4 ways AI and digital transformation enable deeper automation
Organizations that are going beyond the enterprise adoption of digitization are entering a new wave of AI-enabled digital transformation.
The first wave of digital transformation, which is still underway in some businesses, focuses on the digitalization of products, services and business processes. The second wave utilizes AI to improve the quality of decision-making, optimize organizational efficiencies and build closer relationships with customers.
While different companies are at different stages of maturity of digital transformation, many organizations already have been experimenting with AI separately to determine how it could benefit the business in the second wave of digital transformation.
One reason for the uneven results of the second wave is the understanding or lack of understanding of what AI can do. There has been a general misperception that artificial general intelligence (AGI) can solve any problem when in fact artificial narrow intelligence (ANI) is the state of the art.
"There was sort of that mad dash to do anything and everything [with AI]," said Sanjay Srivastava, chief digital officer at professional services firm Genpact. "We've tried it, experimented with it, applied it and now there is an evolution of AI in the enterprise."
Evolution of AI in digital transformation
"We've gone from an era where you've developed a digital technology and deployed it, to technologies that are continuously learning and evolving," said Beena Ammanath, executive director of the Deloitte AI Institute.
There is a shift in companies wanting to use AI to generate insights, said Anand Rao, global and U.S. artificial intelligence and U.S. data and analytics leader at PwC.
"What we really want is the insight to be embedded within our business processes, so operationalizing AI where some machine learning models are sitting next to other pieces of [enterprise software], making decisions on a minute-by-minute, day-by-day basis as opposed to being off on the side," Rao said.
AI-enabled digital transformation builds upon the first wave by providing a layer of intelligence that was lacking before.
- Augmented analytics
Traditional reporting and analytics is supplemented with what Gartner calls "augmented analytics" and defines as analytics that uses machine learning "to assist with data preparation, insight generation and insight explanation to augment how people explore data in analytics and BI platforms."
One of the goals of augmented analytics is to democratize data analytics by providing natural language querying capabilities versus requiring SQL queries. Augmented analytics also includes natural language generation capabilities which narrate data visualizations. The explanatory text helps facilitate a common understanding of what a data visualization means.
Meanwhile, there's a general concern about some AI systems' black box training methods that do not explain how the program arrived at a result or recommendation. This issue has resulted in a demand for greater transparency because regulatory compliance requires it, and because businesses are ultimately responsible for what their AI systems do.
- Automation
Automation has moved past shop floors to white-collar tasks that are rote and repetitive. Some automated systems, such as some chatbots and robotic process automation (RPA) tools, are not really "intelligent" because they're deterministically programmed, meaning that a given input produces a given output. AI expands the scope of what automated systems can do and moves companies to the second wave of transformation.
"AI is expanding the contours of automation. It is allowing us to automate things that we previously thought were not possible, allowing us to deal with edge cases," Genpact's Srivastava said.
- Consumer engagement and insight
For the last couple of decades, businesses have measured consumer engagement by analyzing website traffic logs. However, the second wave of digital transformation using AI can help optimize customer engagement by dynamically aligning the website content with the customer's preferences.
"What we're seeing on the consumer engagement insights is being able to provide a more personalized journey for the consumer, while also being sensitive to what the customer might need," Deloitte's Ammanath said.
- AI-digitized supply chains
Supply chain partners have been connecting their enterprise systems for decades, but those systems aren't designed to pinpoint where in the supply chain an item was damaged. While radio frequency identification (RFID) tag scans can track the item from the manufacturer to a distribution center, a warehouse, and ultimately the customer, what it can't do is identify the point at which the item was damaged in transit.
PwC solved this problem for a health/fitness client by mounting a sensor on bicycles which can sense where and when damage occurred using an AI that distinguishes between an impact that causes actual damage and false positives such as nondamaging impacts that result from a truck hitting a pothole or a ship sailing across a choppy sea.
Similarly, AI is improving product description technology. Deloitte's Ammanath said image recognition is being used to confirm the accuracy of products listed in a bill of materials by comparing the item to a technical drawing instead of relying on information in a database which may have been described differently by different people.
The future of connected digital transformation
The future of connected digital transformation involves more IoT and industrial IoT devices and the democratization of AI.
IoT and IIoT devices are already providing companies the visibility at the edge which they lacked before. The sensors, when coupled with AI, are helping to transform the ways businesses operate, whether it's the clinical trials of new drugs, agricultural yield optimization or hazardous mining.
Organizations with and without AI-enabled digital transformation strategies may adopt some AI by default because it has been embedded in the software applications, tools and platforms they use. To wring more value from AI in the second wave of digital transformation, data scientists should aim to solve the difficult problems while citizen developers (power users) tackle simpler problems such as optimizing business processes and tasks within their department.
"Success is when you see digital transformation and AI capabilities used not just in your core product team, but in finance, HR and legal," Deloitte's Ammanath said. "And the implementations aren't just about efficiency. There are more and more companies now looking at using these technologies to create new products and services."
