Whether you’re a banker, a farmer or an Uber driver, technology is a cornerstone of your business.
Adopting technology is not about having cool stuff. The value of technology is in how it’s applied. That may involve applying technology to improve our daily lives, enhance a vehicle or other product, or rethink the way we run a business.
We must be specific. C-level business leaders such as chief information officers, chief technology officers and chief financial officers must strive to understand that. How quickly the adoption, efficiency and efficacy of these technologies work depends on the approach and blueprint that are developed.
A good analogy is within restaurants. You have specialist roles across the front of the house and the back of the house as well as a tranche of specific roles within those segments — pastry chef, grill cook, sommelier, maître d’. Emerging AI technology plays key roles in a similar way.
Open The Book On The Many Chapters Of AI
There are many chapters — and four primary motions — of AI.
The first and most visible are the systems of engagement. That includes chatbots, virtual assistants on websites, and Siri and Google Home. Systems of engagement have human-to-machine interfaces that ingest how humans interact with laptops, computers, cars and other devices.
The second is machine learning (ML), but there are many varieties of ML. Two varieties are episodic memories and semantic memories. Machine-based episodic memories drive specific situations that could allow you to reset your password on a procurement system, for example. It would recognize you as an employee, validate your identity and then autonomously set and issue you a new password. Machine-based semantic memories have broader scenario awareness. It might recognize that an employee lost their password for the procurement system seven times in the last two months. That could prompt it to explore whether the individual is still an employee, whether they changed roles or whether an algorithm is trying to break into the system.
The critical motion of AI is the actions or the “doing.” Robotic process automation (RPA) falls into this category, but so does the complex integration of autonomous vehicles. There is a spectrum of business tasks that have been converted into automated and autonomous sequences. Some require the human in the loop; others are becoming stand-alone.
The last — and most complex and emerging — chapter of AI is reasoning. Here, we humanize the software or machine and use it to interpret things for us. As the Wall Street Journal wrote: “AI is already making existing prediction methods more efficient and contributing to increases in the speed and accuracy of forecasting, and it shows promise for tracking the paths of severe weather like tornadoes and hail with greater precision.”
AI Adoption Will Require A Lifestyle Change
Don’t think of AI as an add-on widget. AI is not something that you can just chuck into your full backpack. As you move forward with your AI effort, be ready for a lifestyle change and a strong set of methods around your data and content intelligence.
AI adoption will look and feel different operationally and involve new ways of working. Map out those new ways of working. Ensure your skills inventory includes human and digital workers. Digital workers, in most cases, will complement the human resource. In other cases, they will operate in a stand-alone capacity. Create a clear fabric of where skills are and what they do.
Understand that acceptance and adoption of AI will take some time. It took people a little bit of time to get used to talking to Siri. Now, it’s second nature.
Understand The Opportunity And Workflow — And The Economic Model That Sits Behind Them
AI in the form of RPA, for example, can enable efficiencies by reducing the human labor and costs involved in doing things. However, keep in mind that AI also can contribute to efficacy.
If you’re a service provider, it’s cheaper to run a help desk with a digital bot. However, it’s still a revenue unit, and that revenue unit has enterprise value. You may think that you have to give it away for free, but just because it’s a different way of working doesn’t make it less valuable.
Elements of AI-based automation are also emerging in the front office. They’re not just about cost savings; they’re about user experience. Consider the “cost savings vs. value” system around knowing your bank customers. If a customer calls the bank, AI can bring forward data and content analytics so the human agent can understand that this is a multichannel customer with a mortgage, checking and savings accounts, and credit cards with that bank. It can recognize that this customer just completed their first mortgage and realize that they may be buying another house. As a result, the bank can recommend appropriate additional financial products.
Establish A Road Map Of Where You Want To Go — And Find Partners That Will Get You There
Many organizations were early adopters and co-developed the maturity of these technologies as well as robust platform-based services. Some are just learning what is affordable and less complex to drive foundational changes. Regardless of where you are, be sure that you have a road map to navigate your AI journey. Everybody should have a crisp articulation of where they are today in their AI adoption and where they’ll be tomorrow.
Seek partners to help you on this journey. Look for partners that are adopting AI themselves. It’s a red flag when businesses aren’t adopting the things they are selling to clients.
In the AI maturity curve, we’re emerging from the valley and finding creative use cases for AI technologies. As Gartner, Inc. notes (via TechRepublic): “Companies are looking to operationalize AI platforms to enable reusability, scalability and governance and speed up AI adoption and growth.”
To continue the momentum of AI — and, more importantly, your own business momentum — remember that AI is not generic. Find well-defined use cases in which to apply AI technologies.
You can’t use a broad brushstroke.
This article first appeared in Forbes.