Globally, businesses are adapting to constantly changing technology trends by infusing new technologies into probably dated solutions/products/processes. This is broadly to achieve two main goals, one is to stay relevant through innovation and to increase revenue (or reduce costs) by automating and streamlining existing processes using AI. The last decade has seen an upsurge in the development and adoption of new technologies by businesses influenced by various factors like Covid 19, government regulations, dynamic customer behaviours etc. These changes are driving established companies to reimagine how they operate and also creating disruptive new start-ups.
In the UK, according to the government’s AI activity in UK business report, 68% of large companies, 34% of medium-sized companies, and 15% of small companies have implemented at least one AI technology. This indicates a huge opportunity for businesses to increase their revenue stream by upselling and/or cross-selling AI products across different company sizes and verticals. Additionally, according to the report, expenditure on AI technologies could increase to between £27.2 billion and £35.6 billion by 2025, at annual growth rates of roughly 10% and 16% respectively.
UK AI Adoption
While older businesses are adapting, new startups buoyed by VC investments are emerging, intending to get to market radically and aggressively. Investors are taking note of these dynamics and are pouring investments into start-up businesses keen to quickly adapt to these new market realities. According to reports by Flyer One Ventures, over 70% of Y Combinator’s investments between 2020 and 2023 went to AI/ML-themed start-ups. As a business keen to adapt and compete in a very tough and dynamic tech landscape, business leaders must have confidence in the purpose and value of their AI product offering as well as their customer readiness.
Regardless of the stage, your business is in its AI products journey (or the type of organisation you are), here are some of the benefits of building or adopting purpose-driven AI products:-
- Increased revenue opportunities
- Opportunity to upskill your employees
- Deprecate dated services
- Rebuild your tech platform with scalability, adaptability and resiliency at the core
- Quickly become compliant with changing legal requirements
- Expedite cloud adoption and enjoy the benefits of moving to the cloud
- Employee Retention and attraction
- Stronger brand value and awareness
- And many more
If your business operates in a regulated industry, you might want to read my last post on steps to implementing data-driven digital transformation for effective outcomes in regulated sectors.
Now we’ve seen some of the benefits of building AI products, how do we go about building it? Firstly, understanding why (purpose) you are embarking on building new AI features/capabilities will help the business review the associated risks and plan promptly. For companies with existing customers, care must be taken to not impact the user experience of existing customers. Customer segmentation, experimentation and seamless customer onboarding must be an integral part of your product development journey.
Things to think of when building AI products:
- Speak to customers and have customer validation for your AI product MVP. Have an early onboarding strategy and be quick to iterate based on customer feedback.
- Get clarity on compliance/legal requirements. Many jurisdictions are now enacting or having discussions around AI regulations. These discussions are fluid and must be tracked by the product and leadership team.
- ROI: as with every investment, you want to track your investment and the returns your AI capabilities would have contributed to your bottom line.
- Data source & quality: the quality of any AI product is based on the data (remember volume, variety, velocity and veracity). Garbage In garbage out. Avoid overfitting or under-fitting by ensuring you have a varied mix of training data. Any bias in your model may affect your reputation and revenue.
- Ethical considerations: set bounds and scope of your AI capabilities to your domain contexts. Train your models to give relevant responses that are within the scope of your product and gracefully reject inappropriate requests.
- Model performance & observability
Customers pay for value not volume (marketing hype)
Customers! Customers!! Customers!!!
The importance of speaking with customers cannot be overemphasised, Marty Cargan in his book Inspired, highlighted these key questions “Will the user buy this (or choose to use it)? Can the user figure out how to use this? Can our engineers build this? Can our stakeholders support this?” Do not commit to building AI products without understanding your customers’ appetite for it and the value your product will bring to them. Customers pay for value not volume (marketing hype). Some companies may decide to roll out capabilities (often in stealth) while expecting customers to come on board post-launch.
Conclusion
As a business, already building or about to build AI products, ensuring leadership sponsorship and buy-in is a must. Set an achievable scope for your MVP while clearly communicating the values it brings to your customers.