When it comes to new technologies, disillusionment and skepticism are the angsty teenage brothers of hype and innovation. In recent years, all the excitement around chatbots and AI assistants, in particular, have brought with them a fair share of misinformation as well, from their capabilities to their development to the bottom line. Unfortunately, that’s to be expected. There’s a huge gap between the theories and the actionable, concrete promises of AI when it comes to its real-world business applications.
Conversational AI is hard
Successfully developing your own AI strategy and future-proofing your business involves a careful recalibration of your expectations, and it starts with looking at the realities of the market and AI’s current technological capabilities.
Conversational AI and chatbots are a booming market with tremendous business potential. Gartner predicts that by 2022, 70% of customer interactions will involve an emerging technology such as machine learning applications or chatbots, up from 15% in 2018. While these stats indicate a bright future, it’s important to understand that AI is not magic. It takes time. It takes money. And most of all, it takes high-quality data, loads of it.
To set yourself up for success, I’ve created a quick list of the most common misconceptions about AI, so you know exactly what to expect, and what not to expect, as you begin drafting your AI strategy.
Fantasy #1 – AI understands everything and can talk about anything
AI assistants or chatbots are not generalists. They’re specialists. Even Siri and Alexa have a hard time chatting about any given topic at any given time even with millions (if not billions) of dollars have been poured into them.
That being said, it’s unreasonable to expect your AI assistant to know everything about your brand, the weather, what Kim Kardashian wore to the Met Gala, and who the Prime Minister of Malaysia is.
AI can’t freestyle language on your behalf, and if your brand is dear to your heart, you shouldn’t let it. AI assistants are extensions of your brand and your customer support team, not replacements. As such, it should learn from as much of your existing data as possible (product databases, CRM, your FAQs, etc.) and from ongoing interactions with your CS team. We’re pioneering this hybrid model at Heyday.ai that helps accelerate the development of this brand-specific AI. We call it Brand-Safe AI™.
The takeaway? A good AI assistant is not a jack of all trades, and it shouldn’t be. It’s a brand specialist and a sidekick for your customer support team.
Fantasy #2 – A chatbot can be built in 10 minutes.
No matter what those sensational headlines from chatbot development companies suggest, don’t drink the Kool-Aid. Tools that promise to deliver a chatbot in 10 minutes are fine if you’re looking for a base-level starting point or prototyping tools; however, these are not sophisticated AI-powered chatbots by any means. They have no brains, and at best, they are automated workflows.
If you want something that will stand the test of time, that is intelligent and malleable enough to change with seasons, campaigns, and customer needs, it takes much more time to craft a great AI assistant. Ultimately, you get what you pay for, and if it takes 10 minutes to make, it’ll be about 10 minutes before it’s obsolete. A proper conversational AI assistant needs far more than a third of your lunch break and requires extensive training, lots of time, and lots of iterations.
Fantasy #3 – It can learn itself and figure it all out.
Machine learning opens up a world of possibilities, but it’s not magic. It’s called machine learning for a reason, and it’s only as good as the data its fed. So if that data isn’t pristine, you might end up with a PR catastrophe on your hands (let’s not forget about Tay from Microsoft which turned surprisingly racist in a quick 48 hours).
You must keep things in check and have a team supervising your AI training, which is ideally composed of non-technical people and developers alike. A healthy approach to machine learning incorporates both rule-based tools as well as neural net training. Because the reality is that we’re just scratching the surface of AI, and the more tools and perspectives, the better.
Fantasy #4 – You build your AI application once and it’s one-and-done
Launch Day might be the most exciting day for your AI assistant, but it’s actually the worst for its performance. it’s not like a mobile app or website, where the heavy lifting is more or less over once you’ve shipped it. It’s not one and done. You’ve taught your AI as much as possible, but once it starts interacting with people for the first time, it’s bound to trip a few times before it starts walking.
That being said, it takes time and data to improve AI. When you ship and deploy an AI assistant, that’s where the project starts, and the hard work begins. It’s important to continuously monitor your conversation logs to see your chatbots strengths and weaknesses, and flag opportunities for improvement. And this is one of the greatest benefits of a chatbot: its malleability.
Nothing is set in stone and you can always make adjustments on the fly based on your customers’ interactions, preferences, seasonality, marketing campaigns, and more.
A viable path to Conversational AI: 3 steps
So, now that we’ve cleared the air and you know what really goes on with building and launching a conversational assistant, how do you get started?
- Open up a 1:1 communication channel (live chat, Messenger, etc.)
- Gather and analyze data (from customer conversations)
- Iterate and improve your AI assistant continuously
In the end, the idea is not to simply survive the AI disruption but to thrive and embrace it, and nurture a sustainable competitive advantage.
If Gartner’s previously mentioned forecast is right, you’ve got less than three years to get your AI strategy right and future-proof your company. Better start now
Want to learn more about building your conversational AI roadmap? Talk to one of our experts.