How to prepare your AI product launch strategy in 2024

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The AI landscape is continuously growing - with over 3000 startups now in the space and 37% of organizations having actualized AI in some form.

Startups and scale-ups integrating AI into their products will continue to surge.

But launching an AI product successfully in 2024 will require more than your traditional and conventional product launch.

The fast-moving nature of AI development means a successful launch hinges on balancing cutting-edge technology with practical application and market understanding.

The following 5 steps provide an actionable process to help product owners and founders launch a new AI app in 2024.

Step 1: Conduct Comprehensive Market Research

This will feel like we are preaching to the choir but this is a comprehensive step by step guide.

So first things first: what problem are you solving and who are you solving it for?

Identify pain points:

• Analyse market reports and trends from reputable sources
• Engage on social media platforms and forums about pains and bottlenecks
• Analyze direct and indirect competitors in your space

Identify target audience:

• Network with relevant people in the space (in person events and online)
• Engage with different personas of potential users on social media
• Narrow down potential users with online surveys
• Use AI analytics tools to gather insights from online conversations
• Create organic content around your identified problem and analyze the demographics it resonates with

The key to this step is testing assumptions and hypotheses until you gather enough validation and confidence in them to start building a solution that can address a clear, high-value problem.

Step 2: Develop a Robust Product Roadmap

While taking an iterative and highly adaptable approach to development is key (especially when navigating assumptions), it is still essential to have a well defined roadmap with clear milestones to nudge you in the right direction.

A clear roadmap balanced with market feedback can provide:

• The first features to work on
• The first pool of customers to target
• The right resources to allocate and when
• The potential risks and mitigation strategies

Road map + market feedback = actionable steps.

Trying to come up with a perfect roadmap is futile as many of your hypotheses can still be proven wrong.

So here’s a short 6 month template for key milestones with best practices and feedback gathering in mind:

1. Market Research (Month 1)
   1. Problem to solve
   2. Target Customer
2. MVP Development (Months 2-3)
   1. Core AI model training
   2. Solves the core problem
   3. Basic user friendly interface
3. Beta Testing (Month 4)
   1. Limited user testing
   2. Feedback collection and analysis
4. Refinement (Month 5)
   1. Implement improvements based on beta feedback
   2. Performance optimisation
5. Launch Preparation (Month 6)
   1. Marketing campaign
   2. Final test and quality assurance
6. Product Launch (End of Month 6)
   1. Official release
   2. Monitor initial user adoption and feedback

Step 3: Build and Test the AI Product (MVP)

Building an MVP means something other than building the most rough and bare-bone version of a product.

It doesn't say startups should build half-fleshed products to validate hypotheses. The MVP concept is a solution and milestone that reduces our upfront risks while making us more certain that our ideal product is what people will want.

We confirm this with early testing, data collection, and feedback gathering to reinforce our confidence in our built solution.

While iterating with any tweaks and fine tuning necessary until we do.

Step 4: Prepare Your Marketing and Pre-Launch Plan

We can all agree that in 2024, the entire AI industry is much more saturated and opportunities for breakthroughs in the noise are much rarer.

The significance of marketing and sales has dramatically escalated nowadays. A successful launch now demands a serious approach.

Best practice dictates that a multifaceted approach is best—a combination of content marketing, utilizing launch channels, and optimizing prelaunch marketing assets.

Content:

• Blog posts: early investment into SEO for relevant search terms will prove dividends and always a worthwhile investment.
• Social media: share bit size insights, tips, demos, product updates
• Whitepapers: develop thought leadership with reports on industry updates
• Case studies: showcase testimonials and success cases from early testing

Launch Channels:

• Product Hunt: plan a strategic launch to reach early adopters
• Forums: Engage in discussions and share insights
• Email marketing: build a waitlist and marinate early adopters
• Webinars: host educational sessions to showcase your AI product

Pre-launch marketing assets:

• Countdown campaigns: create urgency and anticipation with a countdown on your website and social media
• Early access offers: exclusive access and rewards for early adopters
• Landing page: optimize to capture leads and funnel to email campaigns

The running theme highlights the need to track what’s working and what’s not and adjust based on performance.

Tools like Google Analytics, email marketing software, and social media analytics will help you determine which areas need more attention.

Step 5: Phospho for Data Analytics and Insights Gathering

The most important thing to a successful AI product launch is the richness of data and insights to fuel fast iteration cycles.

At Phospho, we provide a text analytics platform that helps monitor, optimize, and iterate LLM apps by extracting rich insights from user interactions.

This unlocks understanding from your early testers/users that was previously unattainable to early startups and scale-ups.

From real-time monitoring of user interactions to automated extraction of KPIs and continuous performance evaluation, Phospho’s capabilities position your product launch to enable rapid iteration through data-driven decisions.

Sign up here to try using your data. It’s as easy as importing an Excel file or CSV file!