Feature
In the last month, Apple and LinkedIn incorporated new AI capabilities into their respective products. While it's unsurprising to hear of tech giants launching AI, what sets these rollouts apart is their overall theme.
Instead of focusing their messaging on “the transformational power of AI,” Apple and LinkedIn stressed how their enhancements will make the user’s day-to-day life simpler.
LinkedIn claims AI will help its users strengthen their job applications and resumes, whereas Apple’s integration of AI — called “Apple Intelligence” — across its product line is intended to provide improved usability and performance, the company said.
Contrast those approaches with announcements by other AI players. When Alphabet launched Google Gemini in December, its materials highlighted the model’s performance, “sophisticated reasoning” and coding ability.
The difference is subtle but important. Where the likes of Alphabet and Meta want you to know how ambitious and sophisticated they are, Apple and LinkedIn want you to know they’re going to make your life easier. It may sound like a squishy strategy, but it can work.
The UX Difference
Consider AOL: When it launched as America Online in 1989, the company streamlined the entire process of dialing into and navigating an online service. In 1995, its 1 million users made it, in Wired’s words, “the most popular online service on the planet.” (At the time, only about 16 million people dialed into these platforms.) By 1998, AOL served 20 million customers — which represented only about 7% of all internet users at the time.
So, what happened?
In the technology world, this AOL story is an article of faith that Google won the search engine wars by offering more accurate and relevant search results — i.e., by focusing on the user.
Of course, some have argued that this premise ignores the market’s dynamics in the late 1990s. Google launched into a world of terrible user interfaces. Other search engines — like Yahoo, Ask Jeeves, Webcrawler and AltaVista — presented cluttered home pages filled with text and design elements that did little to help users actually search. Google, with its minimalist approach, clearly pointed users to the search box and offered just two options: conduct your search or jump to a website selected by machine.
However you choose to look at it, consumers were drawn to the simplest approach.
Related Article: Generative AI Makes Software Easier to Use. That’s a Really Big Deal
AI Made Easy
Apple is following a similar strategy, using AI to make its devices even more user-friendly than they already are. The company’s plans include improving Siri, summarizing content and generating images. Its goal is about improving current product functionality rather than inventing something.
LinkedIn is also approaching this from a UX perspective, integrating AI into its job-search suite, adding a conversational search tool and helping candidates create cover letters and review their resumes. Its learning platform will offer advice through AI-powered coaches, and, as TechCrunch reported, the company plans to use AI to improve its search functionality, possibly by replacing its current keyword and filter-based approach with natural language.
LinkedIn and Apple bring unique strengths to their respective tasks.
As TechCrunch notes, LinkedIn benefits from being part of the Microsoft family. Its developers can focus on new solutions and improved products, while Redmond shoulders the burdens of innovation. That allows LinkedIn’s product teams to dive deep into how AI can complete tasks with real, specific value.
Apple doesn’t have a mega-parent, but it does know how to leverage outside resources. Its partnership with OpenAI, which will add ChatGPT’s capabilities to Siri, aims to breathe new life into a virtual assistant that many users believe has stagnated. Why build a solution from scratch when your focus is on the output as opposed to the mechanics?
Related Article: Are We Focusing on the Wrong AI Use Cases?
Proving Naysayers Wrong
While some argue that Apple has come late to the AI party, others might say the company is simply being cautious. Rather than rush its AI to market, Apple is taking its time: The rollout of Apple Intelligence is expected to stretch into 2025. That may make Apple appear to be lagging its competitors, but it should also help avoid the embarrassments suffered by OpenAI and Alphabet, which launched features that generated unexpected results (Google Gemini presented inaccurate images, such as African-American Vikings, while ChatGPT was caught making up information when it couldn’t compose an answer).
Apple has only a modest share of the HR technology market, at least in terms of dedicated systems. And while specific numbers are hard to come by, it seems reasonable to assume that HR’s use of Apple devices — the iPhones, iPads and Macs — more or less track with the broader business market.
And that track is trending up.
According to Kandji, 76% of large enterprises saw an increase in the use of Apple devices last year. The company’s operating systems — iOS and MacOS — were used by about 23% of businesses, according to Statcounter.
So, why spend time thinking about Apple in relation to HR technology?
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Because the company has a knack for turning complex technology into products people want to use. Given employers’ preoccupation with improving all facets of the employee experience, that puts Apple in a good place. It also offers HR technology developers hints about how workers prefer to approach their products.
To consumers, simplicity matters. Already, HR technology vendors aim to create user experiences that are more akin to Netflix and Amazon than, say, Craigslist. As generative AI tools become more common, users — and tech customers — will make greater demands for simpler, more natural language interfaces. Whether that’s achieved through better design or delivery in the flow of work, exactly how users access HR tools is going to matter.
All of this fits with reports that customers are looking for meaningful AI use cases rather than pitches for large language models. It’s another step in AI’s evolution: less talk of what’s cool, and more talk of results.
