The emergence of three distinct use cases in generative AI
We’re finally seeing generative AI settle into something more structured, and that’s good. After years of broad hype and experimentation, there’s now clarity. We’re talking about three practical directions: getting things done (what the engineers call “agentic AI”), helping you think better (thought partner), and fulfilling emotional needs (companionship). Most people active in this space are interacting with these three functions in the same app, often at the same time.
Right now, platforms like ChatGPT are mixing all these roles into one interface. It’s useful, but let’s not pretend it’s optimal. When you ask the model to brainstorm strategy, and ten seconds later ask it to summarize a meeting, that’s fundamentally different behavior being run through the same system. This is a messy workaround. Eventually, it makes sense to separate these use cases into their own dedicated tools.
And that’s where the opportunity is. Executives looking to build, or back, the next generation of AI should stop thinking in terms of general intelligence. Real success here comes from creating focused products that do one thing very well. That’s the natural endpoint of what we’re already seeing. The players who separate signal from noise, and build around specific problems people are trying to solve, will dominate the field in the next five years.
Agentic AI focused on task automation
Let’s talk about the area with clear monetary upside: agentic AI. This is about productivity. No fluff. Just output. Tools like GPT-5 have been tuned for this, highly functional systems that write code, generate slides, draft landing pages, and build execution plans on demand. Ethan Mollick at Wharton put it best: GPT-5 doesn’t just respond; it proactively generates deliverables you didn’t ask for, useful ones.
AI companies have raised billions promising they’ll enable machines to do labor-intensive work. That’s not some long-range dream. It’s happening now. When GPT-5 outputs a 90-day action plan with no prompting, that’s commercially disruptive.
The business case is simple: time saved equals cost saved. That’s why most of the best-funded research labs are doubling down on this use case. The potential to replace, or significantly augment, knowledge-based tasks in marketing, strategy, product development, and customer service is very real.
But let’s be clear, tool fatigue is a risk. There’s a difference between giving people AI that automates clutter and AI that overwhelms with unnecessary output. Precision matters. The best implementations will combine autonomy with relevance. If your AI keeps pushing out content you didn’t want, that’s waste. If it surfaces what you need before you know you need it, that’s value.
This space is going to define the next decade of enterprise software. The ones who integrate agentic AI deeply into workflows, not as an add-on, but as the core engine, will lead. Everything else will follow.
The role of AI as a thought partner
We’re seeing the early stages of AI becoming more than just a tool, it’s showing potential to become a partner in thinking. That doesn’t mean philosophizing or mimicking human emotion. It means processing abstract ideas, synthesizing new information, and asking the kind of follow-up questions that help someone refine a concept. This is different from automation. It’s about deep engagement with your thoughts.
The thinking model, what people are calling the “thought partner” use case, is still hard to scale. It requires more time, consumes more compute, and doesn’t produce immediate results like a document or proposal. That’s one of the reasons it hasn’t been prioritized at the commercial level. OpenAI’s o3 model was highly capable in this space, but it disappeared when GPT-5 shipped. Now, even GPT-5 leans into quick productivity over reflective reasoning.
For business leaders, this slower, reasoning-intensive mode of AI opens up important long-term value. There’s demand for this kind of capability in strategy formulation, product exploration, scientific research, policy development, any complex domain where results aren’t binary. Right now, these use cases are underutilized because they don’t offer the same short-term ROI as task automation. But as compute becomes cheaper and users expect more personalised, intelligent engagement, not templated responses, these thought-centric tools will gain a distinct market seat.
This space will grow, but the demand won’t come from everyone. It’ll come from executive teams, leaders, and teams with problems that can’t be solved by checklists. If you’re in that category, and you’re building tools that can think before responding, you’re not behind, you’re early.
Companion AI and its emotional engagement
One of the less predictable, but clearly significant, developments in AI is its role as a companion. This isn’t about replacing humans. It’s about interacting with technology in a way that feels responsive, personal, and in some cases emotional. For some users, these models have become friends, therapists, or romantic partners. That’s not speculation, it’s happening, and it’s shaping real product usage.
This isn’t a fringe use case. People build relationships with AI. They rely on models for emotional support, validation, conversation. For many users, this is one of the primary reasons they engage with generative systems at all. It’s controversial, because the emotional stakes are higher. Done poorly, it leads to confusion and harm. Done well, it builds strong product loyalty. Either way, AI providers can’t ignore it.
Mustafa Suleyman, CEO of Microsoft AI, touched on this directly. He said the future of AI may be shaped less by capability, and more by personality. The emotional interaction layer becomes a competitive differentiator. That matters because users will stay with the model that “feels” most responsive, reliable, and safe, even if the core functionality is similar across products.
C-suite leaders should be making decisions now around whether to encourage or contain the companion use case. It’s a strong driver of recurring revenue in subscription-based models. But it also demands clear safeguards, ethical frameworks, and long-term horizon planning. Overlooking how emotionally attached users become isn’t just careless, it’s a missed strategic opportunity or risk mismanagement. Either path needs clear intention.
The critical importance of a focused product strategy in AI
Focus is going to separate the winners from everyone else in the AI race. We’ve hit a point where generative AI isn’t just a demo, it’s a product. That means companies now have to make real decisions about what their AI is actually for. Whether it’s executing tasks, supporting complex thinking, or creating engaging companionship, each of these has different user behavior, infrastructure demands, and economic models.
Trying to serve all use cases out of one platform is inefficient. It leads to product drift and vague positioning. Users don’t want general, they want effective. Teams that know exactly who they’re building for will move faster, ship smarter, and build stronger brand trust. And as usage scales, focus isn’t just a competitive advantage, it becomes essential for performance and sustainability.
OpenAI, Anthropic, Google DeepMind, all of them are navigating this now. We’re watching what they double down on. That’s the signal. The ones that bet on clear user needs and optimize around them won’t need to compete across every feature. They’ll dominate their space because they’re solving a specific problem better than anyone else.
For senior leaders, this means driving clarity in strategy. Don’t build AI for its own sake. Nail down the core interaction model. Choose the business case you’re targeting, with real-world deliverables, and align your teams against it. Markets don’t reward breadth, they reward consistent impact. AI isn’t magic. Without focus, it becomes noise. With focus, it becomes scale.
Key highlights
- Specialization is the next phase of generative AI: Generative AI is solidifying around three primary use cases, task automation, thought partnership, and emotional companionship. Leaders should monitor this segmentation trend and consider investing in purpose-built AI tools rather than catch-all systems.
- Agentic AI is the commercial priority: AI systems engineered to automate high-cognitive tasks are attracting the most funding and performance tuning. Prioritize solutions that reduce human workload through autonomous output if immediate ROI and scalability are core goals.
- Thought partnership models offer long-term strategic value: Though less efficient and harder to monetize today, AI that supports deep reasoning and complex problem-solving can become a valuable differentiator as compute costs fall. Leaders should view this area as a forward-looking investment rather than a short-term revenue driver.
- Companion AI is a growing, high-engagement use case: Emotional interaction with AI is fueling user retention but carries ethical and brand risks. Executives should define a clear stance, either develop safeguarded companion experiences or formally limit emotional engagement features.
- Focused product strategy beats feature sprawl: The most successful AI providers will be those that commit to one core use case and optimize around it. Leaders should resist building multipurpose platforms and instead double down on clarity, execution, and measurable impact.
