Navigating AI Adoption: Understanding the Context for Your Business
As we step into an era where AI adoption is swiftly becoming the norm, understanding how to navigate this transformative technology is crucial. According to IBM’s report on global AI adoption in 2022, 35% of companies have already integrated AI into their operations, and an additional 42% are actively exploring its potential. AI is not merely a trend; it's a game-changer. To harness its technological benefits while avoiding unexpected pitfalls, it's essential to choose the right AI adoption path for your business by understanding the context.
Leveraging its extensive experience in AI projects, Komodo has distilled four typical business contexts that require different levels of AI adoption. What’s worth noting is that the context is not company-wide. Different departments at the company or teams from different departments would have different contexts and thus need to consider AI adoption differently.
The Solution R&D Context:
Companies or teams that need reactive adaptation to changing business needs, to enhance competitiveness for business growth and to protect against external disruptions fall into this context. Imagine an e-commerce startup that acquires a recommendation algorithm from a third party instead of developing its own. Initially, the algorithm boosts sales by providing accurate recommendations. However, the hazard emerges when the third party changes data policies or updates the algorithm, impacting the e-commerce company's seller and customer experiences and the business performance overall. An accurate recommendation system that fits the specific customers and seller personas is a key solution the company should own. Rather than relying on rental AI models, the company should own its own AI solution.
The Architecture Context:
Companies or teams that rely on extracting insights from vast datasets as the core values to help inform business growth fall into this context. Ownership of the AI model ensures control over data quality and model accuracy, which determine the insights quality. Imagine a company offering economic trend insights as its core business value yet gather the insights from a third-party dashboard. Without control of the data nor the AI model, such a dependency on the third-party solutions would expose businesses to risks when changes occur. If the companies or the teams rely on the AI model to get authentic and competitive insights, owning the AI components is essential.
The Optimization Context:
Companies or teams that prioritizes operational efficiency fall into this context. They can rent sophisticated AI models for tasks like human resources and finance accounting. While renting offers cost-effectiveness, it's vital to maintain a watchful eye on costs to avoid budget overruns. The optimization path strikes a balance between rental and ROI, ensuring cost-effectiveness in AI adoption.
The Insight & Decision Making Context:
For companies or teams that just need to access data insights for better decision making for common purposes such as marketing campaign performance monitoring or customer habits understanding, they can adopt AI that allow flexible insights formations that achieve better ROI than developing its own.
