Putting GenAI in our pockets: Mobile World Congress showed how everyday AI integration can transform day-to-day business 

Organisations globally are still in the early stages of their GenAI journeys, coming to terms with its capabilities and what it means for their business models. Initially, it was hard for businesses to form a strategy around how to embrace GenAI’s arrival and the hyper cycle and fear that followed. Fortunately, the smoke has now dissipated a bit and there’s recently been a realisation, from office workers to engineers to C-level stakeholders, that GenAI has powerful positive capabilities and practical use cases today. 

Last month’s  Mobile World Congress (MWC) featured numerous companies exhibiting their technological and GenAI advancements. With these innovations on the way, let’s explore their forthcoming impact on our working and personal lives.

The current state of AI

During the conference, several companies showed how GenAI could be integrated into smartphones. Samsung, for example, previewed its AI-powered live-translation feature, while Motorola exhibited its personalised AI assistant. Some even demoed phones that rely on AI voice commands instead of apps to handle users’ needs. 

As shown during the conference, AI-powered spatial computing, like wearable VR/AR devices such as Apple Vision Pro, could replace smartphones as the primary personal technology. The price tags and hefty nature of these wearables mean they’re not quite yet ready for widespread customer use, but the workplace could be the perfect opportunity for them to enter the mainstream.

Some advanced organisations are taking AI integration to the next level by adopting an "AI-first" mindset in the meantime. This approach involves examining every aspect of the business, including workflows, business models, and individual disciplines, and asking how AI can help to solve problems. The goal is not only to increase productivity and efficiency but also to promote creativity in problem-solving. However, accomplishing these objectives will require extensive research and planning.

Moving toward widespread integration

Forward-looking companies are starting to develop GenAI manifestos or strategies, covering how they need to change from the inside out by adopting these tools. Some are even exploring embedding the technology into the digital solutions offered to end customers. However, this is a much more complex challenge and will require people to figure out where the value is and how to get started. After all, you can’t yet create an ROI business case, in which you say you’re going to see X return or X new products because the technology is so new.

Instead, the first step in AI exploration is to carry out pilot projects to identify a use case that is as small as possible, but one that can be achieved in a reasonable timeframe of three to six months. Then, you can start to get a bit of a feel for what this technology is, how it works, and how it can be applied to solve problems. This progress will help to achieve some traction in the organisation so stakeholders can see these pilot projects coming to life and delivering some kind of value, earning their increased support. 

Once you’ve got this foundational baseline knowledge, you can begin to craft a genuine GenAI strategy. Then, you can start to communicate your offerings more concretely to different stakeholders, be it the stock market or your investors, customers, and clients. Finally, you can execute that strategy iteratively, with a constant feedback loop. Then, there’s one last consideration: how do you ensure the technology continues to be used positively?

Responsible AI for the mainstream

Can governments ensure regulations promote innovation without compromising on user safety and the red tape that could hold innovation back?

At MWC, attendees and exhibitors emphasised the responsible and ethical development and implementation of AI and technology, including ethical data collection, usage, and algorithm bias. It's all about making sure the models that you use have been aligned with human value and how you apply that technology to different situations. The key to transparency is considering how bias could emerge and being able to monitor for bias in the decisions made based on the outcome or the output of an AI model. It's critical to ensure that the providers you work with are transparent in terms of how they train their models.

Then, you must put in place human gates to prevent harmful, biased content, or anything that can cause unfairness. Ultimately, trust is crucial. Supporting users to put faith in the safety and positivity of AI technology is the key to faster, wider adoption. The sky's the limit for extraordinary AI applications, with confidence worldwide.

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