Generative AI: Why An AI-Enabled Workforce Is A Productivity Game Changer

Today, most of your workforce wouldn’t identify themselves as content creators or even knowledge workers. However, many individuals throughout your company are constantly tasked with producing and curating content. Whether they are summarizing meeting notes, conducting research for a report or preparing documentation for a new product launch, they are frequently waist deep in creating, editing and sharing information both internally and externally.

A small portion of this content is highly strategic to your business—it must be high-quality, authoritative and original. Nobody will question the time and effort that’s spent on this content to get it right. While still important to your operations, the rest of the less strategic content being produced is mostly routine, mundane and repetitive. Even though this tactical form of content doesn’t need to be the equivalent of a gourmet meal, it still needs to be tasty, healthy and safe to eat. In other words, at a minimum, the content must be on point, clear, consistent and error-free.

Creating content can be an involved process as it can span multiple steps such as planning, researching, writing, editing and publishing. To move new content through all these stages on a weekly basis can often be a tedious and time-consuming process. While companies have been able to outsource some content creation responsibilities to outside contractors, technology solutions haven’t been able to significantly lessen the burden of content creation—until recently.

Enter Generative AI

Generative AI is a form of artificial intelligence technology that can produce various types of content such as text, images, audio and other media based on prompts from users. With the release of OpenAI’s ChatGPT to the general public in November 2022, a tremor was felt around the world as the power of AI was democratized to the masses. In only five days after launching, the online service surpassed one million users—something that took Instagram two-and-a-half months and Spotify five months to accomplish. Now, countless startups and software applications have begun touting their own generative AI capabilities and integrations.

Many businesses are beginning to pay closer attention to what generative AI has to offer as well. In a recently released study by Salesforce, 57 percent of senior IT leaders believed generative AI is a ‘game changer.’ Even though a third of the respondents (515 IT leaders) felt it was overhyped, 80 percent of these skeptics still felt generative AI could help their organizations better serve their customers. Goldman Sachs Research predicts generative AI “could drive a seven percent (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.”

In a recent OpenAI study, researchers predicted that generative AI could impact at least ten percent of work tasks for approximately 80 percent of the US workforce. Nineteen percent of workers could see at least 50 percent of their tasks impacted. While the emergence of generative AI may end up displacing some workers, it will fundamentally transform how most people produce content, especially with the routine, repetitive and mundane tasks. ARK Big Ideas 2023 predicts that by 2030 knowledge workers will see a more than fourfold increase in productivity by leveraging AI technologies.

How will Generative AI transform worker productivity?

We are starting to get some glimpses into the productivity gains offered by generative AI. Research analyst Joachim Klement shared a couple of early studies that reveal its promise. The first study asked a group of programmers to complete a programming task. Half of the participants were given access to a generative AI (GitHub Copilot) and were able to complete the task in 71 minutes, which was about 56 percent less time than the programmers who didn’t use AI assistance (161 minutes).

In a second study, 200 university-educated professionals were given a writing task that half could use ChatGPT to complete. The AI-assisted participants were able to complete the task in less time—17 minutes compared to 27 minutes (a 37 percent boost in productivity). Not only where they faster but when the assignments were graded by independent judges, the AI-enabled participants received a higher average grade (B versus C+). The researchers also discovered the weaker participants benefited the most from using the generative AI technology, as it essentially leveled out the performance between low and top performers.

By combining the massive processing power of algorithms with the crowdsourced knowledge and expertise contained within its training data, generative AI helps workers shorten the time it takes to complete their tasks and enhances the quality of their outputs. Every worker can benefit from a personal assistant that can help perform manual tasks, help brainstorm ideas, proofread drafts, develop sample designs and so on. Eventually, generative AI technology will provide workforces with an array of specialized AI assistants to perform a variety of common tasks. Unlike their human counterparts, they are always available, willing to help and won’t care how you leverage their suggestions.

The productivity gains from generative AI will quickly emerge from several areas. A helpful way of understanding the sources of these efficiency gains is through the perspective of the key stages in content creation:

1. Plan. Creating a strategy for what content you need and how you will approach it is an important first step in the process. Generative AI can provide guidance on building a plan for creating, structuring and deploying content. For example, an entrepreneur could prompt for an outline for her business plan or get help in devising a content calendar with recommendations on what topics and channels to target.
2. Research. Until now, research has primarily involved using search engines to scour the internet for relevant and trusted information. Now, based on simple prompts, generative AI can process a massive amount of information and produce summarized key findings on a topic. For instance, a product manager could use generative AI to research the top consumer pain points in relation to his new product concept and get comprehensive results related to the problem.
3. Write/design. Developing a first draft of any content can often be the most time-intensive step. Generative AI can streamline the process of creating an initial draft in whatever writing style and format you desire. For example, a senior manager can provide a generative AI tool with a set of bullet points on different market trends and have it write up an entire long form marketing blog post with relevant examples optimized around specific SEO keywords.
4. Edit/polish. Once you’ve created an initial draft, you often need to further revise, consolidate and improve it. While generative AI doesn’t remove the responsibility for human editing—and may even require more oversight—it can also contribute to streamlining your editing process. For instance, a sales representative can use generative AI to proofread an RFP response for sentence structure and word choice as well as experiment with different tones or styles to better match the sales opportunity.
5. Publish. After you finalize your content, you may need to format it differently so it can be distributed across different channels. Generative AI can be used to quickly refactor content to fit different formats. For example, a designer could use the technology to rapidly create and distribute variations of a single design across multiple social media channels.

From the inception to the delivery of content, generative AI can play an integral role in simplifying and streamlining each stage of the content creation process. When you aggregate the time savings across all these stages, you start to appreciate the tremendous value this technology offers. However, at this early stage in its development, organizations still need to be cautious with how it’s applied within their businesses.

Some bumps in the road before Generative AI can be fully adopted

Recently, Microsoft founder Bill Gates said artificial intelligence was the second biggest innovation in his lifetime after the graphical user interface in 1980. However, many business leaders are not yet convinced it’s ready for primetime—with good reasons too. While early results are promising and extremely compelling, more focus and attention will be needed on the following issues:

1. Accuracy concerns. Generative AI can’t always distinguish between fact and fiction. It can argue confidently that cow eggs are much bigger than chicken eggs. Without proper factchecking, users can be fed bad information that sounds coherent but is complete nonsense.
2. Privacy and security concerns. Sensitive (HIPAA) or proprietary information should not be shared with public generative AI tools. Unfortunately, companies such as Samsung are discovering their employees have uploaded confidential source code and strategic meeting notes into non-private generative AI tools.
3. Bias concerns. Currently, generative AI tools are mostly trained on data collected from the Internet. Human biases that are rampant online become ingrained in these algorithms, and they end up further reinforcing prejudices based on gender, race, nationality and politics. Microsoft’s Tay—a Twitter chatbot—and Meta’s Galactica—a large learning model (LLM) based on scientific articles—were both quickly shut down (16 hours and three days respectively) after they were caught mimicking sexist and racist language.
4. Ethical concerns. When generative AI’s training data relies on the text and images from other people, it raises concerns about attribution and intellectual property. Getty Images recently sued Stable Diffusion for copyright infringement when the art generative AI tool copied 12 million images for training purposes without permission or attribution to the artists. In addition, generative AI can be used to create deepfakes, misinformation and propaganda.

Before organizations can fully embrace this promising technology, more work on standards, policies and guidelines must be done by vendors, governments and client organizations to help mitigate these issues. Generative AI is going to play an important but disruptive role in how we work going forward. While your organization can choose to wait until everything is figured out, it may not be the best strategy based on the accelerating pace of AI innovation.

Start experimenting with Generative AI today

After years of business intelligence consulting experience, Nick Kelly started his own analytics training and consulting business that specializes in dashboard design and adoption. Like many other entrepreneurs, he was amazed by AI’s capabilities—but the technology also made him anxious about his future.

Rather than waiting to see how things would play out, Kelly launched a bold experiment. He challenged ChatGPT to replicate his expertise in creating the requirements for enterprise dashboards. He trained the generative AI tool using more than 50,000 words from his book and other training materials.

What he then discovered was astonishing. Using ChatGPT, he was able to rapidly generate industry-specific questions and a reasonable set of starting requirements to review with his clients, which greatly shortened the discovery process by hours. In addition, with ChatGPT, he found he could generate sample metrics for his dashboard wireframes that were largely aligned with industry standards and only needed minor revisions.

Although he was impressed with these results, Kelly was still skeptical of whether generative AI could also help with dashboard UI design. In general, he has found professionals struggle the most with the design aspects. However, using Midjourney—an image-focused generative AI—Kelly discovered he was able to produce visually stunning mock designs in a fraction of the time it took him previously. The color choices and design cues from these AI-generated mock-ups inspired more appealing interfaces for his dashboards.

Prior to his experiment, Kelly felt threatened by generative AI and skeptical of how it could help his business. Now, he is adapting his skills to this transformative technology and integrating AI tools throughout his new process. The results have been impressive—greater efficiency, wowed clients and a sharpened focus on how he can deliver more value.

Author Frank Herbert said, “Technology is both a tool for helping humans and for destroying them. This is the paradox of our times which we’re compelled to face.” While your organization must be careful how it uses generative AI, you’ll benefit from the lessons your teams learn now as the technology continues to mature. The productivity stakes are too high to cling to the status quo and not explore how generative AI can enhance your existing workflows. Personally, I’m excited to test this technology within my own data storytelling business—and I hope you are too.