AI initiatives often come with great promise, but also high costs and uncertain returns. How can businesses ensure their AI experiments deliver measurable value? The key lies in a strategic approach to maximize ROI.
Why AI experimentation matters so much for business growth? Nowadays, artificial Intelligence is a business imperative. All industries are seeking opportunities in investing in AI to enhance operations, drive revenue, and gain a competitive edge.
Yet, the success stories are a bit rare, no matter how much experimentation is already happening.
Understanding the Costs and Benefits of AI Investments
AI experiments require investments in talent, working hours, data, infrastructure, and integration. Many companies struggle to justify these costs without clear success metrics. It is understandable – an AI initiative makes no exception in the business actions which need careful planning to succeed.
The benefits, however, can be immense: increased efficiency, better researched base on your decision-making, and enhanced customer experiences. To optimize ROI, businesses must balance these costs with expected gains, ensuring that AI initiatives align with strategic goals.
How to find the right strategic goals?
Teamit is an experienced partner for finding the correct areas. The process is in use both in-house and with our clients.
We’re eating our own dog food in the form of AIExp, Teamit’s AI experimenting team. The team works in one-week sprints building understanding of various ways of leveraging AI, from personal workflows to business-wide process assistants.
Customer feedback insight, workshops, technical spikes and no- or low-code prototypes are important phases when sharpening the vision about what is the right thing to build.
With GenAI it’s easier than ever to do research preparing the strategic work.
First, A Few Caveats
General Market Effect
Global phenomena can affect many KPIs when calculating ROI. In the planning phase, take in the consideration what trends are affecting the operations and what changes would take place anyway.
Right KPIs Aren’t Always Easy to Find
That also It’s easy to use numbers already easily available. It’s also easy to things that would have happened anyway. In the process, it can be helpful to focus on creating a certain amount of experiments for example, as it’s often difficult to say whether the experiment leads to something or not.
Don’t Tread on Creativity
Data can tell you an idea didn’t work. But what if it just hasn’t been done right yet, or not at the right time? There should be room to ideate freely. The most obvious things have been tried and possibly put in use already.
Building a Data-Driven AI Experimentation Strategy
What to actually do then? Here’s a solid list of our learnings so far. To achieve high ROI, companies must structure their AI experiments effectively:
Define Clear Objectives
Establish what success looks like in terms of business impact. Involve enough stakeholders and learn what is in their mind. Mapping workflows is a great excercise to find relevant cases and cluster them to see the most valuable spots.
Select High-Impact Use Cases
Prioritizing AI applications can be difficult if the goals are not clear. Are there any time-consuming and repetitive tasks that could benefit from automation? Are there any clusters forming in customer feedback? Inefficiencies and bottlenecks in your processes? Or are there any quick wins in sight? A priorization matrix can be helpful, analyzing potential business impact, the complexity of the implementation, strategic fit, data readiness level and overall resource availability.
Ensure Data Quality
AI models are only as good as the data they learn from. Clean, well-structured data is essential. But what is clean, well-structured data, depends on the case. It involves anonymization, removing inaccuracies, fixing inconsistencies, and handling missing values. Gen AI solutions are a bit more forgiving regarding data than analytical AI.
Monitor Performance Metrics
Use KPIs like efficiency gains to assess AI effectiveness. Maybe the use of AI even opens up new monitoring opportunities? With AI several workflows can be much more effective in the preparation phase and provide a better result thanks to that.
Iterate and Scale
Start with pilot projects, analyze results, refine models, and scale successful experiments. Great ROI often doesn’t lie in the result you’d get by clicking on a button, but instead in augmenting human work phases.
Turn AI Insights into Tangible Business Value
The success of AI experiments depends on how well insights are translated into action. Businesses should focus on:
- Enhancing processes with larger research and faster responses
- Enhancing customer engagement with AI-driven personalization
- Reducing operational costs by optimizing workflows
- Driving innovation with AI-powered product development
With Gen AI, a technical spike often is the first reach out towards new process or solution. If it’s not technically feasible, it’s not worth it to invest any design work into it.
However, after a succesful spike, it’s time to develop a MVP and then test rigorously. End user research is necessary in case the solution has something to do with user touchpoints. Additionally, the development costs have to be considered.
The minimum timeframe for this is about three months. Several tracks should be going on simultaneously. If something turns out not to be technically feasible, not desirable to users, or not productive business-wise, it’s time to put that aside.
Future-Proofing AI Investments
For sustainable AI-driven growth, companies should adopt a long-term mindset. This includes fostering an AI-ready culture, investing in ongoing training, and staying updated with AI advancements. Collecting and preparing data can take a long time.
Leveraging AI potentially also brings a lot of change in human workflows. Training people should’t be overlooked. The most difficult thing in change is when you should change your own behavior. For that support and guidance is often needed.
Moreover, ethical AI practices and compliance with data regulations will be crucial in maintaining trust and maximizing AI’s potential.
Start Experimenting and Learning About AI Today
AI experimentation is an investment, not an expense. Each company has their own unique combination of processes, people, and data. With the right strategy, it’s possible to unlock significant value, transforming data into actionable insights and automation into efficiency.
Want to optimize your AI investments? Contact us today to accelerate your AI journey.
