So, is AI a solution in search of a problem or precisely the solution to your needs? It depends on the execution.
Transforming into a genuinely AI-capable organization takes more than demonstrating with technology. A successful AI evolution requires aligned leadership and business strategy, cross-functional collaboration, superior data engineering, and continuous learning.
Let’s dig deeper into how to make AI work for you: How to approach it, develop your organization’s capabilities, and develop a solution.
How to approach AI – if you want to produce value
First, let’s dig into who and what needs to be involved – if you want to create value with AI, that is. Creating business value from data and AI requires taking into account 3 distinct cornerstones:
Business & Processes
- Business target setting
- Data-driven actions
- Lean decision making
- Cross-organizational transparency
Data Engineering
- Infrastructure
- Data collection, processing, and storing models
- Data lake
- Information Architecture
- Data transparency
Artificial intelligence
- Prediction models
- Recommendation engines
- Dynamic pricing
- Natural language processing
- Machine vision, speech recognition
Instead of partial optimization and silos, the key is to work iteratively end-to-end on all three.
How to build an AI capability in your organization
Finding the sweet spot between what's possible with modern technology and what makes sense for your business is challenging. With so many possible paths forward and a world full of seemingly outstanding solutions, where and how do you start?
Our answer: In smart phases that build the foundation for success. Delivering business impact with AI takes superior software development, service design, digital strategy, and organizational transformation.
How to develop an AI solution
An excellent AI solution development process is similar to any great software development process. With the proper scope, you can move from an initial idea into production in a matter of months. Once you have a clear vision of the business outcome and target, prototype and validate fast. Afterward, move on to demos with all relevant stakeholders and develop the production version. If everything goes well, you can then move on to continuous development.
At Reaktor, we always recommend working with multidisciplinary teams. Here, too, you will benefit from working with a team of service designers, UX designers, data scientists and AI specialists, and software developers.
Data, AI & LLM Solutions
How Reaktor can help you with AI
From generative AI to data strategy and custom models, we help you build transformative AI solutions with value-first approach.
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