AI FAQs for Business in 2026

Artificial intelligence (AI) refers to computer systems designed to perform tasks that normally require human intelligence, such as language generation, image recognition, decision-making, prediction, and automation.

Modern AI systems can analyze large amounts of data, recognize patterns, generate content, and assist with complex business workflows across industries including health care, manufacturing, finance, and cybersecurity.

Generative AI is a category of artificial intelligence capable of creating new content such as text, images, video, audio, software code, and summaries. Popular examples include ChatGPT, Microsoft Copilot, Gemini, Claude, and image-generation tools.

Businesses increasingly use generative AI for productivity, customer support, automation, marketing, software development, and data analysis.

Artificial intelligence is the broader concept of machines performing tasks associated with human intelligence. Machine learning is a subset of AI focused on systems that learn patterns from data rather than relying solely on explicitly programmed instructions.

Learn more about the difference between AI and machine learning.

AI introduces risks including data leakage, prompt injection, credential theft, deepfake impersonation, insecure plugins, hallucinated outputs, privacy violations, and shadow AI adoption. Scammers are also increasingly using AI to improve phishing campaigns, automate reconnaissance, and create synthetic media for fraud.

Businesses should establish AI governance controls, employee usage policies, and monitoring procedures before deploying AI tools broadly.

Shadow AI refers to employees using unauthorized or unapproved AI tools without formal oversight from IT, cybersecurity, legal, or compliance teams. This can expose businesses to data leakage, regulatory violations, intellectual property risks, and inconsistent governance practices. Shadow AI showed up in 43% of security incidents among breached organizations studied by IBM's 2026 Cost of a Data Breach Report, more than double the 20% figure from the year before.

Learn more about Shadow AI risks.

A deepfake is a video, image, or audio clip generated or manipulated using AI to make someone appear to say or do something they didn't. Deepfakes can be highly convincing, particularly in compressed video or low-bandwidth call environments where quality artifacts are harder to spot.

A Deloitte poll of more than 1,000 C-suite executives, published in September 2024, found that nearly 26% reported their company had experienced at least one deepfake financial fraud incident in the previous 12 months. Incidents are likely underreported due to reputational concerns, so actual prevalence is likely higher.

Yes. AI readiness assessments help companies evaluate licensing requirements, security controls, governance maturity, compliance obligations, identity protections, and operational risks before adopting AI platforms at scale.

Learn more through the STACK AI Hub.

Yes. AI is increasingly used in threat detection, behavioral analytics, phishing prevention, anomaly detection, endpoint monitoring, and security automation. Many modern security platforms rely on machine learning to identify suspicious activity more quickly than traditional rule-based systems alone.

However, scammers are also using AI offensively, creating an ongoing AI-versus-AI security environment.

Yes. AI tools are increasingly used to generate realistic phishing emails, multilingual scams, social engineering scripts, fake login pages, and impersonation campaigns. AI can improve grammar, personalization, and scalability for attackers.

Learn more about phishing attacks.

AI is more likely to change job functions than eliminate all jobs entirely. Many businesses are using AI to automate repetitive tasks, assist decision-making, improve productivity, and augment employee workflows rather than fully replace human workers.

Businesses should focus on governance, training, and responsible adoption to maximize benefits while reducing operational risk.

Health care, financial services, manufacturing, education, government contractors, legal services, and companies handling sensitive personal data face elevated AI governance and compliance obligations.

High-risk sectors should pay close attention to evolving privacy laws, cybersecurity requirements, and automated decision-making regulations.

Businesses should monitor evolving requirements including the EU AI Act, Colorado AI laws, and broader state AI regulations.

Companies should establish approved AI usage policies and review vendor data handling practices before deploying generative AI tools.

Learn more in our complete Deepfake Detection Guide.

Microsoft Copilot includes enterprise security and compliance controls, but companies still must configure permissions, data access, retention policies, and governance procedures.

Misconfigured access controls can expose sensitive data through AI-generated responses.

No. Copilot is embedded inside M365 applications such as Outlook, Teams, Word, Excel, and PowerPoint. It operates within your existing tools.

Copilot allows you to search, summarize, and compare documents using natural language instead of manual navigation. It generates responses based on files you have existing permission to access.

Copilot agents are task-focused AI experiences that help retrieve and organize data from sources such as SharePoint or OneDrive.

Copilot follows existing Microsoft 365 permissions, but security depends on how access controls, data governance, and user behavior are managed within your company.

Yes. Copilot is controlled through Microsoft 365 licensing and administrative settings. Companies decide which users have access and can disable or limit it based on security, compliance, or rollout strategy.

Generally, yes. Copilot is licensed as an add-on to existing subscriptions and isn't included in standard business plans by default.

Copilot doesn't use a daily limit for most enterprise users. Usage is governed by M365 service controls designed to maintain performance instead of limiting or restricting activity.

Tokens are the small units of text AI tools process when generating responses. A token can be a word, part of a word, or even punctuation. AI systems use tokens to understand input and produce output. But in Copilot, this gets managed on the back end so it's not something users typically see.

Copilot is built on large language models (LLMs) that process data as tokens, but this isn't exposed to end users. Businesses don't manage token counts. Usage is managed in M365.