How AI Features Enhance Real User Value in Early Products
AI features and user value are widely discussed issues. Cassie Kozyrkov, Google’s former Chief Decision Scientist & AI Adviser, describes her experience with an AI elephant in the room in the following way:
“So often, clients ask me “What kind of AI can I use?” And the answer is, “You don’t need AI for this, and it will be counterproductive for your aim[s].” But the response is always, “But we won’t get any funding if our product doesn’t have AI in it!”
This quote accurately reflects today’s overwhelming pressure to release some sort of AI features. However, the more pressing matter is not in adding AI to get funding – it’s about adding AI that also increases user value. If one seeks to add some AI for decorative purposes, it is likely to become a liability. But, if used strategically, AI features can generate an immense boost in user value, substantial ROI for business, and be a feature to convince investors to fund your further development.
In this blog post, we’ll outline how early-stage startups can introduce AI features to increase user value and get the funding they need. Yet, we’ll start with a brief detour into what not to do with AI to establish why certain AI features might fail and are not worth investing in.
Table of contents
“AI Theatre” and Other Cases of What Not to Do
Adding AI features that truly contribute to user value is a challenge. In fact, according to recent data, 70 to 80% of all AI features are never used by users or deteriorate user experience. So, what is “AI Theatre”? The UX field defines it as follows:
“the growing trend of launching flashy, AI-enhanced features that appear innovative but fail to deliver real value or offer meaningful solutions.”
By now, there is already a whole scary graveyard of ‘novelty-first’ decorative AI features within this AI theatre trend. The graveyard examples where companies cancelled their AI features include:
- LinkedIn AI-generated follow-up questions for posts were dismissed for being shallow, way too generic, and contributing little to nothing to the user experience.
- Apple’s AI-generated news summaries were retired in January 2025 due to backlash from news organizations and journalists for inaccuracies and misinformation.
- Meta’s experimental AI character accounts were also shut down in January 2025 due to strong protests from users and media scrutiny.
- McDonald’s AI voice bots were cancelled in mid-2024 due to AI mishearing and creating strange orders.
- Amazon’s AI-enhanced internal hiring system was also abandoned around 2024 due to its bias against applications from female candidates.
And the list goes on.
The common thread among these failures is that AI Theatre features only decorate the product rather than truly power it. The good news is that there are quite a few ways to implement AI productively. Utility AI features can solve real user problems in novel ways, generate substantial ROI, and get startups funded. According to Terence Mauri, founder of Hack Future Lab, a global think tank:
“In other words, it’s not about the technology itself; it’s about how you use the technology that matters. AI is not a panacea for all ills. Still, when incorporated into a company’s problem-solving repertoire, it can be an enormously powerful tool.”
Successful AI Features: Case Studies
In contrast to AI Theatre, the other 20 to 30% of AI features enhance user value and provide companies with great Return on Investment (ROI).
Loom AI Features: AI-generated titles, chapters, and summaries
For instance, Loom introduced three AI features – AI-generated titles, chapters, and summaries. As a result, since the launch in Q3 2023 to now, user satisfaction for its AI Suite has grown from 73% to 96%. Additionally, 67% do not edit AI-generated titles, chapters, and summaries. This represents a real increase in user value in terms of an immediate productivity boost and time savings. The results in terms of user value:
- A reduced burden of polishing videos for creators;
- ease of understanding and navigation for viewers,
- and overall, increased engagement and number of views for videos created in Loom with the AI suite.
For startups’ investability, this kind of AI implementation tops the list. If you manage to automate a part of the workflow that generates substantial time savings, it will make a strong case to get the funding you need.
Duolingo: “Explain My Answer” and “Roleplay” AI Features
Another example is Duolingo’s two AI features, such as AI-generated “Explain My Answer” and “Roleplay”. Instead of simply showing mistakes, Duolingo’s AI Suite can now explain them and let the student take their practice further, replacing a tutor interaction. The result: 51% surge in Daily Active Users and revenue projection of $1 billion.
The success of Duolingo’s AI implementation has even been featured in academic research circles. Duolingo’s AI has shown the capacity to substantially improve users’ efficacy. The screenshots of features are shown below.
So when it comes to a real user value, most users (90%+) reported that AI features:
- Help understand mistakes;
- Prepare for real-world speaking situations;
- Improve understanding of grammar and speech;
- Helps users learn from mistakes;
- Boosts their speaking ability.
AI Features for Increasing User Value in Early-Stage Startups
With every digital segment, unique and ever-present pain points and actions causing friction persist. It can be either data-intensive user input, choice overload, or the tedious manual editing of user creations. Sure, the tech is developing fast. Yet, the speed of knowledge creation and the growing complexity of modern workflows. still outpace it. If it is an early-stage B2B startup, creating a Minimum Viable Product (MVP) often lies in processing a ton of data from numerous sources or making intricate connections digestible. No wonder such kind of MVP development should feature an efficient AI feature to become investable.
User value-adding AI features often target exactly those areas where AI shines: processing large data volumes, working with the user input, and cleverly automating routine tasks. Not some flashy “I’ll generate it instead of you” gimmicks, but an actual solution to a user pain point or friction step. A good guiding rule for successful AI features is that they extend or assist human workflow rather than replace it. So, here are our top picks of AI features that can add user value in early-stage startups.
#1 AI Automation for Data Entry and Editing
Messy voice notes, long textual queries, or dispersed data sets can be effectively structured into a well-organized post, Jira ticket, or CRM entry. So, digital tools that require user input can leverage AI to edit, structure, or re-format it.
The user value lies in removing the fatigue of entering, editing, and restructuring their input. For the business, it significantly reduces task abandonment and user churn, leading to higher engagement and, ultimately, user Life Time Value (LTV). You can also check out our article “LTV to CAC Ratio: Investor Benchmarks for Your Startup” to ensure your startup is ready for the next funding round.
Moreover, many B2C apps like budgeting, calorie trackers, and the like require a lot of daily input from users. Instead, AI can enable them to provide raw data (e.g., receipts, images of food, etc) and structure it to maintain the data flow with ease. The same goes for enterprise clients that need to fill out CRMs, update notes on leads, or log meeting summaries along with follow-up actions. AI can take raw input and structure it into compressed, actionable data.
Examples: Canva’s AI feature Magic Switch and Descript’s Underlord AI Co-editor fall into this category. They extend the user’s data entry, be it a design, video, or podcast. Canva helps to instantly convert users’ provided input into multiple other uses. For instance, a user can get a blog post or social media ads from a presentation. Underlord AI from Descript cleans the audio by removing filler words and bleeping profanities, among other options. Descript introduced its Underlord AI co-editor, which improves user value by cutting video editing time by 65% and 3x editing speed boost compared to non-AI tools.
#2 “Just-In-Time”/“Next-Best-Action” AI Suggestions
With some tools, complexity is inherent. So, with no option of removing complexity, such an AI feature is the only way of reducing the learning curve and driving adoption. In simpler solutions, this takes the form of an elevated version of a Help button. In more complex enterprise-grade solutions, this takes the form of AI recommendations for what to do next.
This feature involves monitoring real-time user behavior and stepping in when detecting a user ‘stumble’. The latter can occur when a user clicks the same button multiple times or hovers over a complex menu for a prolonged time. In this case, AI generates and displays one-click suggestions aimed at proactively resolving a user problem in the moment.
Examples: Salesforce Einstein, Microsoft Copilot in Dynamics 365, or Intercom’s Fin.
#3 AI/AR Visualization in E-Сommerce
In e-commerce and most online shopping, the “visualization gap” is still quite a real struggle.
Certainly, Amazon and AliExpress can sell without this AI feature. But e-commerce platforms like Amazon or AliExpress rely on scale, affordable pricing, and reviews. AI-powered AR experiences are essential when a static image fails to provide the necessary level of detail or context. In turn, it helps a consumer to reach the confidence level needed for making a purchase. These use cases are:
- fashion, cosmetics, accessories, furniture, home decor, and other categories where look, feel, and fit within the space matter;
- any items that need visual clarity to reduce guesswork and hesitation;
- Goods whose pricing falls into the luxury segment and niche items.
The study by Harvard Business Review found that:
“companies that implemented AR experiences saw a 19% increase in conversion rates and a 94% decrease in product returns.”
Examples: IKEA, for instance, is a trailblazer in this trend. The results of its AR app are a 90% increase in conversion rates for users who interact with this feature compared to those who don’t.
#4 AI-enabled Knowledge Search
Today’s teams, businesses, and individuals produce a lot of ‘knowledge’. They store files, links, chat threads, screenshots, and whatnot. Today’s teams create this intensively and in large volumes. AI semantic retrieval, or RAG (retrieval-augmented generation), helps users navigate these loads of data without having to memorize an exact file name or location.
Moreover, sometimes when a person cannot find something, there is a question whether they can’t find the right location, is it a wrong filename, or the file simply doesn’t exist. Imagine asking, “Do we have a contractor onboarding policy?” Notion AI is one of the companies that successfully implements this AI feature. This AI feature generates answers within the confines of the workspace, linking each answer to the existing file. Below is an ROI calculation for Notion AI for a company with 100 employees, done by Matthias Frank.
As one can see, the average ROI of 695%, for every 1 euro invested in the tool, the company gets 6.95 euros in return.
Other examples include:
- Microsoft Copilot, which scans for answers in the existing PowerPoint docs, emails, Teams conversations, and so on.
- Duet AI from Google Workspace that provides the same semantic AI search for its Docs, Drives, and Gmail.
RAG-search is also valuable in e-commerce, as surveys indicate a 20% boost in conversion and a 30% increase in customer satisfaction.
#5 AI Personalization
For content-rich apps, AI-powered personalization can drive content discovery, boost user engagement, and, ultimately, increase user value. AI learns from the user behavior and forms patterns to predict what content is most valuable for the user. E-commerce and loyalty programs with many offers can also benefit from it. User value here stems from two key factors: less cognitive load and high relevance.
Examples:
- For Netflix AI personalization, it drives 80% of all content watched on the platform.
- Back in 2020, Spotify’s AI personalization on podcasts led to a 29% increase in content consumption, according to researchers. Today, AI-personalized recommendations of daily and weekly playlists on Spotify earn waves of praise from listeners on Reddit and other forums.
Final Thoughts
In contrast to show-offy ‘AI Theatre’ features, successful AI features are implemented with the intent to increase user value. In professional AI app development, AI features extend and supercharge human workflows, not replace them. Below is a summary of AI features in connection to user pain points for early-stage startups.
| AI Features | User Pain Point / Problem | Real-World Examples |
| AI Automation for Data Entry and Editing | The Input Fatigue and Cognitive Load for data entry/structuring/editing | Canva’s Magic Switch, Descript’s Underlord AI |
| “Just-In-Time”/“Next-Best-Action” AI Suggestions | Complexity paralysis when navigating sophisticated interfaces or rich toolkits | Salesforce Einstein, Microsoft Copilot in Dynamics 365, or Intercom’s Fin |
| AI/AR Visualization in E-Сommerce | The Visualization Gap for details, context, or positioning in space | IKEA AR |
| AI-enabled Knowledge Search (RAG) | Ambiguity in filenames, terms, or locations to retrieve ‘knowledge’ | Microsoft Copilot, Google Workspace’s Duet AI, and Notion AI |
| AI Personalization | Decision paralysis, choice overload, and decreased engagement in content-heavy apps | Netflix AI, Spotify’s Daylist, etc |
FAQ: How AI Features Can Enhance Real User Value in Early Products
Automation for input and editing often creates value fastest because it reduces manual effort inside existing workflows. Examples include turning messy notes into structured items, cleaning text, summarizing content, or formatting entries for tools like CRMs and tickets. These features feel practical and predictable, which improves trust. They also reduce abandonment because users reach a finished result with less work.
Strong signals are repeat usage, faster completion of key actions, and improved activation or retention. Less editing can also indicate higher trust in AI output. If the feature improves behaviors linked to long term usage, it adds real value.
Start with a narrow use case tied to a frequent workflow. Make AI optional, easy to review, and easy to undo. This lowers risk and helps learn what users actually want.
AI should not be added as a “wow feature” without a clear user job to be done. Avoid AI that creates extra steps, unclear results, or unpredictable behavior. If users need to constantly verify output, the feature increases workload instead of reducing it.
If adoption is low, support tickets rise, or users abandon workflows more often, a redesign is needed. A feature should be removed when it consistently fails to improve outcomes and damages trust. In early products, removing weak AI is better than keeping it for appearance.