The Data and Measurement Issue - 15

vation
Talent
inclusiveness and become more proficient
in making data-informed talent decisions.
Clare is passionate about supporting
growth, curating a diverse and inclusive
work environment, and creating
innovative solutions to large-scale
global problems that better support the
business. She's also a skilled project
manager with change management
and cross-functional collaboration
experience. (Fun fact: Clare played a lead
and strategic role in standing up the talent and
performance practice at a multinational mass
media and entertainment conglomerate-from
the manual design of talent tools to providing
strategic analysis for the talent practice.)
Clare's work makes an impact on the
organization's culture by ensuring that talent
processes are equitable and sustainable-
using data and measurement as a tool.
Generative AI
Generative artificial intelligence (GAI) has
become ubiquitous in the technology industry.
For years, AI has been integrated into
business operations to identify patterns
and trends, while enhancing customer
experiences; tailoring content on enterprise
platforms; enhancing product design; and
navigating supply chain hurdles. GAI advances
AI architecture through its ability to learn
relationships between data elements and
then reassemble the data into new content
based on prompts. " Content " may include
text, images, code, analysis, and much more.
The power of GAI is its predictive ability to
generate new content from existing datasets.
Each new advance in GAI modeling sees a
monumental step forward-and the advances
are accelerating even amid the navigation
of ethical implications of bias and copyright,
legal considerations, and security concerns.
So, how do leaders strategically navigate
this shift in the power of data without
creating chaos in their organizations? To
do so, first consider these three areas:
1.
Understand where you are in the cycle.
This can range from " we have a small
team adept at navigating GAI " to " we've
already identified our areas of potential
impact, data pools, and role needs. "
Focus on what is practical and applicable
now to create a near-term AI integration
roadmap. In addition, consider how this
maps back to your overarching strategy
and organizational priorities. If you
are early in the process, mapping your
current data infrastructure and data
quality might be great starting points.
3.
2.
Engage
employees
authentically.
Helping employees
see what AI-enhanced
performance can look like is a critical
first step to leveraging it from a data and
measurement perspective. Be proactive
in helping people find their place in it and
to adopt a new personal narrative when
needed. Mapping skills and talent, as well
as future role impacts, can be another
space in which you can build buy-in, and
shift mindsets to see possibility while also
gaining data on current AI skill capacity.
Build trust-rich teams to explore
options together.
Developing AI agility-the capacity to
shape and reshape the organization as
it navigates uncharted territory-will be
critical. Experimental pilots, sharing success
stories, as well as lessons learned, can all
be used to build trust. However, the agility
will also need to be balanced with use case
prioritization (how will we focus our efforts)
and return on investment measurements
(how do we know what's working). Finding
ways to lift up monitoring and evaluation
data to effectively and transparently
identify potential biases, errors, and
unexpected impacts will also be a necessity
for building trustworthy GAI solutions.
When it comes to how we leverage data, GAI
is a definite game-changer. Being strategic in
building a data and measurement strategy
around its adoption in our organizations will
go a long way in turning it into a competitive
advantage for our organizations and people.
PERFORMANCE MATTERS / PG 15
The Data and Measurement Issue

Table of Contents for the Digital Edition of The Data and Measurement Issue
