Resources
Policies, References & Assessments
Our collection of reference material continues to grow as AI and the way it is received evolves.
Policies, References & Assessments
What are some sample questions to start with?
Conversation starters
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Design + Development
How do I talk to clients about using AI for content creation?
Which tools can I use for design?
How do I use Midjourney?
What are best practices for content creation with AI?
What are some popular tools I can use to create video content?
How do I compete with AI as a content creator?
[How] Do I disclose AI use for content?
Leadership
What specific business problems will AI solve?
How do I choose AI tools that align with my risk appetite and business goals?
How do I ensure my AI tools align with company policies and values?
How do I protect my people, my business, and my data?
How do I lead in an AI environment?
How can I create stakeholder engagement?
Oversight
Who should be responsible for AI oversight?
What governance structures should I put in place before using AI?
What safeguards should I have to prevent harmful or biased decisions?
How do I create accountability?
How do I ensure transparency and trust in AI-driven processes?
Who should approve AI projects?
Policies and Guidelines
What needs to be in my data governance policy?
What AI uses should be prohibited?
Do I need a workforce displacement policy?
How should I update my employee handbook policies for AI?
What best practices should I implement?
Risk Management and Compliance
How do I ensure my AI vendors are meeting security and ethical standards?
Does insurance cover AI risks?
Am I responsible for AI errors?
How do I ensure my AI tools align with data privacy laws (e.g., GDPR, CCPA)?
What are the risks of using AI in client-facing services?
How do I set AI policies that balance innovation with risk controls?
Training and Awareness
What is the different between AI and automation?
How do I help employees understand the limitations and risks of AI?
How do I train employees to recognize AI bias?
How should employees report AI errors?
What doe employees need to know about data privacy and security?
How do I help employees understand the impact of AI on our business?
Implementation and Optimization
What AI tools integrate with my current software?
Can AI replace employees?
How do I set up an AI testing environment?
What tools can I use to automate data labeling?
How do I evaluate data readiness?
How do I know my team/ organization is ready for an AI pilot or rollout?
How should I look at AI Agents in the workforce?
Stakeholder Engagement
How should I involve stakeholders early in AI planning?
What strategies will help gain stakeholder trust and encourage AI adoption?
Who are the key internal stakeholders affected by AI adoption?
How do we measure stakeholder satisfaction?
Do I need to involve my Board in AI decision-making?
Technology and Supply Chain
[How] Should we secure our supply chain with AI being added to software and apps?
What should I worry about?
What are the basics of data readiness for AI?
What are some options for data governance? From a tech perspective?
Competitive Opportunities
How can AI help me personalize services for different clients?
Can AI help with pricing strategies?
What AI tools improve proposal writing and RFP responses?
How to use AI for competitive analysis?
How can I build trust with clients when using AI-driven insights?
Change Management
What are best practices for change management related to the adoption of AI?
What is our organization’s AI maturity level?
Who will champion AI adoption?
How will AI change roles and workflows
What concerns might employees, customers or partners have about AI?
Awareness and General Understanding
What is AI and how does it work?
Is AI safe to use?
How can I use AI in my business?
Is AI stealing my data?
How do I know if my software already has AI?
What should I NOT put in AI? (Link to the giveaway?)
What is responsible AI (rAI)?
Why is rAI important?
What are rAI principles?
Applied Understanding
How are businesses currently using AI?
What AI skills do I need to stay competitive?
How do I use Midjourney?
Which LLM is best for a task?
Is it too early to spend on AI?
Risks and Limitations
Is AI safe to use in my business?
What are the risks of relying on AI?
How do I know if AI-generated content is accurate?
What does AI governance mean?
What does data governance mean?
What does it mean to have a “human in the loop”?
Research
How do I get started with AI in my business?
What kind of pilot projects are people using for AI?
What are some popular AI tools I can use?
I need help understanding a vendor’s terms of service.
Do I need a big budget to use AI?
Do I need an in-house AI expert, or can I rely on third-party solutions?
How do I evaluate AI providers?
Preparation
What policies and guidelines do I need?
What’s the best way to introduce AI to my team and clients?
What safeguards should I have in place?
How do I inventory and clean up my data?
Who should own AI in my company?
How do I kick off an AI project?
AI Tools + Software
What AI tools can help with social media?
I need help comparing vendors.
How can AI streamline my email and sales outreach efforts?
Are there AI tools that help with contract review and legal work?
How do I choose the right AI software without overcomplicating my workflow?
What’s the best way to evaluate AI vendors for security and compliance?
What are the tools we use?
Tools We Use
From great ideas and powerful AI to wonderful people, these are the software and systems we use to make the magic happen. Maybe they can be useful for you.
Digital Assistance Glossary
Explore a concise collection of essential terms that define our modern support framework. Quick definitions empower you to navigate our technology with ease.
Digital Assistance Glossary
| Term | Definition | Example |
|---|---|---|
| Algorithm | A set of rules or instructions given to an AI system to help it learn and make decisions. | Netflix suggests shows based on your viewing history. |
| Artificial Intelligence (AI) | The simulation of human intelligence by machines, allowing them to perform tasks like reasoning, learning, and problem-solving. | Virtual assistants like Siri and Alexa use AI to answer questions. |
| Automation | Using technology to perform tasks without human intervention. | AI chatbots automating customer service responses. |
| Bias (in AI) | When an AI system produces results that are systematically prejudiced due to incorrect data or flawed assumptions. | Facial recognition systems performing poorly on certain demographics due to biased training data. |
| Chatbot | A computer program designed to simulate conversation with human users. | ChatGPT answering your questions. |
| Computer Vision | An AI field that enables computers to interpret and understand visual data. | Self-driving cars detecting pedestrians and traffic signals. |
| Data Mining | The process of discovering patterns and relationships in large data sets. | Retailers analyzing purchase history to recommend products. |
| Deep Learning | A type of machine learning involving neural networks with many layers, allowing AI to process complex data. | AI systems that power voice recognition like Google Assistant. |
| Generative AI | AI systems that create new content, such as text, images, or music, based on training data. | ChatGPT generating responses or DALL-E creating images from prompts. |
| Large Language Model (LLM) | AI models trained on vast amounts of text data to understand and generate human-like language. | ChatGPT and GPT-4 are LLMs. |
| Machine Learning (ML) | A subset of AI where systems learn from data to make predictions or decisions without being explicitly programmed. | Email filters identifying spam messages. |
| Model Training | The process of teaching an AI system to make predictions by feeding it data. | Training an AI to detect fraudulent transactions based on past data. |
| Natural Language Processing (NLP) | AI that helps computers understand, interpret, and respond to human language. | Language translation apps and virtual assistants. |
| Neural Network | AI systems inspired by the human brain, consisting of layers of nodes (neurons) that process data. | Image recognition tools like Google Lens. |
| Overfitting | When an AI model learns the training data too well, performing poorly on new data. | An AI trained only on sunny-day driving data struggling in rainy conditions. |
| Predictive Analytics | Using AI to analyze data and make predictions about future events. | E-commerce sites predicting which products customers might buy next. |
| Prompt Engineering | Crafting inputs (prompts) to get specific, useful outputs from generative AI systems. | Asking ChatGPT to summarize a complex article in simple terms. |
| Reinforcement Learning | A type of machine learning where AI learns by trial and error, receiving rewards or penalties. | AI mastering video games by learning from previous gameplay attempts. |
| Supervised Learning | A machine learning approach where the model is trained on a labeled dataset. | Training an AI to recognize cats by feeding it images labeled “cat.” |
| Unsupervised Learning | A machine learning method where the AI identifies patterns in data without labeled responses. | Clustering customers based on purchasing behavior without predefined categories. |
| Token (in NLP) | A chunk of text (like a word or part of a word) used by language models to process language. | “AI is amazing” contains three tokens: “AI,” “is,” and “amazing.” |
| Training Data | The information and examples used to teach an AI model how to perform a task. | Thousands of labeled images used to train AI to recognize objects. |
| Turing Test | A test designed to determine if a machine exhibits human-like intelligence. | If a human can’t tell whether they’re chatting with an AI or a person, the AI passes the test. |
| Vector | A numeric representation of data used in AI models to understand relationships between different items. | AI using vectors to determine the similarity between two words. |
| Zero-shot Learning | When an AI system performs a task it wasn’t explicitly trained for, based on general knowledge. | AI translating a language pair it hasn’t seen during training. |
| AI Governance | Frameworks, policies, and structures put in place to oversee and manage AI systems, covering ethics, legal compliance, and risk. | Corporate policies ensuring AI respects privacy, prevents discrimination, and maintains accountability. |
| Explainable AI (XAI) | Methods to make AI decisions transparent, understandable, and interpretable. | Tools that highlight which parts of an image a computer vision model focuses on. |
| GAN (Generative Adversarial Network) | A neural network model with a generator and a discriminator, often used to create realistic synthetic data. | Generating realistic face images or deepfake videos. |
| Transformer | A neural network architecture widely used in large language models, based on attention mechanisms. | GPT (Generative Pretrained Transformer) models for text generation. |
| Fine-Tuning | Adapting a pre-trained model to a specific task or domain by continuing the training on specialized data. | Customizing a general language model on legal documents to answer law-related questions. |
| AI Agent (Agentic AI) | An AI system capable of autonomous decision-making and action in its environment, often with minimal human oversight. | An AI scheduling assistant that independently handles calendar invites and meeting logistics. |
Research Library
Keep up with the latest policies, guidance and tools that can solve your most important questions.
Research Library
Federal
US Federal AI Legislation Tracker
White House Blueprint for AI Bill of Rights
FTC decisions - Filter by Industry for AI cases
State
US State AI Governance Legislation Tracker
National Conference of State Legislatures - AI Legislation Tracker
US State Privacy Legislation Tracker
Litigation
Global
Global AI Legislation and Policy Tracker
American Bar Association
ABA Legal Opinion re Gen AI Tools
ABA Taskforce Report on Law and AI
Maturity Models
NIST AI Risk Management Framework 1.0