-: In this lesson, we're gonna discuss how
to build an AI Center of Excellence from the ground up,
which includes key roles, training practices,
and overall strategies to create a strong AI culture.
We'll also look at examples on how to enhance preparation,
actionable takeaways, and overall follow up on in person
and online meetings.
So by the end of this lesson,
you'll have the foundational steps on how
to create your own AI CoE
to empower AI driven decisions in your organization.
So let's talk about key components
of an AI Center of Excellence.
So this CoE is your organization's go-to team
for guiding AI initiatives from vision setting
all the way to execution.
You can think of it as a central hub that combines expertise
and governance
to drive successful AI adoption across your organization.
So let's look at the team structure
and responsibilities needed for you to build
and drive successful AI adoption.
First, the core team structure
of the CoE typically includes a mix of AI experts
and business leaders
with roles like AI slash data scientists,
machine learning engineers, and project managers.
Together they bring both technical and expertise
and a clear understanding of business needs.
And if you don't have a large team, let's say,
and you're a smaller organization, the main people
that are interested in AI in your different departments
can step in here.
So this team can be composed
of the trailblazers in your organization
and those that enjoy researching
and sharing what they've learned with AI.
Now, while the core responsibilities are setting the
company's AI vision, like defining priority, use cases,
managing processes and partnerships,
they also create governance policies
to ensure responsible AI usage
and track metrics to share success stories
of AI throughout the organization.
Overall, they should be the AI ambassadors
in your organization, from helping interested colleagues
to pushing forward company-wide initiatives.
Now let's talk about our first example.
Starting with a small team is often the most practical way
to launch your own AI CoE.
And to make it even more tangible,
let's use a hypothetical example
of building a compact AI CoE team that delivers value
with limited resources.
So a three person team, including one AI lead,
a product manager and a data engineer.
Here are three steps to start with.
First is assessing the organization's readiness for AI
and identifying which departments could immediately benefit.
Second is all about defining clear success metrics,
which focus on short term wins,
such as reducing repetitive tasks
or addressing a known OKR prioritized by leadership.
And lastly,
the final step would be establishing a pilot project
using a ready-made AI tool.
Like for example, an automated customer support chat bot
to deliver quick and measurable value.
A pilot project helps build confidence
and familiarity with AI among team members.
So by showing measurable improvements like a 20% reduction
in customer response time, for example,
the team builds trust and interest across departments.
And this potential initial success can become a model
that others can emulate in the company.
Now to maintain a high functioning AI CoE like this,
it's essential to establish structured operations
and regular rituals that promote engagement
and knowledge sharing across the organization.
And by keeping in mind these following aspects
we're about to go over, your CoE will start to take shape
and have more buy-in within your organization.
So let's look for AI champions, is what we're calling it,
across departments that are just people that can advocate
for AI in their teams, help with use cases
and support implementation efforts, like we mentioned.
So we wanna start with the AI pilot projects
and follow up with progress reports.
This quickly demonstrates AI's value,
like using predictive analytics for sales forecasting,
for example.
On top of that, you wanna develop your own governance
and really a playbook with clear data
and ethics policies to guide AI projects across departments.
This ensures members will align
with your company values in a regulatory way
and in an organizational standard of values.
You also want to set up reoccurring rituals
such as monthly AI updates and quarterly workshops.
You can use these meetings for sharing updates and successes
or failures across the different employees and departments
and have quarterly workshops on top of that
and introductory AI topics for your team members.
These sessions can help build shared understandings
across multiple teams, making it easier
for departments to collaborate.
Another great tool are hackathons and innovation days.
These often create opportunity for team members
to experiment with creative AI applications,
which can spark new ideas and lead to valuable new projects.
And in some scenarios, the ideas
that are exchanged in the sessions can turn into
profitable products or internal tools
that can massively enhance productivity.
Now let's talk about a second example
on how to increase AI adoption.
Let's explore a potential ritual for organizations to adopt.
The important aspect here is to cultivate consistency,
to first familiarize people with the change of AI
and the direction they're going.
After that you can create the habit of thinking and talking
and acting in the context of AI and its uses.
So for these meetings,
we recommend a few key aspects to be covered.
First up is showcasing exciting projects.
Always have a main project to talk about during each meeting
where the team members behind the project can share
challenges faced and insights gained.
Another important aspect is to have interactive Q&A.
This is where staff can ask questions, suggest new ideas,
and even propose new projects which can create a more
collaborative atmosphere.
In turn, this can help people feel safe
to explore these new ideas
because it's a priority of the company
to talk about 'em openly.
And most importantly, having continuous training.
By ensuring each session includes a short
relevant AI training,
this helps bridge the knowledge gap, spark curiosity,
and get rid of the fear around new technology.
So by showing that AI isn't as hard
or as complex as people may think, it's more likely
that these participants will start to be more open
to investigate it on their own.
For instance, in one session, the AI team might go over
how to use AI tools
while protecting company data.
In another, they may go over
how to automate internal workflows.
So by regularly discussing AI initiatives
and sharing knowledge, employees can feel a part
of the AI journey, making them more likely to support
and collaborate on AI projects.
Additionally, the interactive Q&A
and training sessions help create a culture of transparency
and learning,
which strengthens the overall AI adoption
inside your organization.
So with that said, by establishing a focused team
and setting clear goals for AI use in your organization,
you'll be able to create a small team that'll help build
and create a organization
that harnesses AI in a structured and sustainable way.