Frequently Asked Questions

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AI adoption is not about swapping one software for another; it’s about reshaping how work gets done. Unlike past technology shifts, AI affects decision-making, creativity, and compliance all at once. Without a strategy, organizations risk wasted spend, low trust, and even regulatory scrutiny.

• Impacts productivity, innovation, and risk.
• Requires rethinking workflows and roles.
• Demands governance, not just tool rollout.

Policies tell employees what they can and cannot do; manifestos tell them why it matters. An AI manifesto sets the tone for responsible, inspiring adoption.

• Policies = rules and compliance guardrails.
• Manifestos = vision statements that motivate.
• Together, they balance safety and culture.

SANS Institute has a series of great AI policies.

For a walkthrough on creating your own AI Manifesto - start here.

Leaders must avoid the “shiny tool” trap by grounding adoption in strategy. Begin with clarity:

• What business outcomes do we want AI to achieve (cost savings, efficiency, customer delight)?
• Where do current processes bottleneck or cause frustration?
• How will we measure whether AI is truly adding value?
   

This framing prevents random tool purchases and sets the stage for intentional adoption.

AI governance is a system of guardrails that ensures AI use is ethical, safe, and aligned with company culture. Unlike IT governance, which focuses on infrastructure and access, AI governance must also address bias, explainability, and human accountability.

• Establishes principles for fair and safe AI use.
• Sets clear accountability for outputs.
• Evolves faster than traditional IT governance because AI evolves rapidly.

The best AI governance templates and frameworks can be found at AI Governance Library. 

“AI slop” refers to low-quality, unchecked, or poorly governed AI outputs flooding workflows. Left unmanaged, it damages trust and productivity. Employees may ignore outputs entirely, reverting to old processes.

• Leads to misinformation in decision-making.
• Erodes trust between leadership and staff.
• Creates reputational risk if content is public-facing.

Shadow AI happens when employees use tools outside company control. Banning AI outright only drives shadow use underground. The better approach is offering approved tools, education, and safe experimentation channels.

• Provide sanctioned, secure alternatives.
• Encourage employees to share use cases.
• Reward transparency over punishment.

Training must be layered and role-specific. One-size-fits-all approaches don’t stick as they don't define the use-cases. Effective programs combine baseline education with role-based application.

• Foundations: security, privacy, safe prompts.
• Role-based: tailored workflows for HR, marketing, finance, etc.
• Champions: super-users who mentor others.

If you want to clearly define AI use cases - try this prompt:

*"Act as an AI productivity strategist. Help me identify specific ways AI can improve my work. Start by asking me clarifying questions about my role, daily responsibilities, and the bottlenecks I encounter. Then suggest concrete AI use cases tailored to me, organized into categories (e.g., task automation, communication, content creation, research, data analysis, decision support). For each use case, include:

• The problem it solves
   
• The AI approach or tool type to use
   
• The expected benefit (time saved, accuracy, focus, reduced stress)
   
• Any risks or governance considerations I should keep in mind
  At the end, summarize the top three highest-impact use cases for me to start with and suggest how I could measure success over the first 30 days."*

Employees look to HR for clarity during change. If HR doesn’t anticipate their concerns, morale suffers. Core questions include:

• Will my job change, and how?
• What reskilling opportunities exist?
• How will AI affect promotions and performance reviews?
• How are we ensuring fairness in AI-driven decisions?