Is your organization prepared for AI deployment? Most companies prepare only data, but our research finds enterprise preparedness for AI must span 5 areas: Strategy, People, Ethics, Infrastructure, and Data. This deck is a sample of questions to ask in each area, sourced from our research report: AI Readiness: Five Areas Businesses Must Prepare for Success in Artificial Intelligence accessible here: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
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AI Readiness: Checklist of Key Questions to Ask
1. Jessica Groopman, Industry Analyst and Founding Partner
Q1, 2019
Key Questions Business Leaders Must Ask & Answer
AI READINESS CHECKLIST (Sample)
2. How to use this document
• The following are readiness questions critical to ask and answer when
designing AI programs in your organization
• They were sourced during the research and interview process conducted
by Kaleido Insights in the development of the report AI Readiness: Five
Areas Businesses Must Prepare for Success in AI
• These questions are applicable in virtually all deployment scenarios
(across industries, functions, processes, etc), but represent a partial list.
Download the full report and/or reach out for deeper support and advisory.
5. Strategy: Key Readiness Questions
How does our internal culture facilitate and enable innovation?
How will AI initiatives support existing digital transformation
efforts and data strategy?
Where will design, development, and management for AI
initiatives sit in the organization?
How will organizational structures support governance and
scale of AI deployments and collaboration?
How will efforts be driven from the ground-up but empowered
top-down?
What are adjacent use cases to which we can apply learnings,
metrics, or similar techniques?
7. Ethics: Key Readiness Questions
What is the organizational culture and resource allocation given
to ethical assessments, design, training, and communications
today?
How is the organization prepared to analyze bias and mitigate
associated risks?
How is the organization prepared to mitigate risks associated
with ethical breaches, compliance, as well as legally
unprecedented areas?
How is the organization managing opacity in the explainability of
AI-driven models?
8. KEY QUESTIONS ORGANIZATIONS SHOULD
EMPOWER EMPLOYEES TO ASK:
• Is this a system that should be built?
• What assumptions are we building into this system or product?
• What biases are at play in the data used to train the model?
• How could the system be used in unintended, exploitative, or nefarious ways?
• Should this engine be designed specifically for humans in the loop (e.g. providing
context) or designed to remove humans by automating actions and decisions?
• What are the impacts (business, customer, financial, legal) of false negatives or false
positives? Who benefits, who suffers?
• How will we ensure equality in designs and outcomes?
• How will we ensure documentation and transparency of inputs, tweaks, changes?
• How might we be held liable in the event of unintended consequence?
10. Infrastructure: Key Readiness Questions
What is the readiness of current data, compute, storage, and
networking infrastructure?
What resources are available internally or in the cloud to develop
and host AI software applications?
At what point in the data value chain does the organization sit and
how should that inform short-term vs. long-term infrastructure
investments?
How prepared is the organization to capitalize on emerging
advancements in AI hardware and data storage innovations?
How will the organization introduce new AI capabilities without
further exasperating IT and data inefficiencies?
12. Data: Key Readiness Questions
What is the level of awareness we have in data inventory, pipeline, and
integration?
What is the readiness of the data (sources, cleanliness, ownership,
compliance, etc.) needed to train and deploy the organization’s AI
initiatives?
What kind of data governance is tied to the management, security, and
documentation of data, AI models, metrics, and changes made?
What resources are in place to manage inputs and outputs across multiple
AI systems within the organization?
What resources are in place to enable ongoing learning and enterprise
knowledge optimization over time?
14. People: Key Readiness Questions
Who is leading, and who can best contribute to, AI program
development?
How will the organization facilitate readiness of executives, leaders,
and partners?
How will we facilitate readiness of employees and end users that
will come into contact with AI-driven outputs?
How can subject matter experts (SMEs) best support design,
testing, roll-out, and evangelism?
How will the organization support upskilling, learning, and sharing
over time?
15. Learn more about this research
Report Purchase Includes
o 54-page Research Report, sourced from more than 27
research interviews of companies that have deployed AI
o Includes real-world examples, pragmatic recommendations
and best practices, frameworks to activate, and endnote
resources
o 11 high-resolution graphics and frameworks visualizing
research findings
o Two (2) Downloads per purchase
o One (1) Complimentary call with Lead Analyst for a report
overview discussion
16. THANK YOU! Jessica Groopman
Industry Analyst and Founding Partner
Automation & Data Strategy
JESSICA@KALEIDOINSIGHTS.COM
@JESSGROOPMAN
About Kaleido Insights: Kaleido Insights is a research and advisory firm focused on the impacts of disruptive technologies on
humans, organizations, and ecosystems. Our industry analysts provide business leaders with clarity amidst a fragmented technology
landscape. Kaleido advisory relationships, webinars, speeches, and workshops are grounded in research rigor, impact analysis, and
decades of combined expertise. Innovators are realizing that implementing each new technology isn’t enough, especially as business
models are disrupted. Keeping up is becoming more difficult. Our mission is to enable organizations to decipher foresee, and act on
technological disruption with agility, based on our rigorous original research, trends analysis, events, and pragmatic
recommendations.
If you’re interested in building a relationship with our analysts, we’d love to hear from you. Please email info@kaleidoinsights.com to
start a conversation, or visit www.kaleidoinsights.com to learn more about our offerings.
Notes de l'éditeur
Training Session: AI Readiness Training
Enterprise interest in AI has been driven by its promises for efficiency in the form of speed, accuracy, agility, and access to insights. To capture this opportunity, most organizations today are preparing for AI through extensive data cleansing. Data may be the requisite for AI, but investing in AI is about investing in people. Our research finds true enterprise readiness for AI goes well beyond data, and requires preparation of people, strategy, ethics, infrastructure, and governance. Join Founding Partner and Industry Analyst, Jessica Groopman as she shares findings and best practices for enterprise AI readiness, accessible to any employee.
One-Hour session, attendees will learn:
State of enterprise AI
How to define and apply AI to business or marketing strategies
How to prepare people for automation
How to think about data and infrastructure for organizational fluidity
Key takeaways for AI success-- both for employees and the organization
What insights do you wish you had?
What do you spend time searching for?
Where is the greatest tedium?
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
What is the level of awareness we have in data inventory, pipeline, and integration?
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
Achieving the benefits of AI requires companies deeply understand more than its utility; it requires preparedness across ve critical areas.
To make AI work for the business, you have to put it the path of existing work. Initiatives that try to take a bolted-on, very technical, or altogether new approach will face greater challenges and steeper barriers to adoption. First, people are generally resistant to changing behaviors, particularly if they are measured by their productivity (e.g. sales, cases resolved, etc.). Second, when asking employees to give more control to machines, it’s important not to do so abruptly, but to offer an onramp.
“This requires collaborative roll-out,” says Clayton Clouse, data science lead at FedEx, “the program has to be deployed in such a way that doesn’t just invite feedback, but implements it in a manner that shows people how and why this way is better. That means it has to actually be better!”
The art here is aligning AI workflows with existing workflows and behaviors to drive trust and adoption.