- Stop plugging these 7 devices into extension cords - even if they sound like a good idea
- I changed these 6 Samsung TV settings to give the picture quality an instant boost
- I tested a 9,000,000mAh battery pack from eBay that cost $10 - here's my verdict
- The 3 most Windows-like Linux distros to try because change is hard
- This 'unlimited battery' GPS tracker is an integral part of my hikes - and it's on sale
How to kick-start your generative AI strategy
How do you lose the AI race? By not entering.
So says Andrew McAfee, principal research scientist at the MIT Sloan School of Management. “When a technology this powerful comes along where you have to learn by doing, finding reasons not to do it is a pretty big error,” he says.
Despite the mass embrace of generative AI in its first year of release, most organizations remain cautious about mass adoption. Two-thirds of risk executives surveyed by Gartner consider gen AI a top emerging risk. Among their biggest concerns: exposing intellectual property through publicly available generative AI models, revealing the personal data of users to third-party vendors or service providers, and securing the AI itself from criminal hackers.
McAfee counters that such risks are manageable.
“These risks are things you have to worry about with any other large-scale database technology project—but they’re not terrifying, and you have a great deal to gain,” says McAfee. The potential benefits of generative AI are huge, and the rewards in success are worth pursuing.
To identify opportunities and determine the potential ROI for generative AI applications, McAfee advises that business leaders consider these four basic steps.
1. Inventory existing knowledge-work jobs
Generative AI is useful for almost all knowledge workers and best-suited for language-based tasks within those jobs.
“Think about the different jobs that are done in your organization and then get a rough idea about what percentage of the tasks for those jobs are amenable to generative AI,” says McAfee. “Start with the jobs where a lot of the tasks can have their productivity improved substantially.”
Read also: AI is posed to solve the tech overload problem
For instance, if what you’re creating follows a well-established template, such as a newsletter, why start from scratch? “Let AI take the first crack at it, edit it, fill in the blanks, and then let the human worker review it,” he says.
2. Consider off-the-shelf AI
After identifying roles that lend themselves to gen AI applications, consider whether the individual would benefit from having a “competent but naive gen AI assistant”—akin to a worker who excels at programming or writing but doesn’t know anything about the organization, McAfee says. This type of AI assistant can be delivered through a pre-built, off-the-shelf AI solution.
“Someone who is a new coder can start to be productive pretty easily,” says McAfee. To test software or debug errors, the coder could hand that off to a digital assistant, which could do it well and quickly.
3. Consider bespoke AI
Some knowledge-work jobs that lend themselves to gen AI require more experienced digital assistants. A customer service agent needs institutional knowledge and case-resolution expertise that only a veteran can provide.
In these instances, an off-the-shelf generative AI system isn’t enough; organizations will need to combine it with another system trained on internal data to achieve the output of the more experienced assistant, says McAfee.
Some of this data may include customer information, such as demographics and buying behavior, in order to personalize recommendations and customer support; sentiment analysis from customer feedback to proactively address concerns or capitalize on positive feedback; industry-specific knowledge, such as trends and jargon, to improve the accuracy of responses; and product or service data to provide customers with recommendations.
4. Prioritize potential projects
After identifying the roles best-suited for naive or experienced digital assistants, leaders must identify and prioritize the most promising gen AI projects, McAfee says.
“Think about where the most productivity benefit is to be found and the percentage of those tasks that are amenable to generative AI,” he says. Some 75% of the value that generative AI use cases could deliver falls across four areas, according to McKinsey research: customer operations, marketing and sales, engineering, and R&D.
“Success means having a clearer idea of where the big potential benefits are to be found,” he adds. “Maybe it’s not going after opportunity #1 because of other priorities, but they can pick and choose among those—and that clarity is helpful.”
A version of this story originally published on The Works.
