AI in startups is moving at warp speed, and it’s a thrilling ride! But as VCs, we’re not just here for the adrenaline rush; we’re here to invest in innovation that stands the test of time—and trust.
Startups are doing incredible things with AI, but some applications are sparking a lot of debate. Here are a few of the “grey-area” uses we’ve been pondering lately and why we think they deserve a closer look.
1. Job candidates rated on their chatbot chats?
Imagine applying for a job, and you grant the hiring team access to all your interactions with AI tools like ChatGPT. The hiring team then learns more about you from your interactions, the way you do prompt engineering, the tone you use, how precise or vague you are with your commands, and your problem-solving approach. This all becomes clear from all the random queries you’ve typed in over the years.
Upsides: For hiring managers, this could be a goldmine. The insights might help identify candidates who align with a company’s vibe with a quick and data-backed peek at personality and communication style.
But here’s the catch: Would you feel comfortable knowing that your midnight, pre-caffeine chats with an AI are now part of your job application? What if your quick “venting sessions” could sway a hiring decision? Not to mention the bias angle—does this risk giving certain communication styles an unfair edge? For founders, balancing innovation with respect for candidates’ privacy is essential, and if they’re not careful, they might scare off top talent.
Examples: While there’s no official word on companies diving into this (maybe they’re keeping it hush-hush?), a few startups are already making waves in this space. Take HireVue, for example – they’re using AI to evaluate video interviews, analysing everything from speech patterns to tone and word choice, like a virtual Sherlock Holmes for hiring.
2. AI customer service with emotional ESP
Imagine an AI that not only answers your questions but actually feels your pain or at least senses when you’re frustrated. AI tracking could analyse customers’ emotional tone over time, signalling when someone’s annoyed or likely to leave. It’s futuristic and very tempting for any company wanting loyal customers.
Pros: For startups, this is a customer service superpower. If AI can catch and fix problems before customers walk away, you’ve got a real winner for retention and loyalty.
The dilemma: Do customers know they’re essentially “on emotional camera” with every interaction? Some might find it helpful, while others could feel like their privacy is stretched too thin. And if startups go one step too far, using emotional insights to push extra sales? Let’s just say customers might not find it so “helpful” anymore. For founders, this is a tightrope walk: get it right, and it’s a dream; get it wrong, and it’s a PR nightmare.
Examples: Startups like Entropik Tech are leading the charge with tools that decode emotions through facial expressions, voice tones, and even eye tracking. Their goal? To truly understand how customers feel. Then there’s EnableX, whose FaceAI platform slips Emotion AI into customer engagement solutions, turning every interaction into a masterclass in personalisation. New Metrics takes it a step further, weaving Emotion AI through the entire customer journey – from the first ad you see to your post-purchase experience.
3. AI in the driver’s seat of employee promotions
Imagine AI tracking your work patterns to predict if you’re ready for a promotion. In theory, it could bring some fairness and objectivity to career growth decisions. In practice? It might feel like Big Brother with a spreadsheet.
Why it’s cool: This could reduce bias by analysing only the numbers. In startups with smaller teams, it also saves a lot of time, and decisions feel more data-driven.
Why it’s tricky: Employees might not love the idea of their every move being under an AI’s microscope. Constant scoring? Predictive models for who gets the next promotion? It could lead to stress, demotivation, and severe work culture concerns. Startups using this tech should think about transparency and employee trust; otherwise, the very talent they’re trying to retain might feel pushed out.
Examples: Startups like HiBob are turning career growth into a data-driven art. By tracking employee performance trends, skill development, and leadership readiness, they’re helping companies make smarter, fairer promotion decisions. Similarly, Personio is blending analytics into HR workflows, giving managers predictive insights into who’s ready for the next big step and when.
4. Personalizing prices just for you (and you, and you…)
Finally, let’s talk personalised pricing—setting different prices based on your online behaviour, purchase history, or even how much AI thinks you’re willing to pay. Dynamic pricing sounds efficient, but there’s a fine line between personalised and, well, a bit sneaky.
Why it’s a money-maker: Startups can increase revenue by tailoring prices to each customer. It’s flexible, competitive, and has huge potential.
Why it’s controversial: If customers realise they’re paying more than their friends for the same item, they might feel cheated instead of valued. This could quickly turn customers off or even lead to backlash. For investors, it’s a risk to consider; done right, it’s profit gold. Done wrong? Customer trust goes down the drain.
Examples: Companies like Zilliant and Dynamic Yield are transforming pricing strategies with AI. Their platforms dig into customer behaviour, purchase patterns, and even perceived willingness to pay, enabling businesses to set prices that feel tailored (and hopefully not exploitative). It’s a delicate dance between maximising revenue and staying transparent enough to keep customers on board.
Final thoughts: Staying smart about the risks
AI opens up some amazing possibilities for startups. But when it comes to sensitive areas like hiring, privacy, and loyalty, it’s worth stepping back to think about the big picture.
For startups and investors, it’s not about shying away from innovation but finding that sweet spot between exciting and ethical. With some open conversations and clear policies, we can create AI-powered companies that are not only profitable but also responsible and, well, likeable.
This is an opinion by Andra Bagdonaite, partner at FIRSTPICK – a €20 million high-speed venture capital fund and an accelerator for tech startups in the Baltics. The fund mainly invests in pre-seed Fintech, SaaS, Deeptech and Consumer marketplace startups. FIRSTPICK’s portfolio startups receive precious early market access, impactful partnerships, and deep expertise of the early stage.
Original article: https://labsoflatvia.com/en/news/where-do-we-draw-the-line-controversial-ai-use-cases-in-startups