When is AI a time and money waster rather than a saver, and how do you make sure the outcome is a net positive for the business?
There are three broad categories of AI deployment, and the opportunities and the pitfalls are different in each.
Public-facing
Chatbots, sales and marketing content generation, intake of external data.
Saver: the cost per contact of most communication can be automated or drastically reduced – gathering initial information for customer service and first-line support, more finely targeted outreach to prospects, straightforward administrative tasks.
Waster: many people actively dislike dealing with automated systems. A poorly designed or badly implemented one can cost far more than it saves, through lost sales and through the clean-up afterwards.
Private-facing
Internal reporting, operational data flow, legacy data mining and clean-up, systems migration.
Saver: improved efficiency and accuracy, and genuinely valuable business analytics.
Waster: the cost of the data clean-up needed to get quality outcomes, misapplied resources, incorrect outputs, and staff resentment or confusion.
Ad-hoc
Research, one-off activities, everything else.
Saver: research, custom reporting, one-off content and experiments that used to take hours or days of staff time can now take minutes.
Waster: the cost of training staff, incorrect outcomes, and over-reliance.
On that last point, a real example: we are aware of a case where an administrator spent many hours across three weeks trying, and failing, to get ChatGPT to produce an office layout plan – a task that could have been done in under an hour with a pencil and graph paper.
Itwerx Corp is a service-disabled veteran-owned small business providing IT services across Seattle, Bellevue, Everett and Snohomish County. This is the kind of thing our business process analysis work deals with – talk to us about yours.
