- Okay, so the second false narrative, AI is magic.
The one thing I found most fascinating
about the last few years is our collective willingness,
even urge to believe that magic is real.
It's not.
Sorry, Harry Potter.
But this belief says a lot about the human psychology
and our general frustration
with the current state of technology.
The number of disappointing stories
about AI not working or not meeting expectations
could fill a course five times this long.
Organizations have fallen prey
to an expectation that complex algorithm development
is quick and easy, that data doesn't need to be clean
to be useful, that their systems are in a state
that can support such projects,
and in some specific cases, that the vendors
of the core systems they depend on
will permit access to data for the sake of AI projects.
Dreamers, be bold but beware.
This next wave of digital transformation will be
massively impactful, but it will not come quick or easy.
Nothing worth doing ever does.
Complex algorithm-driven technologies
are going to change the game on many levels,
but if it sounds too good to be true, it is.
Use your gut and the guts of those hardened operations folks
in your organization as your gauge.
Chances are good you have battle-tested practitioners
and operational types in your organization.
If a solution suggests that what they're doing
and what they've worked so hard to achieve
over the last few decades is
now easily emulated with software,
be wary and test, test, test these assertions.
Don't get me wrong, embark on AI projects
and do so aggressively, but keep
the following tenants in mind.
One start with the data.
Always start with the data.
And consider three key points here.
First, hunt for and digitize any source
of unstructured data, be it emails, scans, paperwork.
If you can digitize it, do it.
Second, clean it.
Garbage in still creates garbage out.
That hasn't changed.
Third, curate it, whether you keep your data
in bases, lakes, or buckets, the importance
of wise administration is more critical than ever.
Two, understand the charted destination.
Know what it is you're hoping to accomplish.
Too many projects today are science projects
with no purpose other than to play with cool tech.
Unless your budget can support this,
keep the mission parameters tight.
Three, plan accordingly, these projects are not short.
Very little is out-of-the-box when it come to AI.
Like you train employees, you train AI.
This requires preparation, that reliable data
I keep mentioning, and technical expertise.
Make sure you budget for the time and infrastructure.
Four, don't confuse yourself with buzzwords.
The new wave of work has caused us
to get loose with terminology.
Don't let exciting new words get in the way
of honest discussions about technology.
Beware of anything called smart or intelligent.
And for that matter, challenge every new vendor
that says they are AI-powered.
Five, consider the audit trail.
I've personally seen algorithms in machine-learning pilots
that come to right answer every time,
and yet the projects were shut down
because there was no clear way to determine
how the algorithm did so.
Be conscious of this, and remember the audit trail
is everything in certain highly-regulated industries.
Six, appreciate the talent required.
AI requires data science, math, coding,
and complex analytical skills
that you may not have on your teams today.
So be certain you have a fair assessment
of your existing capabilities before you embark.
And finally, seven, govern appropriately.
Like all significant software projects,
working with artificial intelligence demands
a rigor and control over data cleansing,
data security, access rights, IP protection,
and often regulatory compliance.
Be sure all such controls are in place
before you let teams explore how AI
might apply to your critical business functions.