AI For Internal Communication: A Guide to Navigating AI Safely and Effectively

Artificial intelligence (AI) is a sophisticated technology enabling machines to simulate human-like intelligence.

It's become increasingly important to businesses due to its capacity to adeptly tackle complex problems, generate innovative solutions, forecast outcomes, and provide invaluable strategic recommendations.

How can internal communication specialists leverage AI to enhance their tasks – while staying vigilant about security implications? We'll explore some common tools, show you some ways AI can streamline internal communications, and highlight five critical limitations of AI.

Which AI tools best suit internal communications teams?

Here are a few AI tools that internal communicators should know:

How can you use AI to improve internal communications?

Hate opening up a document only to stare at a blank screen, not knowing where to start? Thanks to AI, the dilemma of a blank page is no longer a problem. We've shared some ChatGPT prompts for internal communicators, but here are some ways internal communications specialists can use AI:

• Repurpose content. Make one long-form PDF go the extra mile with AI: generate many rewordings of the same key messages to keep things fresh; extract snackable summaries to share on digital signage screens; reformat content into different styles – like turning a lengthy safety policy into a rap to add fun into your comms!
• Translate comms for global teams. Integrate AI language translation tools to encourage effective communications for global and diverse teams.
• Create content at scale. Quickly generate social media captions, blog articles and first drafts for new policies.
• Automate news updates. Use an AI content curation tool to pull relevant news, industry updates and internal announcements; schedule it to go out through communication channels such as your digital signage screens.
• Develop a knowledge base for common queries. Select and train the chatbot to answer common questions during onboarding (such as company policies, benefits, or procedures); routine questions (such as payroll or IT support); training and development (such as questions for apps and tools like Vibe); health and safety guidelines (such as protocols, remote work best practice, or crisis management procedures).
• Personalise communications. Use an AI platform that can analyse employee data to develop personalised messaging based on their preferences and behaviour patterns.
• Choose the best way to present information. Let AI show you different ways to present information and data. For example, it could suggest turning your cybersecurity policy into fun quiz questions to reinforce learning. 
• Generate custom imagery. Use AI image generation tools to quickly create high quality imagery to support key messages. No more spending hours hunting for the right stock photo.

Exploring the limitations of AI

With such an impressive and growing list of what AI can do for internal communications teams, it may cause you to sweat. But don't worry – AI is not a replacement for seasoned communicators like yourself. Sure, it can create content at scale, but you'll still need to ensure it's giving good quality output. Here are five critical limitations of AI.

1. How data is collected, stored and used

Artificial intelligence collects data using open-source datasets, synthetic datasets, exporting data from one algorithm to another, or from primary/custom data.

The challenge of AI data collection and storage is that the AI model is pulling data from many sources – some data may even include someone's copyrighted artwork, content, or other intellectual property without their consent. This problem is compounded as the models use this data for training and fine-tuning – it can get so complex that not even AI vendors can confidently say what data is being used.

Using AI tools like ChatGPT or Midjourney requires you to input your own data in the form of queries. Your queries feed into the AI model, becoming part of its training dataset, and it might show up as outputs to other users' queries. This becomes a huge issue if users share sensitive information into the AI tool, like some staff at Samsung did.  

2. Bias within AI models

AI tools depend on their training data to generate content. The principle "garbage in, garbage out" applies here: the output quality matches the quality and quantity of data the AI model receives.

Bias is found in an AI model's initial training data, the algorithm, or the predictions the algorithm produces. As a result, it can reflect and perpetuate human biases within a society, including historical and current social inequality.

Flawed training data can result in algorithms that repeatedly produce errors, unfair outcomes, or even amplify the bias inherent in the flawed data. Algorithmic bias can also be caused by programming errors, such as a developer unfairly weighting factors in algorithm decision-making based on their own conscious or unconscious biases. As the AI algorithm learns and changes over time, this evolution can introduce bias.

3. The quality and accuracy of data

AI tools can sometimes struggle to grasp context, nuances, and cultural references. For example, ChatGPT is only trained up to 2022 as of late; it won't get the most up to date cultural references. Here I've asked it to define Oxford's 2023 Word of the Year, “rizz”, but it looks like it's beyond ChatGPT's capacity:

Another instance that casts doubts on the reliability of AI tools is that they can make up facts and produce inaccurate information, also known as AI hallucinations. It looks like we still need a human eye to check for errors, fact-check information, and enhance the quality of the content.

4. Data privacy

AI systems often rely on vast data to train their algorithms and improve performance. This data can include personal information and sensitive information.

The growing uptake of AI tools raises concerns about how the data is being used and who has access to it.

Some AI tools like Microsoft Copilot integrates with an organisation's data in order to provide better answers. Microsoft have made it clear in their policies that organisation's data won't be used to train the AI model, and just like the data access controls you're used to in other Microsoft apps, their permissions model can ensure that data won't unintentionally leak between users, groups and tenants.

As a rule of thumb, assume that any time you ask an AI something, your input will be cached and processed. Whether that data is stored or used for advertising or learning purposes is up to the developers and their privacy policy.

Like with anything on the internet, it's important to think about where and how to use these tools before giving them our personal information. AI is a new field, so the rules and policies for it are still developing worldwide. Laws such as GDPR and California Consumer Privacy Act will regulate how AI vendors collect, store, and process data.

One way AI vendors are encouraged to maintain data privacy is through data anonymisation and de-identification of personal data. It relies on various methods and techniques to balance safeguarding personal information while keeping data useful. This prevents data misuse and mitigates data breaches.

5. Picking safe AI tools

All the talk about data breaches and misuse can be quite scary. These are some tips that can help you confidently assess risk and choose a suitable AI vendor for your needs.

• Assess the vendor's own AI governance. Ask for evidence of AI governance processes, defined roles & responsibilities for AI development, ethical codes of AI conduct, and any conformity to AI governance standards.
• Ask for evidence substantiating performance claims. The core AI's model structure and training techniques can be highly sensitive, but evidence of quality or performance should not be proprietary
• Understand the source and collection methods to understand what population the dataset represents how the data was filtered for low quality or inappropriate content, and how it gets updated and kept fresh
• Enquire about monitoring and incident reporting process. A responsible AI vendor will have robust response times, be continuously improving, as well as provide a clear path for reporting incidents
• Ask vendors about their secure data handling procedures, and ensure it meets your own internal standards

Vibe and Artificial Intelligence

Vibe is embarking on an AI journey to take communication teams to new heights of efficiency while amplifying their impact within the Vibe platform.

The first phase of our AI journey, our team will create an AI assistant that can streamline tasks such as transforming lengthy content into easily digestible bite size messages, spark creative content ideas, dynamically generating images to reinforce messages, offering Vibe template recommendations tailored to specific content needs, and facilitating language translation for global teams.

The second phase will grow our AI assistant, with a focus on fully automating the slide setup process. This involves transforming content sourced from the AI engine, Word, PDF, or PowerPoint and working in harmony with Vibe's extensive template library to ensure visually compelling, bite-sized content is delivered on-brand and in a way people learn and retain information.