As we start 2025, it’s not a stretch to say artificial intelligence (AI) has significantly transformed industries and reshaped the global economy. The introduction of AI-based ChatGPT and its record-setting adoption curve—1 million users in the first 5 days and 100 million users just 2 months after its launch—accelerated the focus on AI by a number of competing organizations, big and small alike. Content Science asserts that “AI is experiencing symbiotic, exponential growth.” As of December 2, 2024, the global AI market was valued at over $621 billion. It’s expected to reach $2.74 trillion by 2032, as noted by Fortune Business Insights. 

There are very few businesses that wouldn’t benefit from AI in some way. In fact, more than half of businesses expect AI to increase productivity. Nearly three quarters of businesses have adopted AI for at least one business function and half of respondents use AI for two or more. AI is also changing user behaviors. Over 65% of users surveyed said they’d use ChatGPT over Search. Many are already using AI to reply to text or emails, get answers to questions, plan travel, aid (or do) their writing, and a number of other daily tasks. 

So what should you do about AI if you’re running a business or leading an organization? How do you ensure your business is ready to ride the AI wave? Here are 3 areas you should consider when investing in AI:

1. AI won’t govern itself, so you need to have a plan and process in place to ensure ethical use.
2. Good content is still the best policy.
3. Someone who knows how to use AI is more likely to take your job that it is to be replaced by AI itself—for now.

Governance and Ethics

Sometimes, you have to move slow to go fast.

Businesses are concerned with exposing AI to their intellectual property. Consumers are concerned that businesses using AI will infringe on their privacy or that we’ll have a real life Skynet situation. How do you mitigate those concerns? Salesforce AI’s CEO Clara Shih notes:

There’s no question we are in an AI and data revolution, which means that we’re in a customer revolution and a business revolution. But it’s not as simple as taking all of your data and training a model with it. There’s data security, there’s access permissions, there’s sharing models that we have to honor. These are important concepts, new risks, new challenges, and new concerns that we have to figure out together.

Like any good integration or process, start with governance. Pair that with thoughtful ethical considerations and you’ll be better off than most.

Governance

Governance involves establishing policies, frameworks, and practices to ensure AI systems are developed and used responsibly. If you’re not developing AI but are more focused on integrating AI into your workflows, this entails ensuring the AI systems your business uses are operated responsibly. Policies, frameworks, and practices should address accountability, transparency, and oversight to ensure any AI you integrate with aligns with organizational and societal goals. Effective governance is crucial for establishing trust in AI systems and their outputs. 

Ethical Considerations

AI systems must prioritize fairness, accountability, and transparency, just as your other organizational policies should. Policies and procedures should be implemented to ensure that user privacy is respected and protected, especially when handling sensitive customer data. The same can be said about what company information is exposed to AI for training and/or productivity purposes. Timnit Gebru, founder and executive director at The Distributed AI Research Institute warns:

There’s a real danger of systematizing the discrimination we have in society [through AI technologies]. What I think we need to do—as we’re moving into this world full of invisible algorithms everywhere—is that we have to be very explicit, or have a disclaimer, about what our error rates are like.

Ethically speaking, it’s imperative for businesses to ensure any AI they develop or integrate doesn’t repeat the sins of the past and continue to propagate cultural biases. 

One of the primary AI concerns for consumers is misinformation, according to a recent survey covered in Forbes. This concern has been propelled to the forefront of our collective psyche during the last decade or so of election cycles. Axios chief technology correspondent Ina Fried shares more perspective:

When I talk to experts in the field, the area they’re most concerned about is misinformation…It means watermarking videos and other things so that we know they were created artificially. It means having a provenance chain so that you can tell this is footage that was legitimately captured from a device and here’s everything that’s happened to it along the way.

It’s important to know where the data is coming from. Are the sources credible and factual? If not, you’re adding to the noise instead of providing trustworthy content.

Best Practices 

When it comes to best practices for AI, you don’t have to build them from scratch. A number of organizations have already developed standards you can reference or leverage to create your own. Google’s AI Principles prioritize social benefit and safety while avoiding misuse. Microsoft’s Responsible AI Standard emphasizes fairness, transparency, and inclusivity. IBM’s pillars are built on transparency.  OpenAI follows the approach of Teach, Test, and Share; teaching AI right from wrong, conducting internal evaluations, and sharing externally to obtain real world feedback. Salesforce’s 5 Guidelines for Responsible Development stress accuracy and sustainability.

Wherever you’re starting from, know their are many others paving the way so you don’t have to feel like you’re in uncharted territory.

Good Content is (Still) the Best Policy

One of the best ways to maximize search engine ranking and findability is with high-quality content. The same is true when working with AI. Whether you’re building your own AI model or choosing the best tool to compliment your workforce, make sure you know what the AI is being trained on. High quality content resonates with humans and bots alike. What makes for high quality content in the age of AI?

Quality Data

High-quality data is the cornerstone of effective AI training. Garbage in, garbage out (GIGO) is a well-known principle in AI: poor data quality leads to flawed AI performance. OpenAI’s GPT models, for example, rely on diverse and comprehensive datasets to generate accurate and relevant responses. Training AI on biased or incomplete data can skew results, highlighting the need for rigorous data curation. RT Insights stresses quality over quantity as a significant impact on AI training and performance.

Creating Relevant Content

I’ve been saying for what feels like ages that one of the most important elements to driving SEO success is the quality of the content you create. Google confirmed the same in 2024, noting that quality content is crawled more often. What about AI you ask? There are a number of expert insights and opinion pieces on this point. One of my favorites notes that AI trusts lengthy text from high authority review-oriented sites. It means emphasizing external brand mentions and word-of-mouth over first party claims. What others say about your brand is important and should be infused in how you speak about your brand.  

Context is Key

Generative AI are essentially conversation-based tools. They rely on context for providing accurate and relevant responses to prompts and queries. Derek Philips sums it up very effectively:

Imagine chatting with someone who can’t fathom the context of your conversation. While you’re asking the person how to get to the nearest gas station they keep talking about the factors that influence the price of gas. Interesting, sure, but not particularly helpful in that instance.

Producing content that aligns with user needs and AI learning requirements is crucial. Using analytics to understand your users, their preferences and behaviors are important. Effective strategies include using diverse datasets, regularly updating training material, and involving subject matter experts in content creation.

Balancing Automation and Human Touch

Effective integration of AI into your business requires a combination of human intervention coupled with thoughtful automation. Just as automated SEO audits are best interpreted by people, humans are instrumental in the creation of quality content. Human oversight ensures that your content remains relevant and engaging. Writers, designers, and marketers should collaborate with AI to ensure content resonates emotionally and culturally. Human editors can refine AI-generated content for tone and context. Striking this balance helps to maintain authenticity and foster meaningful connections and trust with users.

When implementing AI, what might your employees or peers be concerned with?

AI Won’t Take Your Job; A Person Who Knows How to Use AI Might 

While not 100% accurate – there are certainly examples of AI replacing jobs already – the biggest threat to your employability or your ability to rise above your competition is an inability or unwillingness to learn how to most effectively use AI.

So how do you catch up, keep up, or get ahead?

Always Be Learning

There are an almost endless number of ways to evolve as a knowledge worker. Here are some of my favorites. 

Organizations like Content Science Academy provide content-focused coursework on AI, workflows, and user experience creation.

AI platforms like LinkedIn Learning, Coursera and Duolingo use personalized learning algorithms to help motivated learners gain new skills.

Google, DeepLearning.AI, and a number of universities offer free AI coursework. Examples include Harvard, MIT, Stanford, and Duke. Even MasterClass has a series focused on AI.

The tools are there if you’re willing to invest the time to learn.

Using AI Tools for Productivity

AI tools such as ChatGPT, Notion AI, and Grammarly can significantly streamline tasks and improve your workflow. From drafting emails, presentations, and even copy, to calendar and time management, summarizing information, and expediting research, AI can make you much more efficient. It can free up time to focus on strategic initiatives as opposed to repetitive tasks. 

Here’s an example relevant to this article. I used a combination of ChatGPT, Microsoft Copilot, and Poe to help me think through my outline after providing them with a number of different variables including: the quote that was the foundation for this article, my primary topics, and some additional topics I wanted to be sure I included. I prompted them to provide links to all sources they used. I read through the majority of those sources, revised my outline, and resubmitted it for further input.

Once I was happy with my outline, I wrote my first draft. I then prompted each of the three tools to write an article based on my final outline, once again asking each to provide links to any sources they leveraged. Each AI-generated article was similar but unique, and there didn’t appear to be an overlap in the sources they used—which provided me with a pretty extensive list of white papers, university projects, articles, and opinion pieces to reference as I refined my draft. It also provided me with some ideas on how I might refine or tweak how I presented certain topics. 

AI is a game-changer for content creation.

Decision-Making and Strategy

AI’s ability to analyze vast datasets quickly enables analysts to derive insights more efficiently and focus more time on storytelling and sharing of insights. Machine learning models and advanced analytics tools identify patterns, trends, and correlations in data that might be overlooked by human analysis. AI-powered visualization platforms like Tableau and Power BI help analysts to simplify complex data insights into actionable strategies, and do so quickly. 

Predictive analytics can identify emerging customer preferences, allowing businesses to tailor their offerings to meet their customers where they are. It can forecast demand and supply fluctuations, enabling companies to optimize inventory levels and reduce costs. (See Comparables.ai). Examples like this five-year-old PepsiCo use case highlight how AI-driven tools can be used to optimize supply chain operations and identify growth opportunities.

Innovative Applications

Sifting through seemingly endless volumes of data isn’t the only way you can leverage AI to improve how you work. Creative workflows are being enhanced by heavily enterprise-adopted software like Adobe Photoshop or Canva and their generative design AIs. Newer tools like Midjourney, Dall-E, and soundraw.ao are being used to generate images, video, and audio tracks. Healthcare diagnostics are being transformed by AI. AI chatbots are streamlining customer interactions and freeing up human agents to address more complex issues.

Those are just the tip of the iceberg. AI is being leveraged for so many use cases, and the possibilities are limited only by our imagination and ability to prompt our AI-counterparts for solutions.

The Future Is Bright with Our AI Partners

While my imagination is certainly active enough to not completely dismiss the possibility of Skynet-like AI takeover, I’m more bullish on AI’s future and its impacts on the workforce. When implementing or integrating AI in your organization, you’ll be best served to not forget the following

1. AI won’t govern itself, so you need to have a plan and process in place to ensure ethical use.
2. Good content is still the best policy.
3. Someone who knows how to use AI is more likely to take your job that it is to be replaced by AI itself—for now.