The term “digital transformation” has become so ubiquitous that it can mean almost any change from a manual process to an electronic one. But why do we have to think of change in terms of digital transformation? Few would argue that replacing an inefficient manual task with automation is a “transformation.” However, I think of change in terms of innovation, in terms of altering how we do something or the way we behave—in terms of disrupting an ecosystem. Innovation isn’t just automating processes that already exist, but rather applying technology to solve a problem in a different way.
In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.
Using AI/ML for innovating the customer experience
One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.
Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.
AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.
But digital innovation shouldn’t stop with customer experience
There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.
No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.
- Motivating a remote workforce – With so many teams working remotely, first-hand observation of employee engagement is next to impossible today. AI can analyze which applications employees use most, possibly even judging their levels of efficiency or frustration. Organizations can understand how happy, motivated, and engaged teams are so they can maintain or increase efficiency and productivity.
- Refining a business model and marketing – Beyond mere numbers, AI can analyze which products in your online portfolio work best and for which shoppers. Yes, this can help you shape the online customer experience. But it also lets you adapt which products you choose to keep or eliminate from your lineup (your business model) and adapt your offers based on observed customers’ choices or preferences (your marketing strategy).
- Protecting intellectual property – Organizations can even protect their patents, intellectual property, and product uniqueness by using AI, ML rules, and image recognition to smartly crawl the web to identify look-alike products and would-be theft.
The possibilities for internal process improvement across the enterprise are endless.
AI/ML isn’t just for large technology companies
In short, companies should apply AI/ML innovation to their operational processes as much as they do to the customer experience. Artificial intelligence isn’t just for technology companies, nor is it for analyzing and solving only technology problems. Organizations can use it to better understand their customers. They can use it to automate inefficient internal processes. They can leverage it for improving online security, boosting employee engagement, and reducing theft and risk.
Why aren’t more companies using AI? Frankly, they do not know how to start. They know they need to use it, but they don’t know where to “plug it in” to their systems first. Or they think they have to hire a team of AI engineers and build their solution from scratch.
Today AI is available for any company to use and benefit from, even smaller companies, without the need for a team of experts. There are many commercial apps and solutions in the marketplace that readily adapt to an organization’s existing processes. Some are even SaaS- and cloud-based solutions, meaning they do not require a big infrastructure investment to get started. The important thing to know is that any company can start small and scale up their AI solution in their own time — but getting started is the only way to stay competitive.
TB2020 Enterprise Tech