We had an active conversation at last week’s
Coffee with Digital Trailblazers
on navigating the risks when experimenting with generative AIs and large
language models (LLMS). My panel included Joanne Friedman, Joe Puglisi,
Heather May, Tyler James Johnson, Ashish Parulekar, Roman Dumiak, and Gary
Berman. They shared where gen AI provides value and what risks Digital
Trailblazers must address when experimenting with them in their
We had already covered the impact of LLMs in every industry during a
previous Coffee Hour, and I’ve been writing about the Gen AI opportunities
for CIOs and their businesses. See my articles on
five critical priorities for CIOs to lead on generative AI
how generative AI impacts your digital transformation priorities. If you want here-and-now ideas, see
what ChatGPT and LLMs can do for your business
five AI search capabilities people will expect because of ChatGPT.
With every promising and transformational new technology and AI capability
comes new risks, so it was time for us to discuss them at the coffee hour.
Here’s the list we reviewed:
1. Elevate data governance: Now more important than ever
Data quality, categorization, mastering, and security were always important,
but it’s been an uphill battle for many data governance leaders and CDOs to
get executives’ priority and investment. Generative AI increases the risks
by an order of magnitude in the following ways:
Where is your data going, and is critical IP leaking from your
organization? Employees can easily cut/paste product information, code,
and other trade secrets when prompting a gen AI tool.
Are you reviewing how SaaS uses your data to train their LLMs,
whether they are using your data to provide the functionality to you, or
they’re anonymizing the data for their generic models?
Do you have data quality practices against your unstructured data
so that you can experiment with building a private LLM or participating in
an industry-specific LLM, or using a small LLM?
Advice to Digital Trailblazers: If your leaders are excited by Gen
AI’s promises, take advantage of the moment and bring data governance to the
forefront of priorities.
2. Define the guardrails: How should employees experiment with Gen AI
Remember when IT was backpedaling through issues created by rogue and shadow
IT? Some CIOs and IT leaders still struggle with this issue (if you are,
let me know), and today, we have a new issue of rogue and shadow AI.
Without guardrails, employees can pick their problem, use any Gen AI tool
they can access, and use any accessible IP when prompting (see data
We discussed several issues where setting some guardrails and guidelines
How are people using their time, and are their principles or
guidelines to help employees know what types of work are appropriate for
trying a Gen AI tool?
What Gen AI tools should employees use, and how can they request a
review of new tools that aren’t on the list? Where can IT create sandboxes
to test new tools without exposing data to open LLMs?
How should employees validate an AI’s results, especially when some
AIs like ChatGPT train on older data, and most Gen AIs disclose the
possibility of sharing false information when prompting an AI for facts?
Where can employees apply a Gen AI’s results, and what legalities
need review? Are there copyright and trademark issues to consider based on
the AI’s training data, and does it reference licensed material (such as
GPL-licensed code) in its results?
Are there regulatory and safety considerations that employees
should understand before prompting a Gen AI? This is particularly
important in enterprises with multiple business units where some units
have greater compliance and human safety considerations.
How will you validate your private LLMs for queries you can’t
easily anticipate? How might a bad actor prompt your LLM to extract and
use information in detrimental ways?
Advice to Digital Trailblazers: We want employees to experiment with
defined practices and controls. Digital Trailblazers should communicate the
guardrails and identify ways to monitor and enforce them. Consider
developing an experiment database for employees to log their experiments,
share findings, ask questions, and document unexpected issues.
3. Communicate expectations: What’s shared with board directors, leaders,
Gen AI generates many emotions, from those wanting to chase shiny objects to
fears that armageddon is nearing. We have young people in the organization
stressing about their careers and more experienced employees, fearing their
skills becoming obsolete faster. Leaders have visions and goals where Gen AI
can provide short- and long-term business benefits, but the risks need
continuous review as Gen AI rapidly evolves.
So, what are leaders communicating about Gen AI to customers, partners,
board directors, leaders, and employees to set realistic expectations, quell
fears, and share risk considerations? Below are several considerations:
What are you saying to the board about how the enterprise will
transform and leverage gen AI capabilities? Which opportunities will the
leadership team pursue around efficiencies, product evolutions, employee
experiences, and new business opportunities? How will the organization use
and protect its IP?
Is the leadership team collaborating and defining a strategy,
setting objectives, and outlining priorities around Gen AI? Are they
aligned on the risks and taking proactive mitigation steps?
What are you saying to customers about any Gen AI capabilities you
plan to offer and how you’re using and protecting their data?
What are you telling employees about the organization’s
opportunities using Gen AI, the data risks, and the guardrails? Are you
listening to employees’ concerns and providing career counseling and
pathing for people whose roles and skills may be impacted by Gen AI?
What are the learning opportunities for employees who want to learn
AI tools and participate in other Gen AI and LLM initiatives?
Advice to Digital Trailblazers: Communications should be the center
of all innovation and transformation programs, especially when there are
business risks, inflated expectations, and anxieties that need addressing.
If you’re working with Gen AI, consider your responsibilities for keeping
people informed and engaged.
One final and critical risk
There’s a significant risk for organizations that ignore generative AI
opportunities, especially when competitors take big bets or make bold moves.
Consider what happens in healthcare when LLMs are applied to patient data or
in financial services when LLMs aid portfolio analysis; it’s too easy for
laggards to fall behind and face disruption.
Join us for a future session of
Coffee with Digital Trailblazers, where we discuss topics for aspiring transformation leaders. If you enjoy
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