What exactly is technical debt? When discussing your organization’s technical debt - and possible changes to it - with various audiences, you need to articulate the key issues in plain terms. Here’s expert advice on how to do that.
How to identify an AI opportunity: 5 questions to ask
Could AI solve that problem? Speed that process? Five important things you should ask to unearth AI opportunities in your organization
Any IT leader with a pulse already understands that AI will have an increasing impact on their business in 2019, as well as in their broader industry. The same goes for the related disciplines that tend to get lumped in under the AI umbrella, such as machine learning and robotic process automation (RPA).
But that’s not really an actionable insight. It says “be prepared,” but it doesn’t weigh in much on where to get started. And where to get started with AI is the most pressing question today, especially given that what constitutes the best applications of AI (a term we’ll use in blanket fashion here) will necessarily be organization-specific, or at least industry-specific.
[ Which AI project should you tackle first? Read our related article: Artificial intelligence: Examples of how to start successfully. ]
We asked a group of AI experts for just that: Actionable insights. How can IT and business leaders think clearly about where AI might be a good fit? That question produced, well, more questions, but productive ones.
Here are five important things you should be asking to help unearth viable, results-oriented AI opportunities in your organization.
1. Where can we make better decisions?
One of the fundamental opportunities for AI or AI-augmented solutions lies in any process or area where your organization can improve its decision-making. In particular, where would your organization benefit from moving to a more data-driven model for making business decisions rather than relying entirely on human instinct and input? (You’ll need to determine criteria for measuring this kind of improvement.)
“The goal should be to ask: ‘Can better decisions be made?’” says Michael McCourt, research engineer at SigOpt. “When a company has people with authority willing to make changes and revisit assumptions using data-driven AI models, then there is an opportunity for a successful AI project.”
McCourt notes that this can cause conflict in organizations that don’t already have a data-oriented culture, especially if people perceive this line of thinking as a threat to their job. If that’s the case, you’ll need the right champions and executive involvement.
Note that your organization can and should determine for itself what makes a decision “better.”
“IT leaders and their business counterparts can break down the business into data and the decisions overlaid across that data,” says John Sprunger, senior architect at West Monroe Partners. “They can determine where automating those decisions or improving the outcome of those decisions will provide value or competitive advantage.”
2. Where are we most inefficient?
Amy Hodler, AI and graph analytics program manager at Neo4j, says that the best first step for uncovering AI opportunities is to look for areas where things aren’t running optimally today.
“Identify which of your processes have measurable inefficiencies,” Hodler advises.
A mostly universal example would be to look for critical processes or tasks in your business that rely heavily (or entirely) on manual data entry.
“If companies are still relying on manual data entry for critical functions, they are putting themselves on a fast track to becoming a laggard, or worse yet – extinct,” says People.ai CEO Oleg Rogynskyy. “Having high levels of manual data entry leads to heightened error rates, slow turnaround time, and required quality checks – these are just a few of the antiquated byproducts that AI solutions can solve.”
Inefficiency isn’t just marked by time-consuming tasks or bottlenecks; it can just as well be measured by the repetitive practice of simple tasks.
“It’s critical to keep an eye out for areas of your organization that generate data and need decisions that require a human to deliberate for a second or less,” says Bill Brock, VP of engineering at Very. “If a person can do a mental task with less than one second of thought, these tasks are ripe for automation.”
Sprunger from West Monroe Partners advises IT leaders to look closely at repeatable, low-complexity, and data-driven human-computer interactions (such as manual data entry) and customer service interactions as possible AI opportunities.
“These are prime use cases for offloading to AI technologies like RPA or AI chatbots,” Sprunger says.
3. Where do we have a lot of relevant data?
AI depends upon the information you feed it. While that could ultimately come from all manner of sources, the early phases of identifying promising AI opportunities will likely be better served by considering areas in which you have robust, reliable, and accessible data.
Data is step two in Hodler’s holy trinity for uncovering good AI opportunities.
“Determine which processes you have the most information on, prioritizing those where you have the most relevant information about the elements and relationships involved,” Hodler advises.
That “relevant information” Hodler mentions is of fundamental importance. Without it, you might be pursuing problems rather than results.
“Make sure you have access to data,” says Tom Wilde, CEO at Indico Data Solutions. “Labeled data that captures the desired outcome is the single most important ingredient for success. Ideally, this data is present inside your enterprise because that type of data is much more digestible than trying to scrape data from across the Internet.”
Those are “boil the ocean” type of projects, and Wilde says they are doomed to fail in most companies.
[ Want to learn more about AI's pressing questions? Read 10 TED Talks on AI and machine learning. ]