By Srikanth Balusani, MST Solutions
Here are some practical considerations to make before implementing it in your business.
As IT and business leaders, we’re all familiar with high-gloss examples of artificial intelligence — the expansive concept that involves a machine’s ability to carry out human tasks after being fed copious amounts of data that is trained with algorithms. AI has indeed been glamorized (and in some cases, chastised) by the advent of self-driving cars, listening and machine learning technologies like Alexa and Siri, and the robots that use IoT and AI to carry on a conversation or carry out tasks like controlling home appliances.
We wouldn’t be technologists if we didn’t enjoy hypothesizing about AI’s future applications. But, how will this forward-thinking technology actually play out in business? Is it even applicable to most? While 72 percent of business leaders said integrating AI would be a “business advantage,” according to a study by PwC, a similar survey by McKinsey found that “few companies have in place the foundational building blocks that enable AI to generate value at scale.”
To understand its implications, we first have to look at the current state of artificial intelligence, how it can be applied in various use cases in business, and then determine whether it is truly a feasible solution to your needs.
What Is The True Current State Of AI Technology?
Despite some fears that AI will displace human jobs, in its current state, AI is primed to be leveraged by businesses looking to enhance the work of humans. In fact, many experts argue that the technology will create new jobs and allow employees to further develop their careers due to the fact that AI automates the rote, repetitive tasks that burden employees and take them away from other important tasks. From entering routine updates to accounting systems or chatbots for customer service, AI is freeing up knowledge workers to concentrate on what they do best. A recent Accenture study demonstrates this, showing that integrating AI into workflows can boost business productivity by up to 40 percent.
What’s more, the somewhat onerous task of building and training AI tools is in some cases now being handled by other AI tools. A shortage of artificial intelligence technology experts paired with an exponential boom in demand has made this an attractive option―one that companies like Google are already leveraging with its AutoML project, H2O.ai with its Driverless AI platform, Microsoft Azure with its Machine Learning Studio, and Salesforce with Einstein.
Don’t want to hand over the development of your AI tools to other AI tools? Startups like Lobe, which was recently acquired by Microsoft, have introduced drag-and-drop interfaces for devising deep-learning algorithms, no knowledge of code required.
How To Uncover Practical AI Use Cases
Now, those are some of the higher-level ways in which some companies are utilizing AI today, but is it truly practical for businesses where proprietary technology is not a core focus? To understand whether the technology is a good fit for your organization, start by identifying specific use cases. This assessment should involve three steps, each steeped in collaboration.
We recently used this process to develop a Salesforce implementation for one of our large enterprise clients who had historically been burdened by a very complicated sales process. After talking to stakeholders, we learned that the existing system was cumbersome and it was very difficult to manage the numerous variables within the system resulting in new sales reps taking months to onboard. To solve this challenge, we turned to Salesforce’s AI tool Einstein to create a custom integration that will provide real-time, situational learning. Based on the current work the sales rep has on their screen or step they are at in the sales process, relevant help content will automatically be served up to guide them through it.
By automatically serving up relevant information to new sales reps, it will reduce onboarding time from months to a matter of a couple of days. This means new members of the sales team will be up and running faster, and ― you guessed it ― making sales earlier. All of which leads to better employee retention and cost savings.
More food for thought: When selecting pilot use cases, make sure you consider the volume of requests, insights which are hard for humans to surface, the complexity of tasks, and level of dialogue AI would be shouldering. Do you have access to a quality data set? When it comes to AI, the old adage, “Garbage in, garbage out,” applies. Your AI application will only be as accurate as the data you feed it. Additionally, if your data is siloed between different teams, it could result in data bias. And perhaps the biggest question: Is your organization really ready for AI?
Establish Goals Before Implementing AI
When it comes to artificial intelligence, part of the process of selecting winning use cases is clearly establishing what business problems you are trying to solve. For example, would leveraging AI help you:
AI might be the buzzword du jour, highlighted by uses cases that may not come to fruition for many years. But in practical terms, it’s very much in reach for companies that do their due diligence, set reasonable expectations and assess the best opportunities to bring the technology into their operational fold.
About The Author
Srikanth Balusani is Chief Technology Officer at MST Solutions, a provider in CRM/marketing automation technology. He brings more than 18 years’ experience developing and delivering innovative solutions. Srikanth is passionate about leveraging cloud platforms, AI, IoT, and conversational UI to build customer-centric solutions and improve business outcomes to help mid-market companies solve real business problems and accelerate growth. He is responsible for driving the technology strategy for MST Solutions to provide world-class solutions to customers.