Having recently re-watched the “Iron Man” movie franchise, I was
fascinated again with J.A.R.V.I.S., Tony Stark’s artificially intelligent
computer. Imagine the vast amounts of information J.A.R.V.I.S. needs access to
in order to fulfill a request or anticipate a need!
What if this artificial intelligence technology was
deployed within the enterprise today? Many organizations have indeed begun to
deploy AI-enabling technologies as part of their digital transformation
initiatives, with information capture and process automation being the key
drivers.
However, the AI hype has CIOs thinking beyond mere process efficiency.
What if you could transform customer-provided content, documents, and
communication into actionable context, relevance, relationships, and data?
Think about how much smarter and more effective your “robots” and “robotic
processes” would be if they had more information in order to make effective
decisions that improve outcomes, customer service, and decisions.
Robotic process automation (RPA) is a catalyst for AI and is fueled
by data ingested by intelligent capture and machine-learning technology.
Organizations are ripe with data waiting for intelligence. The top areas CIOs
and line-of-business leaders should prioritize for AI-enabling technology are
processes that offer more personalized and faster customer experiences,
processes that could be more efficient, and processes that require a
swift control and understanding of vast amounts of data to make smarter decisions
or meet compliance.
Onboarding
An important use case where digital transformation is uprooting old
processes is onboarding, including the onboarding of new employees and
customers in insurance, healthcare, and finance. For example, when opening a
new bank account and applying for credit cards or mortgage loans, the applicant
is asked to provide a number of documents, such as an ID, employment details,
and proof of address. With the help of intelligent capture and classification,
this can be done with a smartphone, and extracted data can be processed by
the bank’s own systems. This saves time and money for both the bank and the
applicant.
Once the customer is enrolled, other systems using machine-learning
technology can track and learn from this customer’s behavior to let a company
offer tailored services based on the customer's credit history, lifestyle,
and health. This involves pulling publicly available information and
cross-referencing it with the products and services the company can offer.
An insurance company, for example, would be able to make smarter decisions
knowing an applicant was involved in extreme sports by cross-referencing social
sites.
Accounts payable and invoice processing
Automating invoices and applying RPA offers more intelligence to
accounts payable. Not only does AI learn an accounting staff’s manual
processes, but it also gives staff more control over the approval cycle. AI can
also help staff make better decisions with the use of graphical
dashboards that give real-time status of staff productivity, invoices, and the
source of exceptions, key performance indicators, and more.
Digital workforce
Another area where AI capabilities are changing the corporate landscape
is in hiring a “digital workforce” by automating repetitive tasks or processes
with RPA. Says Forrester Research in its report, Predictions 2018: Automation Alters
The Global Workforce: “As enterprises become more acclimated
with automation, RPA will take over low-value repetitive tasks and rote
work. In 2018, RPA-based digital workers (i.e., bots) will replace and/or
augment 311,000 office and administrative positions and 260,000 sales and related
jobs to deliver enhanced customer experiences. Digital transformation spending
will increasingly emphasize automation, and operating models will be
re-engineered around it.”
By automating workflows and learning from human intervention on-the-go,
RPA allows knowledge workers to focus on addressing exceptions or solving
problems.
Compliance and risk
Lastly, organizations should deploy AI in applications where a system
can learn to understand, find, extract, and provide insights from unstructured
documents, such as commercial agreements and contracts. This is a topical area
in light of data protection rules where enterprises need to process vast
amounts of documents to control and mitigate compliance risk.
