Many SMBs are wary of implementing artificial intelligence, thinking it a risky technology. Far from it. You may even already be using it and not realize it.
Many small- to medium-size businesses (SMBs) wouldn’t consider installing artificial intelligence (AI), thinking it a technology that’s too far ahead or challenging to implement and use. It isn’t. You may already be using apps with AI embedded in their functionality and not realize it. AI can increase human productivity and efficiency. It won’t replace workers; instead, it will free up employees to address tasks that need the human touch to grow business.
Types of AI
Machine learning (ML) and deep learning – which takes ML a step further using neural networks – are two of AI’s many subsets that are commonly used in business technologies for SMBs. These forms of AI use an “If so” then this, “If not,” then that, technology, like decision-making flow charts where users are guided toward decisions based on data received. ML systems get smarter the more you use them. Read more about the types of AI and cybersecurity challenges.
AI for customer relationship management
SMBs can apply AI now to many areas in business. Three use cases are customer relationship management (CRM), customer support and cybersecurity. You will need to take a leap of faith with vendors who have a track record of success in your industry – those with mature products with integrated AI tech.
Ready to give this tech a try?
Many CRM vendors are supporting SMBs using ML and deep learning. For example, FreeAgent CRM Software is a cloud-based, browser-agnostic solution that uses AI to aid selling. It generates a flow-based feed of prioritized tasks and insights.
HubSpot uses ML and deep learning to help SMBs make relationship-building decisions based on data, similar to how Netflix makes movie recommendations. It uses ML to develop an on-target SEO (search engine optimization) strategy which understands search topics associated with the business’s online content.
Natural language processing and customer service
The goal of AI in natural language processing (NLP) is to teach machines to understand human language. Early examples include Siri and Alexa. It’s well established as a customer service enabler that people are now used to using. Think of how live chat has revolutionized customer support by making it available 24/7.
Many customers are perfectly happy to talk to a bot for straightforward requests instead of plowing through pages of a website to find hours, office locations or pricing. It provides synergy with live customer support; when issues become complex, people take over. An AI bot can handle customer service requests and sell too.
Ready to give this tech a try?
NLP is an important ingredient in customer support applications like Zendesk Suite, targeted to SMBs. It uses a combination of ML, deep learning and NLP for customer support, including live chat.
NLP can also separate relevant data like human sentiment which can be used for different purposes. Birdeye uses an NLP engine to pull human sentiment from feedback to improve product feature development in R&D phases and operations.
Many SMBs are challenged when it comes to having time available for human sales efforts like lead generation. smith.ai Chat uses AI chatbots to capture website leads for small businesses.
Most of these CRM and customer service products offer free trials, so you don’t need to invest significant resources if you find out it doesn’t meet your needs.
The cybersecurity challenges of AI
No SMB can survive without a robust security package across all points of its network. But when it comes to applying AI for cybersecurity enhancements, it’s a tricky proposition. While AI is helpful to guard against attacks, bad actors can turn it against companies by targeting the algorithm’s training data.
According to a MIT Technology Review article, at a 2018 Black Hat conference, several companies admitted they were releasing machine learning products to stay relevant with customers who are enamored of AI. That poses a problem as companies could think they’re safer using AI than they are.
Some products which use supervised learning data sets that algorithms train on label the data as clean code or malware. Here’s the rub: companies can use training data that hasn’t been completely scrubbed of anomalous data points, which in turn may make the algorithm miss attacks. Bad actors could also get hold of the data and change labels, labeling malware as clean code.
This doesn’t mean machine learning and AI don’t have important roles to play in your defense lineup. MIT’s article concludes that customers need to monitor and minimize risks associated with algorithmic models.
AI can power your digital transformation
Digital transformation is here for SMBs. These new technologies to help power it are in reach and available now, helping you to compete and take on the bigger players. And like enterprises, SMBs that adopt emerging technologies like AI can gain a competitive edge and grow smarter and faster than the rest.