Trappe Digital LLC may earn commissions from sponsored links and content. When you click and buy you also support us.
In the early days of social media, some companies rushed to create social media policies without understanding the platforms or how people used them. These policies often included vague restrictions like “don’t do this or that” without specifying what “this or that” actually meant. By the time the policy was released, whatever social media behaviors they were trying to restrict were already outdated.
The same risk exists today as companies scramble to create AI policies. If done poorly, these policies won’t actually guide employee behavior and could quickly become irrelevant as AI capabilities advance rapidly. As Mitch Jackson explained on “The Business Storytelling Podcast:” “The AI train has left the station,” making it crucial for organizations to develop thoughtful, comprehensive policies that protect both the company and its employees.
Article sections:
- What is a corporate AI policy?
- The process to develop an AI policy
- How an AI policy helps marketing
- Challenges during implementation
What is a corporate AI Policy?
A corporate AI policy outlines acceptable and unacceptable uses of AI within an organization. It serves as a framework for employees on when and how they can leverage AI tools to do their jobs. “It’s one thing to say, I want everyone to embrace AI as the owner of a company,” says Mitch. “It’s another thing to say, we’re going to embrace AI and here are the 10 steps we’re going to take using this particular product.”
Areas covered in a corporate AI policy
Data privacy – Rules around using customer data, protecting sensitive information, anonymizing data sets, etc. This includes clear guidelines on which AI platforms are approved for handling company data, as Mitch warns that “you may be inadvertently disclosing private information” when using free versions of AI tools that share data to train their models.
Levels of Transparency – Requiring disclosures when AI is used to generate content, imagery, recommendations, etc.
Risk management – Assessing and mitigating risks ranging from bias in data sets to potential legal issues. This includes reviewing Terms of Service agreements for all AI platforms, particularly examining indemnification agreements and mandatory arbitration clauses that could expose the company to unexpected legal obligations.
Access/approvals – Clarifying which employees can access certain AI tools and what approvals are needed for higher-risk applications.
Oversight – Instituting auditing procedures to ensure AI policies are being followed.
“If you’re encouraging your team to embrace AI, but you’re not giving them these data protected AI versions of the tool,” Mitch notes, “what they’re going to do is they’re going to download a free version of ChatGPT, or one of the other platforms where the data that you’re typing in is being shared and used to train the LLM.”
The goal is to encourage innovation and efficiency gains from AI while protecting the business and customers from harm.
According to digital strategist Shawn Goodin on “The Business Storytelling Show,” about 30 percent of organizations lack AI policies. This creates uncertainty for employees about what tools are allowed and how they can use them appropriately. In addition, he estimated that 80 percent of marketing workflows will be impacted by AI.
Some key elements to include:
Principles
Prioritize ethics, fairness, and transparency. Don’t just focus on efficiency; establish principles that consider human impact. Focus on education. Don’t assume employees understand AI or its risks. Training is crucial.
Guidelines
Provide specific apps employees can use rather than blanket AI prohibitions. Clarify which tools can be used for what. Outline how new AI tools/uses will be evaluated for approval.
Involving internal stakeholders
When creating an AI policy, it’s important to gather input from the employees and teams who will actually be using AI tools day-to-day. Getting their perspectives allows you to shape pragmatic policies tailored to your organization’s specific needs and challenges. Areas to discuss include:
- What excites and concerns internal stakeholders about deploying AI? This frames where policy guardrails are most needed.
- Should responsibilities and decision rights be codified around using AI outputs? This empowers sound judgment.
- What safeguards would help address reproducibility, bias, and safety concerns around data sets? This mitigates risks proactively.
Regularly incorporating user feedback also builds broader buy-in, giving policies credibility on the frontlines. Employees help fine-tune policies over time, keeping them practical as AI progresses.
Educating leadership and staff
Before finalizing any policies, extensive education on AI for both leadership and staff is vital. Everyone impacted by AI guidelines should possess foundational knowledge of relevant concepts like:
- Algorithmic bias – How bias enters and propagates through AI systems
- Privacy risks – What types of data are most sensitive and require protection
- Transparency needs – The importance of explainability and audits
- Advantages – How AI can help us do our jobs better
Leadership needs particularly in-depth fluency with ethical AI principles to inform policy trade-offs and oversight. But staff also requires literacy to make daily decisions aligning with overarching guidelines.
Education furnishes a shared language and mental model, empowering policies to meaningfully direct activities. It also builds recognition that policies exist to empower, not restrict – unlocking AI’s potential safely. Fostering learning is thus an ongoing imperative as policies evolve with AI.
Read next: AI Content Creation: What is AI Content and the Stages to Use it
Appointing AI owners
To oversee policy implementation, organizations should designate clear owners and governance processes for AI oversight. Depending on scale, this may involve:
- Assigning executive-level AI safety officers to maintain policies enterprise-wide
- Embedding AI review boards within each business unit to assess new use cases
Creating working groups of ethics, compliance, security, and engineering leaders to update guidelines
Central AI ownership streamlines enforcing policies, responding to incidents, and adapting approaches as AI progresses. Distributed governance through working groups and unit oversight tailors safeguards to nuanced needs across the organization.
Combined, this bridges policy intentions with on-the-ground realities – sustaining AI safety as innovative applications develop over time.
The process to establish a corporate AI policy
Consider using a development process like this:
Corporate AI policy creation flowchart
1. Understanding the need for AI Policy
- Understanding the role of AI in the organization
- Recognizing the potential risks and benefits of AI
- Identifying key areas that require policy guidelines
2. Defining AI policy principles
- Ethical considerations
- Data privacy rules
- Transparency requirements
- Risk management strategies
3. Drafting AI policy guidelines
- Defining acceptable AI applications
- Setting restrictions on AI usage
- Outlining the approval process for new AI tools and uses
4. Involving internal stakeholders
- Gathering input from employees and teams who will be using AI tools
- Understanding their concerns and needs
- Incorporating their feedback into the policy
5. Educating leadership and staff
- Algorithmic bias
- Privacy risks
- Transparency needs
6. Appointing AI policy owners
- Assigning executive-level AI safety officers
- Embedding AI review boards within each business unit
- Creating working groups of ethics, compliance, security, and engineering leaders
7. Implementing the AI Policy
- Communicating the policy across the organization
- Providing ongoing training and education
- Auditing adherence to the policy
8. Regularly reviewing and updating the AI policy
- Monitoring the effectiveness of the policy
- Adapting the policy to technological advancements and changes in the organization
- Incorporating feedback from employees to fine-tune the policy
You can use an AI tool like Taskade to create and update a workflow like this, which is what I did. It also offers writing first drafts.
How good policy helps marketing teams
Marketing, in particular, stands to benefit enormously from AI tools in areas like content creation, personalization, data analytics, and more. But missteps could significantly damage brands.
AI policies empower marketing teams to navigate AI safely and effectively through:
Clarity: Knowing specifically which AI tools are approved, what types of data can be used, and what processes to follow enables confident adoption of helpful AI.
Creativity Within Guardrails: Guidance provides the freedom to apply AI capabilities creatively.
Risk Avoidance: By flagging common pitfalls and establishing auditing procedures, policies help marketers avoid basic mistakes like putting sensitive data into public AI platforms.
Trust Building: Following established ethical principles and transparency guidelines reassures customers that AI is being used carefully to improve their experience.
Read next: Can AI edit videos?
The challenges of implementation
While sound AI policies are essential, effectively implementing them presents difficulties. As AI progresses exceedingly swiftly, policies struggle to keep up. What constitutes “acceptable” and “unacceptable” uses continues shifting.
“You want to make sure everyone’s properly trained,” Mitch emphasizes. “If you’re using AI to troubleshoot, if you’re using AI to help with sales content or how you’re handling an incoming phone call, whatever it may be… everyone needs to be on the same page.”
A particular challenge comes with managing independent contractors who may use AI tools on behalf of your company. As Mitch notes, “Make sure you’re using properly drafted third-party independent contractor agreements that protect you as the company for something that this independent contractor may inadvertently or intentionally do in violation of the law as it applies to artificial intelligence.”
This requires flexible policy governance processes focused more on core principles than detailed prescriptions. Using centralized oversight groups rather than static policy documents enables adapting to new AI innovations. As unprecedented tools emerge, these groups can provide guidance tailored to specific use cases.
Similarly, organizations must continually update employee education on the latest AI advances and risks. Relying on one-time training produces knowledge gaps as technology changes. Instead, organizations should provide AI literacy as an ongoing learning stream.
Grab your free, ungated! AI policy template
The bottom line
AI delivers game-changing opportunities but also risks if mishandled. This necessitates clear policies guiding acceptable usage. While complex to formulate and maintain, setting baseline expectations around safe, ethical application fosters innovation by providing guardrails tailored to an organization’s needs and culture.
With the right principles and processes guiding usage, companies can empower employees to harness AI’s full potential while building critical trust with stakeholders. But outdated policies hamper progress. By taking an adaptable, educational approach to governance, policies can evolve apace with AI itself.
Grab your free, ungated! AI policy template