.png)
Artificial intelligence has dramatically elevated the importance of offering products that meet customer expectations. The same AI is also pivotal in ensuring that these expectations are met.
Consequently, my colleague, IT & Product Management Professional Dan Lucas, and I decided this was a good time to review the impact of AI on Product Development and Promotion processes. Having been involved in developing SAAS products throughout our careers, Dan and I were both keen to analyze how we could do things differently from now on . . .
So let’s explore the 7 stages of product development, both with and without AI support.
Idea-generation techniques typically include SWOT, cost/benefit analysis, employee brainstorming, and mind mapping.
In design thinking, participants utilize multiple ideation methods to generate as many ideas as possible, thus promoting communication, critical thinking, and creativity among stakeholders.
As professionals in the modern business environment, this is how we are used to generating ideas. Whether triggered by brainstorming, mind mapping, SCAMPER, or reverse thinking, ideas are vital for exploring new opportunities and improving existing products and services to maintain competitive advantage.
Working in digital marketing and developing associated software products, we devote much of our idea generation to facilitating best practices via digital tools and online spaces for anyone wanting to generate ideas to provide a structured environment in which to capture them (e.g., integrating products like Trello, Miro, and Jira) to foster and amplify online collaboration and capture important ideation-related data.
A major restraint on idea-generating teams, especially start-ups with stretched resources, has always been time. Now, however, advances in AI see idea generation accelerating exponentially. The emergence of AI language models as a scalable tool for mass-market use has also brought significant opportunities, as has the explosion of idea generation-specific AI products following companies such as OpenAI making their models available via APIs.
Deep learning models enable Product Teams to increase the potential of their ideas, virtually eliminating the gap between conception and visualization and leading to off-the-shelf products, such as DALL-E and Stable Diffusion.
That said, all may not be as seamless as it seems because generating ideas with AI tools relies heavily on a vital human trait: communication. Conveying complex ideas clearly to peers and stakeholders is an important skill; however, when interacting with AI models, this skill must be honed to a whole new level. The way we prompt the model or give instructions is fast becoming a field of study in its own right. At the time of writing, a quick Google search for GPT-4 prompts returned over 6.5 million results, and it’s widely predicted that prompt engineering may be one of a slew of new specializations created by the rise of AI. However, as the rapid pace of development continues, even prompt engineering risks being overtaken by yet more advanced ways of interacting with AI products.
Idea Generation Products: Taskade, Seenapse.ai
When using team exercises and AI prompts to generate as many ideas as possible, you need idea screening to eliminate anything unconnected with your overall business objectives. Screening is core to any new product development because—of the masses of data generated by a multiple-technique approach—you only need ideas suitable for your customers, products, and business.
Some ideas will be new to the business; others may improve existing products or services. When screening for the latter, emerging AI products also bring efficiencies in the final stages of the product development lifecycle when evaluating existing company datasets, such as customer feedback and user data.
When screening, you may use qualitative and quantitative research. Qualitative techniques, like surveys and focus groups, will gather unbiased data and testimonials about existing products. AI tools can help data collectors and evaluators save time by categorizing responses and sorting them by qualitative categories such as positive, negative, or neutral.
Besides its time-saving benefits, AI can enhance idea selection by removing our unconscious human biases and inability to give all ideas our full attention. Using new AI tools at this stage could significantly improve efficiency and objectivity. This is all the more exciting when you consider that some new AI products claim they can automatically capture some of the hard data used to evaluate ideas, such as market research and competitor analysis information.
New AI products have emerged, claiming they can assist in evaluating new business ideas. Check My Idea-IA, for example, aims to analyze business ideas by utilizing AI to validate against market trends, competition, and other variables.
Once you finalize your idea and monetization strategy, you move into the actual build and design stage. Good UI/UX design can transform your product into an addictive /experience that customers can't resist. The way users interact with your service directly impacts conversion and retention rates.
With AI, you can easily integrate the following:
When translating ideas and user requirements into future product specifications, product teams often write user stories, product descriptions, software specifications, and use cases based on the laborious, time-consuming construction, by product managers, of clear, concise statements—a tiresome task that’s tailor-made for the strengths of generative AI.
Let’s take another key stage in digital product development—wireframing. Emerging AI products, such as Uizard, allow users of all product abilities to generate interactive prototypes for web and mobile applications faster than ever before. FigmaAI also utilizes Open AI’s ChatGPT, integrating it into existing wireframing tool Figma for AI-assisted wireframes, logos, and assets.
As teams get used to working with AI in their lifecycle processes, they’ll be able to automate stages within it by deploying templates and historical data that their AI products will then learn from.
Multiple no-code AI products currently cropping up include several that help you with wireframes, prototypes, and assets for products:
Midjourney - asset generation (images)
Khroma - color palette generation and relevant assets
Adobe Firefly - AI-enabled software embedded in Photoshop for asset creation (not yet available for commercial projects)
Google’s AutoDraw - asset creation, especially icons for digital products
BlendAI - product image generation
Visily - software wireframes from hand-drawn sketches, app screenshots, and built-in templates
Fronty - An AI-Powered tool for creating websites in a few minutes by converting images to HTML and CSS code
AI opens up tremendous product possibilities and brings significant marketing implications. Overall, product marketing is responsible for the voice of the customer, positioning, messaging, and product adoption. AI-powered marketing tools can provide detailed insights and data-driven recommendations. Businesses can make more informed decisions about their marketing spend, leading to better results.
AI tools enable us to:
Marketing & Content Products:
Crayon - a competitive intelligence platform
Cohesive - AI editor
LogoAI - AI-driven engine processes logo information, design standards, and brand development guidelines
Jasper - AI writer and AI art generator
When developing your product for launch, several supplementary products allow you to optimize workflows and improve your technical team’s efficiency.
Software such as GitHub’s Co-Pilot provides developers with supporting functionality to help them develop technical products. Powered by OpenAI’s GPT-4, Co-Pilot aims to integrate with every aspect of your development team’s technical coding workflows.
A key feature is the inclusion of integrated AI-assisted conversations that help developers understand code blocks and explain and fix error messages in your codebase. Other additional features include advanced AI-assisted unit tests and pull requests. These AI-assisted technical products should reduce the overall development time while speeding up technical workflows.
Our experience working with startups and their very restricted resources suggests that developments in AI that augment and enhance time-intensive tasks will be a game-changer. For example, senior developers in a startup environment can often be stretched. When small companies begin to scale, these developers must wear several hats, adopting the roles of mentor, regular developer, and product owner. These advances in AI-assisted software development tools will likely enable junior staff members to self-serve and progress with less reliance on the help of senior staff.
However, like any development of technical capability within a business, introducing AI or ML models to improve processes calls for clear direction and a skilled team. The emergence of AI-assisted programs and products that augment and enhance business processes through existing SaaS offerings allows smaller businesses and start-ups to unlock significant efficiencies but only when backed up by clear leadership and adoption strategies.
Product Development tools:
GitHub’s Co-Pilot - software development assistant
Visual Eyes - AI-Assisted product for digital product testing
Mintlify - AI-assisted documentation for developers
Jam - AI-assisted debugger
Product photos, a promo video, and a landing page have always been considered the bare minimum for a successful test campaign. But now, thanks to AI-assisted programs, testing campaigns have evolved.
Leveraging the power of ChatGPT can also optimize customer support operations. Automating the handling of repetitive inquiries can enhance the response times of your marketing and customer support teams and elevate customer satisfaction.
Additional Marketing Products:
PromoNavi - online ads automation
Hubspot SEO - several AI tools that help you create optimized content
Copy.ai - AI-powered copywriter
Zapier - AI-powered Zap builder
AdCreative - AI-powered ad creation tool that helps brands create higher-performing ads
So here we are at the end of the cycle. Launching a product is a big achievement for any company. You’ve worked together to bring something to market that meets and hopefully exceeds customer requirements. It’s an exciting time for teams and companies and should be celebrated, but the hard work isn’t finished yet. The launch is a critical step, mainly because it’s the first time your customers will interact with your product for real.
Our experience monitoring product launches has included hours of manual work. Implementing feedback touchpoints for users, such as product ticketing systems, helps capture feedback, but the data is still very time-consuming to review, analyze, sort, and prioritize. It is also an example of where feedback can be captured. Product development is very iterative in its approach. As the wider business environment evolves, we will also have internal stakeholders, such as senior management and sales teams, providing their own ideas and feedback as they pass on client feedback from their calls and demos.
AI advances have made it easier for teams to analyze historical customer data and beyond. Understanding areas such as previous product launches, sales data, and wider market trends have always been important for product teams, but with AI, the data can be harnessed much faster and more effectively, bringing efficiencies and leaving product managers with more time to make critical decisions that benefit stakeholders.
When considering future developments that may go into newly launched products, AI can assist product teams in prioritizing their feature roadmaps for future releases. This will be effective for product teams when removing unconscious bias and meeting pressing customer needs.
Feedback product tools:
Collato - AI-driven tool for collating data from feedback sources such as customer support requests
Mixpanel - uses AI to gather insights based on user behavior, demographics, account types, and more
Chisel - allows teams to gather feedback from customers within their products via surveys and other tools
Helpshift - AI-powered in-app feedback and customer service tools
AI is revolutionizing how startups approach branding, and this trend is set to skyrocket as we roll further into 2023. By harnessing the power of automated audience feedback, data-based copywriting, and personalized customer content and experiences, startups can establish a powerful brand, make a mark in a competitive market, and cement their position as industry leaders.
Ultimately, AI is another in a long history of innovations in the product management ecosystem. As members of teams working within the lifecycle, from marketing to software development, AI is to be studied, understood, and tested to see if its various uses suit your specific business goals. Senior leaders must not be distracted by the hype but instead recognize AI as another tool in the toolbox, albeit a very powerful one. Trust your skilled team members to understand this latest development and how it may inform their workflows and augment their existing skills.
If you have any questions or ideas to boost your marketing campaign with innovative technology, please get in touch with us.
and see how we can make your marketing a sucessful journey
