The portrayal of AI in literature and cinema often paints a dystopian picture, leading to understandable reservations about its integration into the workplace. However, it's crucial to acknowledge AI's transformative impact on workplaces, industries, and economies, which we're only beginning to grasp.

 Artificial intelligence attracts significant media attention for its potential to revolutionise productivity and decision-making. CEOs foresee it as the most disruptive technology of 2024, anticipating both job loss and creation. AI streamlines operations, minimises downtime, and optimises workflows by processing vast amounts of data beyond human capacity.

However, challenges persist, including employee apprehension and potential job obsolescence. For instance, IBM's CEO plans to replace back-office roles with AI, raising concerns about job loss. While technology often automates tasks rather than entire occupations, concerns arise about a shortage of experts in various fields amidst rapid technological advancements.

We spoke to an expert - Lauren Huntington, an organisational psychologist and Employee Experience (EX) strategist at Qualtrics, to gain insight into the impact of AI on workplace dynamics. Much like the way Gen AI reshaped the workplace narrative for many individuals, Lauren experienced a significant shift in her discussions as well. In fact, she recounted instances where she entered meetings only to find that the organisation was resistant, stating, "No, we're closing our doors. This goes against our security policies. Don't talk to me about AI. We're shutting down that conversation."

So, can shutting down conversations around AI protect organisations or employees?

While some embrace the possibilities and innovations, others adopt a more cautious approach, expressing concerns over security policies and potential threats. Likewise, the conversation about employee experiences varies greatly in different regions. For example, in India, we see average engagement scores of 85%, while in Japan, the average is 56%. We have reached a point where the technologies available allow us to begin measuring employee experience constructs consistently across these regions. However, what we have not yet solved is how to act on these differences in a meaningful way.

This next wave of technology is helping us address this challenge because the key unresolved issue is taking action. Knowing the sentiment is valuable, but without the tools to personalise the response, “We end up with one-size-fits-all recommendations. For instance, if we recognise a significant disparity between what's happening in Malaysia and Vietnam, we may decide that our top priorities are leadership development and a standard presentation, leading to uniform solutions. However, this is not due to malicious intent but rather a lack of capacity,” highlighted Lauren.

She further explained that, “We haven't had the capacity for systems to extract insights and truly understand where we should focus our attention. It's about recognising not just the differences on the surface level but also the intersectionality of these differences. We need to craft action plans that are more personalised and move away from massive, generic objectives. With the advancements in technology, we can finally operationalise our efforts to respond with more energy and authenticity to our employees' needs.”

 Takeaways:

• New tech sparks innovation and security concerns; engagement varies regionally.
• Tech advances allow personalised employee responses, moving past generic solutions.

The interconnected nature of technological adoption, engagement, and trust

Effectively integrating AI into an organisation requires aligning AI initiatives with overarching business objectives, focusing on areas like operational efficiency, customer experience, and product innovation. By strategically directing investments towards AI projects that promise tangible returns, leaders establish a unified vision where AI acts as a catalyst for achieving strategic ambitions. However, emotional reactions must be acknowledged, and data should guide decision-making to ensure successful AI adoption.

According to Lauren, two key steps are crucial in this process: firstly, assessing the readiness of people for specific technologies and data sources, and secondly, establishing committees to provide ethical guidance, drawing from reputable sources in psychology and technology. This approach facilitates a thorough stress-test of AI initiatives, ensuring safe experimentation and addressing potential interferences. Additionally, leaders must recognise the interconnected nature of technological adoption, engagement, and trust, considering factors like generational differences and cultural nuances to foster enthusiastic AI adoption while addressing legacy challenges in engagement and trust.

 Takeaways:

• Consider emotional reactions and data for informed decisions.
• Align AI with business goals for strategic impact.

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Considerations for AI solutions: build, buy or ally?

In the rapidly evolving landscape of business, the integration of AI technologies has become a pressing need for companies striving to maintain their competitive edge. Countless studies underscore the benefits of incorporating AI into business operations, with projections suggesting significant gains in efficiency and profitability. However, the journey towards successful AI implementation is riddled with challenges that can make the prospect seem daunting.

Factors such as computing power limitations, a deficit in trust towards AI systems, and the scarcity of quality data pose significant hurdles for organisations aiming to harness the power of artificial intelligence. Lauren Huntington emphasised the need for organisations to approach AI implementation with a realistic mindset.

Building a reliable and robust AI solution, whether for content generation or predictive analytics, demands access to large and dependable datasets. While many organisations possess legacy data, retrieving and structuring it in a usable format can be a complex and resource-intensive endeavour. Despite these challenges, some organisations have embarked on the journey of developing their own AI solutions, leveraging existing resources and expertise.

However, Lauren suggested, “organisations must conduct a thorough cost-benefit analysis when considering whether to build AI solutions internally or seek external partnerships. External providers may offer access to unique datasets, such as Acme Corp's experience management data, which spans years and captures insights from millions of employees. In such cases, partnering with external providers who have invested in AI development based on these datasets may be more feasible than attempting to replicate them internally.”

 Takeaways:

• Ensure alignment with ethical standards and access to quality data.
• Assess overall costs and benefits of building AI internally, partnering with external experts, or buying an entire AI solution.