Beyond ChatGPT: Generative AI, the New Workplace Productivity Tool?
Defining workplace productivity just got a little trickier with the rise of ChatGPT and other generative artificial intelligence tools.
Going forward, the concept of a productive workplace requires a look at how AI, machine learning (ML) and other technologies can assist with — or even take over — human tasks.
Generative AI Assists Humans
There's significant interest in the potential of generative AI in the workplace. For some, the technology brings fascination about its breadth of capabilities; for others, it is causing concern over its unintended consequences.
Jay Pattisall, Forrester VP and principal analyst focusing on marketing services, including how machines and technology impact the skillsets and roles of people creating marketing ideas, says part of the fear is due to the idea that generative AI can accomplish some of the human tasks we long thought would remain human, like creating and conceptualizing ideas appealing to audiences or readers.
While new generative AI use cases pop up every day, some of the core functions of this technology include:
Coding
Generative AI tools can write and update code, and translate it from one programming language to another. They can also check existing code for bugs and explain the problems they encounter.
“Code creation, transformation and variation are certainly the field where ChatGPT is a welcomed tool,” said Dirk Schrader, VP of security research for Netwrix. “It can be used either for writing certain modules of the code or when variations (a rewrite) are needed; for example, to change hash values to avoid easy detection by defensive tools like Virustotal.”
Below, the ChatGPT bot shares what programming languages it knows and what it can code in Python.
Writing
One relatively new use case of generative AI is creative endeavors — like writing.
Give ChatGPT (or a similar tool) the command to write, and write it will. You can ask for an 800-word essay on the migration of humpback whales, or a Twitter post encouraging users to read your newest blog post. The technology will deliver.
Below, ChatGPT tries its “hand” at a short poem on pink flamingos in iambic pentameter.
The more detail you provide, the better results you’ll receive. For instance, you could say you want the writing to be “thought-provoking” or “in the style of the New York Times.” You could ask the bot to write like a pirate or a cowboy from the Wild West.
What’s great about the creative capabilities of the tool (and all generative AI) is that it remembers your conversations and can therefore answer follow-up questions. If it spits out something you want it to revise, you need only ask. You might type: “Can you take that pink flamingo poem and double the word count?"
Other written content use cases for generative AI include:
- Crafting personalized marketing and sales content
- Creating task lists for efficiently completing activities
- Answering complex questions, pulling data from documentation
- Drafting and reviewing reports
Related Article: Better Together: How Creatives Can Benefit From Generative AI
Visual Creation
ChatGPT is a generative text tool, meaning it can write words and code, and it can share information. But other generative AI tools — like Dall-E, another offering from OpenAI — can produce images and video.
Dall-E is similar to ChatGPT in that you enter a text prompt, but in this case, you describe the image or video you want to see. For instance, you might ask for “a field full of pink tulips with a herd of chihuahuas running through.”
This tool specifically produces four images to choose from. And, like with generative text, you can refine your description or request to change the tool’s outputs.
Related Article: ChatGPT Opens the Floodgates for AI in HR
Generative AI: Not (Yet) Ready for the Big Leagues
Generative AI has a lot to offer. Businesses, schools and individuals across the globe are tapping into these tools to supercharge productivity and lessen workloads. (Or, in the case of schools, scrambling desperately to determine how this tech will fit into curriculums.)
But generative AI isn't quite ready to be relied upon entirely — at least not yet. While the tools continue to learn and evolve, they have shortcomings.
Inaccuracy
We must remember that these are experimental emerging technologies, and while they show a tremendous amount of promise, Pattisall said, "at this stage, there’s still a degree of inaccuracy.”
For one, the output you get from generative AI may not match what you asked. Think back to my chihuahuas in a tulip field example. I asked Dall-E for an image that included a herd of chihuahuas, but all four outputs included only one.
And generative text seems to have the same issues. You might ask for an 800-word essay but get 450 words in return.
And the information within the text itself? That could be problematic.
“When you get into more detailed requests where you’re asking for written articles or papers,” said Pattisall, “sometimes what has been noted among some of my colleagues and Forrester…is that there are a lot of inaccuracies.”
Generative AI can and does produce content that is factually incorrect or misleading. This means users still need to parse through and fact-check all information. “There’s a little bit of mediocrity in the language side of generative AI at this stage," Pattisall said.
Copyright
Then, there’s the copyright dilemma.
Tech companies train generative artificial intelligence on materials from the open web — news websites, social media posts, forums and more. The AI tool then uses that source information to generate its own text or visuals.
So, who owns this image? Is it the creator of the source images? The AI itself? The company that owns the AI? The person who typed in the prompt?
This is an “early generation” problem for generative AI, Pattisall said. One he thinks organizations will contend with early on.
“I think it's reasonable to conclude that ownership rights around IP will be extended to artists and creators as their materials are being used,” he said. “Otherwise, the creators that use these materials — and more specifically, the brands that they’re using them for — could potentially be subject to liability if that weren’t the case.”
Bad Inputs
With generative AI, the inputs affect the outputs.
Let’s look at ChatGPT. It’s trained on information from the open web, information put there by biased humans. Because the AI tool’s inputs are biased, its outputs will show bias, too.
Some have already documented generative AI's insensitive remarks. And Sam Altman, OpenAI CEO, tweeted that “ChatGPT has shortcomings around bias.”
Similarly, Schrader said generative AI tools can suffer if a malicious actor influences inputs, which would result in the wrong outputs and, potentially, bad consequences.
Learning Opportunities
This issue — malicious influence on the input — doesn’t seem to be a concern specific to ChatGPT, he said, as its data basis and learning phase are likely decoupled from operations.
“However,” he added, “there are examples around chatbots learning from interaction or AI-based cybersecurity solutions failing due to training on influenced traffic like false or altered datasets. General vendors and users of AI/ML solutions need to be extra careful about the data used to train the solution for the job.”
Related Article: Work Processing: The Tools We Use to Think, Write and Remember
AI & Humans: A Future Partnership?
ChatGPT (and other artificial intelligence tools) will evolve. Beyond updates from the tool creators, generative AI learns from its past interactions to better meet human expectations and preferences.
“If I had to predict in the future where this is headed,” said Pattisall, “where it’s most likely headed is a new partnership.”
One based upon a new set of tools in which the machines sit alongside the people, he said. They’re assistants to the creative process, there to help out creators — by creating iterations or drafts of things that are improved upon by people, for instance. “Or eventually,” he said, “become sophisticated enough to take on certain workloads autonomously.”
We haven’t reached that point, though. “But you can see how that could become a reality in the future.”
New AI Partnership, New AI Rules
The first thing companies should do when they adopt artificial intelligence or natural language tools is to talk to employees affected by the decision, said Schrader.
Organizations will need to develop new training programs that teach employees how to work with AI tools. For instance, users should be fully aware of what data they’re allowed to share with AI, he said.
“If we are talking about open source, it is a bad idea to provide it with any kind of sensitive information. If this is an in-house solution, there should be rules for data input to avoid future problems and possible bias.”
Beyond that, he said, creating tasks for AI is an ability that needs to be trained. Employees might not have an in-depth understanding of how they can use AI for their betterment. Companies should empower teams by training them on the specific AI tools they adopt.
Related Article: How Companies Can Get Employees on Board With the New Wave of AI
A Call for Updated IT Infrastructure
“ML and AI are already part of our lives, and it's going to evolve,” said Schrader.
Eventually, companies will use this technology to optimize processes, so IT teams should be prepared to secure this new IT infrastructure.
For instance, for the many AI/ML-based next-generation firewall solutions available, Schrader said the added security requirement concerns how these systems learn from the traffic being processed by the algorithms in use.
“Basically, when employing AI/ML, a set of simulations needs to be defined and used to gauge the effectiveness of the solution. Does it still detect what it should? Has the learning caused a drift from basic scenarios?”
Change is a regular factor with any configuration of servers, desktops or networking devices, he added. “But with AI/ML solutions in place, it is vital to make sure a company notices that drift.”
Smaller Organizations Can Compete — and Thrive
Another prediction from Pattisall? Generative AI, along with other automation tools, will lead to smaller organizations.
Not smaller in terms of capabilities, but people. These organizations will be able to use a powerful set of tools to make them smarter and allow them to work smarter, he explained.
This tech could be a win for small businesses that don't have the funds to hire hundreds of staff members in a race against big-box brands. Tools like ChatGPT could provide the boost needed to remain competitive.
Plus, generative AI can help shore up the current skills gap.
In the US alone, there are 11 million job openings, which translates to thousands of companies understaffed and struggling to maintain workplace productivity. In the (potentially near) future, AI tools could allow those companies to maintain productivity without hiring more employees.
The Future of Generative AI and Workplace Productivity
Generative AI will bring a lot of value to the workplace in terms of productivity.
McKinsey outlined some use cases that create an early impact, including in HR, employee optimization, legal, engineering, marketing and more. And people are discovering new use cases each day.
Still, it will take time before ChatGPT and other generative AI tools become a permanent, productive fixture in our work lives. These tools are still in their infancy, and many capabilities and advancements are yet to come.
Still, the future looks clear: AI isn’t going anywhere.
About the Author
Michelle Hawley is an experienced journalist who specializes in reporting on the impact of technology on society. As a senior editor at Simpler Media Group and a reporter for CMSWire and Reworked, she provides in-depth coverage of a range of important topics including employee experience, leadership, customer experience, marketing and more. With an MFA in creative writing and background in inbound marketing, she offers unique insights on the topics of leadership, customer experience, marketing and employee experience. Michelle previously contributed to publications like The Press Enterprise and The Ladders. She currently resides in Pennsylvania with her two dogs.
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