3 focus areas to lead the AI workplace revolution

Published: February 27, 2024 | by Natasha K. A. Wiebusch, Marketing Content Manager at Brightmine

At the beginning of 2023, OpenAI boasted record breaking site visits and users, with an estimated 100 million monthly users just two months after launch. That is unheard of. For comparison, it took Instagram one and a half years to reach an audience of similar scale.

In the following weeks, employers and the public quickly acquainted themselves with the new, mesmerizing, and sometimes quirky world of generative artificial intelligence (AI). Business leaders announced AI initiatives to identify use cases, employees explored ChatGPT’s free writing services, and the public had fun with weird inaccurate pictures of… hands?

But then things took a turn. The New York Times published an article reporting that Bing’s AI said some things that were…well, disturbing. Then, Google’s top AI expert, aka the Godfather of AI, abruptly resigned, saying he regretted his work and warned of AI’s very real dangers. Around the same time, and somewhat paradoxically, IBM announced a hiring freeze of all positions that could be replaced by AI.

Yes, AI’s honeymoon phase is over — and it was short-lived, really. Despite the concerns, evidence shows that organizations will continue to move forward with AI adoption. To stay competitive, employers must achieve their own AI revolution.

The state of AI at work

Early data of AI adoption reveals that so far, some organizations are struggling to develop organized practices to fully and safely integrate AI into their business. As employers work towards their own AI revolution, it’s important to understand where AI is and is not being adopted, and what the potential issues may be.

Business functions

First, data shows AI adoption is not ubiquitous but rather adopted for certain specific functions. And few organizations (less than a third) have adopted AI beyond one business function.

Adoption also seems to be more inwardly focused, supporting internal operations by enhancing employee performance. For example, according to McKinsey & Company’s 2023 state of AI report, AI’s adoption rate overall is 55%. However, the average AI adoption rate to support the production of goods or services was significantly lower, at 3.9%, according to a report from the US Census Bureau (rates vary by business sector and are higher for large companies).

Meanwhile, McKinsey found that human resources, marketing, and sales were among the top three business functions on which AI is having the largest impact.

AI’s limited scope of use suggests that organizations may not yet have a clear AI strategy or the necessary skills within their workforce to guide their AI projects.

Adoption of generative AI

Even though AI may not be integrated into wider business processes at every organization, generative AI has proven to be somewhat of an outlier, in that its use has been notably higher. Recent surveys have found the following:

But generative AI has problems of its own. Much of generative AI use is unguided and unmonitored. And generative AI’s shining stars, chatbots, are known to be inaccurate. Publicly available chatbots also lack citations, could compromise privacy, and are the subject of litigation for copyright violations. These and other issues with AI have raised novel ethical and responsible use issues for employers.

Focus areas moving forward

AI’s promises and problems are proving to be varied in these early stages of adoption. However, the data reveals three key focus areas employers with AI ambitions should prioritize:

1. Skills

According to a survey commissioned by Amazon Web Services, hiring AI skilled workers is a top priority for 73% of employers. Additionally, in 2022 IBM found that the top barrier to AI adoption is limited skills, expertise and knowledge of AI.

Ensuring employees have the skills necessary to work with AI is not just a pain point for employers, it is also certain to be a major differentiator in the coming years. Employers must implement a successful AI skills strategy to facilitate adoption, enhance the organization’s performance, and reduce talent loss.

Developing such a strategy will first require employers to have a clear vision of the role AI will play within the organization. So before hitting the ground running on a new skills program, employers may consider answering some basic questions about the type of AI they’d like to adopt, why, and for which functions.

Employees will need reskilling

Of course, AI will enhance work for employees… but it will also lead to job elimination, and employers and employees need to face this reality head on. In fact, in 2018 (before ChatGPT came on scene) the Organization for Economic Cooperation forecasted that new automation technologies would likely eliminate 14% of global jobs and significantly transform about one-third of jobs.

Even in these early stages, AI is already leading to layoffs (even if indirectly) at notable companies like UPS and Blackrock. More layoffs are inevitable, but employers have an opportunity to minimize talent loss through robust upskilling and reskilling programs.

Accordingly, in addition to determining which employees will require reskilling versus upskilling, employers will need to secure buy-in from leadership and managers for AI adoption, evaluate the capabilities of employees and the potential job matches for reskilled workers, and align skills programs with the organization’s succession planning strategy.

2. Project management

AI adoption is increasing quickly in certain areas – particularly when it comes to generative AI. But companies are still struggling to get their AI projects off the ground. In fact, some estimate that the failure rate of AI projects in business is upwards of 80%. This is almost twice as high as the failure rate of other corporate IT projects.

The problem may be in how organizations are managing AI projects, which are significantly more complex than other tech-related projects.

First, teams charged with leading AI adoption may be too fixated on executing quickly, preventing them from taking the time needed to understand the complexities of AI projects. This fixation is also called solution fixation, which is the tendency to focus on possible solutions before understanding the problem. To avoid solution fixation, teams will need spend more time learning, asking questions and understanding the potential issues of their AI projects.

Second, teams may be using the wrong project management approach. According to Ron Schmelzer, Managing Partner & Principal Analyst at Cognilytica, teams shouldn’t rely solely on the agile method of project management for AI projects. Its short iterative cycles don’t account for the complexity of data. Instead, Schmelzer recommends that teams use a hybrid approach that blends agile and data-centric methodologies to help deal with the complexity and importance of data in AI projects.

3. Ethical and responsible use

Unfortunately, thus far, organizations have been slow to respond to the important ethics and responsibility issues related to AI. Mckinsey & Company’s report found that most organizations using AI consider inaccuracy a relevant risk of generative AI. However, only 32% are mitigating those risks (and only 21% said they have generative AI policies in place).

Generally, few organizations are actively working to mitigate known ethical risks of Generative AI:

Related ethical risks of generative AI and recent of organizations using AI that are working to mitigate them

Ignoring or deprioritizing ethics and responsible use can at minimum damage the employer brand. At most, an organization can expose itself to liability for several types of legal violations (e.g., privacy, discrimination, intellectual property, and securities laws).

Employers must address these issues to mitigate risks. Action items may include:

• Implementing an AI policy.
• Adopting and communicating safeguards for using AI.
• Establishing guidelines for ethical use of AI.
• Creating a cross-functional AI working group with C-suite representation to address ethics and responsible use issues.
• Analyzing AI’s impact on the organization’s diversity, equity and inclusion (DEI) or environmental, social and governance (ESG) strategies.

Conclusion

In the coming years, employers will have the opportunity to revolutionize their organizations through AI. However, early data shows that to succeed, they must address the skills gap, adopt an AI specific approach to project management and take precautions to ensure ethical and responsible use.