Developing an AI-based solution is expensive—not only in upfront development costs but also in the ongoing expenses of running computationally intensive models. Unlike traditional software, generative AI (GenAI) requires continuous investment in data acquisition, model training, and infrastructure to meet growing user demands. This creates significant financial risk, especially as companies restructure their teams to incorporate more AI capabilities.
So, how do you ensure your AI integration isn’t just something that trends on Reddit for a few weeks and then is long forgotten versus building a profitable solution that people actually need and are willing to pay for? The answer lies in achieving AI product-market fit.
Achieving product-market fit for AI-based solutions can also present a unique set of challenges. GenAI based tools, apps, and integrations can take on tasks that we once considered uniquely human. For example, many commonly used apps and services before the democratization of GenAI, excelled at “left-brain” activities like analyzing data and generating reports. GenAI can also take on these tasks while addressing traditional “right-brain” tasks such as writing compelling marketing copy or creating original artwork and designs.
This ability for a GenAI solution to tackle both left and right brain tasks makes achieving product market fit both more interesting and more challenging at the same time.
The Shift to Right-Brain Tasks: Finding AI Product-Market Fit in the Creative Realm
It’s not uncommon to think of a B2B-focused piece of software as something we use to sift through mountains of CRM data or analyze sales reports—a domain where algorithms excel because of structured inputs and clear outcomes. Traditional software often automated these “left brain” tasks such as:
- Data analysis: Crunching numbers in spreadsheets, generating reports
- Process automation: Streamlining workflows, scheduling tasks
- Information retrieval: Searching databases, filtering information
However, GenAI is stepping in to take on more “right brain” tasks that require creativity, intuition, and a more human-like flair. For example:
- Email Sequence Writing for Sales: Instead of manually crafting every line of persuasive sales copy, AI can generate nuanced email sequences that adapt tone, style, and context to resonate with diverse customer profiles.
- Content Creation and Social Media Engagement: GenAI can create engaging blog posts, social media content, and even ad copy that mirrors brand voice, something that traditionally required deep human creativity.
- Creative Design Concepts: While data-focused tools crunch the numbers, AI-driven design platforms can propose innovative visual concepts or branding ideas, capturing the more abstract qualities of aesthetics and emotion.
To illustrate this shift towards “right-brain” AI, we actually had a debate with ChatGPT about the nature of creative tasks. We argued that traditional software mainly automated “left-brain” tasks (like data processing), while generative AI is tackling “right-brain” activities (like creating art and stories).
ChatGPT pushed back, citing tools like Photoshop as examples of creative software. But we countered that those tools still rely heavily on our input, our direction, and at times our step by step instructions. GenAI, on the other hand, can create original content with a minimal set of inputs, sometimes even a simple phrase. This is a huge change. It means AI is capable of mimicking that “spark” of creativity that we traditionally have ascribed to humans alone.
The Next Stage of AI: Blurring the Lines Between Human and Machine
This evolution can be understood through the concept of “centaurs” and “cyborgs”, which illustrate different models of human-AI collaboration:
- Centaurs: Humans and AI have distinct roles. Humans handle the creative and strategic aspects of a task, while AI handles the analytical and data-driven parts. There’s a clear separation between what humans do and what AI does. This is how many AI tools have traditionally functioned.
- Cyborgs: Humans and AI are seamlessly integrated, collaborating in real-time on both analytical and creative tasks. This is the new frontier of AI, where AI becomes an extension of our own minds.
In the past, AI tools have functioned in a centaur-like manner, where humans relied on AI for task automation while separately handling creative endeavors. Now, we are shifting into an era of cyborgs, where AI can not only automate tasks but also collaborate with us on creative endeavors.
The transition from centaurs to cyborgs signifies a move towards deeper integration and collaboration between humans and AI. While centaurs emphasize complementary roles, cyborgs embody a unified workflow where human intuition and machine intelligence work together fluidly.
AI Product-Market Fit: Building the Right “Cyborg” Through Research
To build truly successful AI products in this new era of creative collaboration between humans and AI, you need a deep understanding of your users and the competitive landscape.
Market research is essential for uncovering how AI can best integrate with human workflows and amplify human creativity, rather than simply automating tasks. Here are a few of the top research areas that are crucial for success:
To build truly successful AI-based solution in this new era of creative collaboration, you first need to gain a deep understanding of your ideal customer. This means identifying:
Market Segmentation
- Company Size and Industry: Which industries and company sizes would benefit most from your AI solution, and what are their unique challenges and opportunities?
- Geographical Location: Where is your target market located, and are there any local regulations or cultural differences to consider?
- Revenue: What is the financial health of your ideal customer, and can they afford to invest in your AI solution?
Buyer Personas
- Pain Points and Challenges: What are the recurring challenges your ideal customer faces, and how can your AI solution address them?
- Goals and Aspirations: How can your AI solution help your ideal customer achieve their ambitions and objectives?
- Technographics: What are your ideal customer’s attitudes toward and willingness to adopt AI-powered solutions?
- Decision-Making Dynamics: How can you tailor your sales approach to the purchasing processes and key decision-makers within target companies?
Gathering these details is an essential first step toward building AI-based solution that will be able to effectively collaborate with your ideal customer.
2. Jobs-To-Be-Done (JTBD) Research
With your ICP defined, focus on understanding your target personas’ JTBD – the specific tasks and challenges they face in their daily work. By identifying these areas, you can develop AI-based solutions that act as true partners, enhancing productivity and efficiency. Here’s what to explore:
- Daily Tasks and Responsibilities:
- IIdentify users’ daily tasks, especially those that are tedious or time-consuming, and understand their key responsibilities to determine how AI can support these roles.
- Pain Points and Goals:
- Uncover users’ biggest challenges and their goals for specific tasks, and determine how AI can help achieve these goals more efficiently.
- Workflows and Unmet Needs:
- Analyze current workflows and the tools users employ to identify gaps where AI can provide significant improvements.
Understanding the specifics of your users’ daily duties is crucial for designing a solution that genuinely collaborates with them. This insight allows you to build an AI-based solution that seamlessly integrates into their existing workflows, addresses their needs, and solves their pain points.
3. Competitive Landscape Analysis
Since today’s AI landscape includes competition that goes beyond companies automating traditional analytical tasks, companies now face a new wave of competitors developing AI for creative work. These new players bring fresh ideas and unique skills to the market. To stay ahead, it’s essential to understand the entire competitive landscape—from large corporations to small startups. Here’s what to look for:
- Identify Key Competitors:
- Analyze both established giants and innovative startups focusing on creative AI.
- Analyze Their Products:
- Compare core features, strengths, and weaknesses, and gather user feedback to understand what works and what doesn’t.
- Understand Their Pricing and Marketing:
- Evaluate their pricing models and marketing strategies, and identify how they position their products and the messaging that resonates with their audience.
- Benchmark Your AI Capabilities:
- Assess how your solution stacks up in terms of speed, accuracy, and efficiency, and highlight unique capabilities that set your AI apart.
A competitive landscape analysis will help to differentiate your AI-based solution by showcasing its ability to foster a deep relationship with users, enabling them to achieve new levels of creative expression and efficiency.
AI Product-Market Fit: Is Your Machine on the Human Agenda?
“The key is not to prioritize what’s on the machine’s agenda, but what’s on the human agenda.” – Steve Jobs
Achieving product-market fit for AI isn’t about building AI for its own sake; it’s about creating solutions that address real human needs and enhance human capabilities.
So consider your target users: their workflows, needs, JTBD, and their operating environment. Do you understand their frustrations and joys? Do you see opportunities where an AI-based solution could fill a gap in the competitive landscape? Most importantly, do you know how AI can empower your users to achieve their goals and enhance their abilities?
If not, it’s a clear indication that you need to conduct research before confidently bringing an AI-based solution to market. Without this understanding, your AI solution risks becoming an expensive flop instead of a valuable tool for your users.
If you’re ready to unlock AI product-market fit and build a truly valuable solution, give us a call. With over 17 years of experience in B2B tech market research, Cascade Insights can help you understand your target audience, identify the right problems to solve with AI, and position your product for success. Contact us today to unlock AI product market fit and build a truly valuable solution.
This blog post is brought to you by Cascade Insights, a firm that provides market research & marketing services exclusively to organizations with B2B tech sector initiatives. If you need a specialist to address your specific needs, check out our B2B Market Research Services.
