AI Readiness: Checklist of Key Questions to Ask
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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.
- 3. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 4. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 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?
- 6. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 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?
- 9. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 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?
- 11. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 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?
- 13. ENTERPRISE AI READINESS REQUIRES COMPANIES PREPARE FIVE AREAS
- 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.
Notas del editor
- 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.