Steer for a talent transformation strategy (and avoiding AI fatigue)

 There was a debate on whether to feature the term “AI” in the title of this article. Honestly, a key motivation for pursuing the research that led to this post was sparked by the widespread excitement about AI appearing constantly in our LinkedIn feed, to the point of feeling the fatigue, and even a bit disappointed in the algorithm of this, and the others, social media and content curated apps. 

We soon discovered that there is an entire concept called "AI fatigue", not exactly how we were feeling it, but more about the mixed emotions people in the workforce have regarding the use of AI tools.

Photo by Mart Production via Pexels (background updated with AI and Adobe tech)

From micro blog posts to video podcasts, lately, most of the tech content we encounter revolves around AI. They often sound or read very similar, usually mentioning the same few top providers. The articles (and social posts... at least the popular ones with paid-campaigns behind it) tend to focus less on the “value” of the technology (and its practical use) and more on serving as marketing tactics aimed at “big tech” companies or those with the purpose of writing to generate shallow content that attracts viewers and clicks.

So, in this instance, our aim is to recognise and tackle this anxiety, demonstrating that there are more efficient methods for adopting AI that lead to improved results by framing it as tools designed to support our employees and their performance, which in turn benefits the organisation.

Let’s begin.

There is no doubt that AI as a technology is  now more accessible, and it is transforming the way we function. 

According to a Harvard Business Review article...

... About eight in ten employees had strong concern about at least one AI angst item.

- Photo by HBS

However, as everything, there is a cause-and-effect, and at times that effect is not always positive. Here are a couple:

• You cannot see the truth from the fake. What is truly providing value vs what is market manipulation; where bubbles at some point burst as the market looks for correction and normality.

• The creation of unnecessary anxiety, that takes away the amazing value behind innovation and exploration.

It is that anxiety, one of the factors that can put at risk what some are desperately, and with questionable ethical approach, are pushing forward; creating risks for their own business.

This is evident in the way we consume content. Algorithms and influencers on platforms like LinkedIn and others frequently present similar material. For instance (and as an example), when scrolling through LinkedIn, let us say that 6 out of 10 stories might be about AI (depending on who/what you follow and the content you interact with). Among those 6, one might offer valuable insight or education, while the other 5 tend to follow trends and may be forgettable to some; meanwhile, the reader might feel more concerned about their position in the market.

McKinsey has reported that 88% of companies reported regular use of AI. They also reported that adoption might be stalling as the results of employees’ anxiety due to displacement.
 
Another report indicates the average company puts in the drawer 46% of its AI POCs before hitting production. This is an indication of the challenges behind AI adoption and deployment. 

The realignment.

What if instead of focusing on volume, companies would focus on the value provided by their talent?
 
One of the risks behind wrongly promoting AI adoption in your Org is when failure to direct human talent toward areas where they provide the most unique value, specifically by choosing to maximize work volume rather than work quality. Below are several themes that may be encountered depending on this risk:

• When it comes to value, leaders often make the mistake of assuming that any time saved by AI should immediately be filled with X more hours of work. This optimises for "tasks completed" rather than the "strategic thinking and human judgment" that drive true efficiency and innovation.

• Prioritising quantity over real business impact.

• It is not about workload efficiency (e.g. automation that hits business metrics) and more about raising the expectations of what employees are delivering, leading to burnout.

•  Missing the opportunity of granting a percentage of the employee’s time for research, innovation, training. Missing the 90/10 rule (90% delivery, 10% your time for personal growth that aligns with the company’s business).

• Mid-management and above should dedicate time to strengthening company culture. Happy employees will embrace AI adoption and drive business success. Without investment in empathetic communication, leadership training, relationship building, and team-building activities, risks such as diminished trust, toxic environments, unhealthy competition, and attrition may arise.

We’ve been reading the number of companies that hired without a plan, or strategy, due to many challenges lately:

• Pandemic and post-pandemic times.

• Rethinking how we work: Fully remote, hybrid approach and/or Return-to-office.
• Many companies acted out of self-interest during the pandemic—focused on survival rather than genuinely considering their employees. They often displayed pretended empathy, only to reverse course once the pandemic ended, lacking both a clear strategy and valuable lessons learned from the experience (though this might be better discussed in another article).

• The benefits of automation coming out of traditional tools and the AI technology that was in front of them.

• Market fluctuations caused by crises such as geopolitical tensions and wars.

As a result, companies require restructuring because of financial challenges, yet in many areas (with the USA market playing a major role and acting as a strong advocate), they cite the “AI boom” as a justification. This absence of transparency and honesty is creating not only trust problems and anxiety in the job market but also producing the opposite outcome that executives aim for, which is a truly effective AI implementation with wholehearted commitment.

In other words, the rhetoric should be about:

1. Job transformation,
2. New skills to build (for the AI adoption),
3. Strategic thinking for our leaders,
4. Solution-driven mentality for our leads and individual contributors. 

The rhetoric SHOULD be less about “AI is a job killer” or that “entry-level jobs are doomed”. This last one causes an issue for all the graduates that are coming out of our universities.

 

According to an Upwork study...

... Data reveals 96% of C-suite leaders expect AI to boost worker productivity, but 77% of employees report AI has increased their workload.

- Upwork logo

AI anxiety, or what some are calling *AI angst (*AI angst are the concerns related to job security, loss of control, uncertainty, concerns about privacy, etc. due to AI), can be mitigated. It starts with accountability and proper planning from top to bottom in the organisation. The first step is by leadership acknowledging that success requires seeing this as a culture change + a talent transformation.
 
We look into this in the next section.

Reward and metrics to drive positive AI adoption.

Good culture and positive AI adoption with desired outcomes.

Having a clear plan is always a wise approach when aiming to introduce change within your organisation’s operations, such as implementing AI tools across various departments, which represents a significant shift.

 

You might start by enabling AI tools for select groups as pilot projects, then gradually incorporate security measures, followed by training sessions, and eventually establish some form of interim governance. At a later stage, you could introduce a metrics framework. However, this “let’s just get started” method and figuring things out along the way might seem like rapid progress (and provide plenty of content for LinkedIn micro-blogs), but it should not be mistaken for true agility; rather, it often results in chaos. In an attempt to keep pace with competitors, this approach can lead to high costs with only minor gains, alongside increased turnover, a toxic work environment, possible security vulnerabilities, and the need to reassess the entire AI adoption process, potentially requiring further financial investment. 

 

I would choose to do what numerous others have already been discussing instead:

1. A leadership approach, doubling down in transparency.
1. Public sharing. Leadership showcases the overarching AI strategy, its adoption plan, and the AI syllabus based on the department, business vertical and career path.
2. Communication. Comms. will take place through town halls, workshops, chat rooms, and internal newsletters. These channels will be used to share the dos and don’ts, as well as the reasoning behind the strategy for aligning the new tool with the company’s vision and mission. Communication is a two-way process, enabling employees to participate and feel involved, which helps build trust and improves retention rates.
3. Building together versus a mandate approach. Leaders should offer visibility about how the work is evolving, how the company is transforming as a result, and how employees play a role in that development, giving them a sense of belonging within the organisation.
    
2. Provide clear information about the recent changes related to productivity and offer incentives or rewards. Take “time” as an example. If AI tools are saving time, then do not pile more work onto your employee, instead reward them with work-life-balance, or involvement in more strategic work (and not just tactical tasks). Another great place is to revisit the focusing on outcomes, instead of outputs. Here are some of the benefits this can bring for your leads and individual contributors:
1. A burnout reduction, which correlates with the metric of retention improving.
2. Quality improves when individual contributors have time beyond their regular Business-As-Usual (BAU) tasks.
3. Innovation accelerates as motivation increases. 
4. Recruiting improves. Good policies + stability + culture beats a toxic environment that pays you more.  
3. Enablement and upskilling.
1. Learning occurs across all levels and disciplines. It’s important to strike a good balance between broad knowledge (skills and certifications that can be used in your marketing) and hands-on training focused on solutions (demonstrating the company’s value by delivering quality work and addressing the problems your clients expect you to solve).
2. AI task force and AI ambassadors. This helps with:
1. Keeping accountability.
2. Normalise the learning curve.
3. Set the new gen of experts.
4. Provides assistance with new AI-related business and helps senior technical leads become independent and self-reliant, enabling them to lead future proposals alongside product owners and client services managers.

Metrics: Assess the true "impact" delivered through effective AI integration.

We can measure and assess the value employees deliver when they embrace AI intentionally and willingly, compared to when they experience AI-related anxiety. This value can be demonstrated in various aspects, including customer satisfaction, enhanced workflows, and knowledge gained.

 

Here are a few ways to measure that:

• Value delivery, not time spent:
• Workflow time reduction.
• Workflow efficiency.
• WIP (Work In Progress) stability.
• Knowledge sharing (increased frequency).
• Reusability (Example: AI workflows, test suites, scripts, others).
• Quality outcomes. 
• Reduction in defects. This is when leveraging AI-assistant for testing and code reviews.
• Improved stories’ acceptance rate.
• Documentation quality (for projects, for company on-boarding material, for code, others). 
• The value delivered to customers or stakeholders.
• Research and insights.
• Innovation. Spinning prototypes to present concepts.
• Enhanced customer satisfaction through quicker identification of problems and bugs in the solutions or systems provided to your clients.
• Governance framework, responsible AI and best practices.

Final remarks.

To successfully navigate the transition to an AI-integrated workplace, organizations must move beyond the hype and prioritize a human-centric, value-driven strategy, by:

• Champion transparency over mandate.
• Reinvest the "time dividend".
• Foster expert enablement.