mind.”

Chatbots are different from conversational AI platforms and no two bots are alike. There are several bad bots in the market that are fouling the name of the wider conversational AI market and we’re thankful that the industry is starting to realize and acknowledge this difference. For field reps to deliver on their sales goals, they need platforms that can learn from their preferences, data patterns and business context and not the other way round. With whiz.ai there is no need to look at complicated dashboards or learning new software and sales reps can spend more time doing what they do best – selling.

Thought #4

from “Gartner Symposium 2019 in Barcelona” (Publish date: November 11, 2019)

Finding the balance between legacy systems and emerging technologies through a symbiotic vendor-customer relationship was a recurring theme at this year’s Gartner Symposium. Coining the term ‘Techquilibrium’, Valentin Sribar, Senior Vice President at Gartner, stressed that it was crucial for businesses to find the right balance between the ‘traditional’ and ‘digital’, which is in constant flux. 

“Working at the forefront of new technology like conversational AI can be challenging, as there are no manuals or clear instructions on what to do. And even though a vendor may strive to develop the very best technology possible to satisfy a need in the market, it’s crucial that a business (along with its decidedly human resources) can take charge in molding a virtual agent into an asset for their organization.”

We’re thankful to our client in manufacturing for supporting us with useful training data so we could prepare whiz.ai on key business concepts such as sell-in, sell-through, inventory, and orders, and competitive metrics like market share, reviews, ratings and pricing to elevate your organization’s efficacy with contextual AI. Improved sales productivity with real-time access to sales, distributor and market data and 17% uplift in sales correlated to whiz.ai wouldn’t have been possible with algorithms and interfaces alone. Our working together ensured a successful launch.

Thought #5

from “Gartner Symposium 2019 in Barcelona” (Publish date: November 11, 2019)

In the same article, another thought that struck a chord was that AI is creepy when it comes across as ‘too human’.

“It may be a false endeavor to attempt to make your virtual agent come across as too human. Customers don’t like it and can easily see through it, and you end up bumping up against the uncanny valley effect.”

A while ago, Aaron Carlock, whiz.ai Co-founder was wrestling with the same question when he shared his thoughts via whizdom. Upon launching whiz.ai at our first major client (in life sciences) we began to notice that the more people thought of whiz as a person, the more out of touch with their expectations of what it could do became. Similarly, if we pitched it more as a tool to a user, not some chatty assistant, the more patience and understanding they had, with many even coming to its defense around others if it couldn’t answer the question. We’re thankful to Aaron for enlightening us with his experience. 

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