As an experienced entrepreneur who has worked with various innovations, I’ve seen my fair share of new technologies come and go. Nothing can potentially transform businesses more than artificial intelligence (AI). However, while AI offers immense opportunities, it does require severe investments of time, money, and effort to implement correctly. In this series of blog posts, I’ll share my insights on the costs and benefits of embracing AI in business based on my own experiences.
Many business leaders get starry-eyed when they hear buzzwords like “AI” and “machine learning.” They envision reducing costs, improving efficiencies, and boosting quality overnight with the snap of a finger. Unfortunately, this perception is misguided. The truth is that quality AI takes real investment. Like any technology, you must lay the proper data infrastructure, hire the right talent, and methodically train systems.
Rushing into AI with adequate funding and expertise is a recipe for success. Quality AI requires the same severe investments of time, money, and strategic planning as any other core business capability. There are no shortcuts. Failing to account for the actual costs and complexity of building successful AI is a recipe for disappointment.
For starters, AI is not a single “plug and play” software you can install and benefit from immediately. There are diverse technologies and approaches like machine learning, neural networks, natural language processing, robotic process automation, and more. Each requires specialized expertise to implement, manage and improve over time.
The costs start with hiring the right talent. You need data engineers to build pipelines, data scientists to derive insights, ML engineers to build models, and AI specialists to ensure optimal deployment and maintenance. For an enterprise AI team, expect minimum salary costs in the $300-500k range.
It would be best to have the proper data infrastructure in place. Machine learning models require massive amounts of quality, well-organized data to function correctly. Most companies must have their data protocols sufficiently streamlined, requiring significant investment to clean and consolidate data sources. This could involve aggregating siloed databases, building data lakes, removing errors, and standardizing taxonomies.
The data preparation alone can cost hundreds of thousands in engineering time. Then you must factor in the physical storage and computing costs as you scale up AI capabilities. High-performance GPU clusters are expensive but critical for complex model training.
After the underlying data foundations are implemented, the real work begins. The AI team must analyze use cases, build models, train them on test data, and continuously monitor and tune performance in production. Constructing custom deep-learning models is complex and time-consuming. It is typical for initial models to have poor accuracy, requiring many iterations and tweaks to reach optimal effectiveness.
And like any technology, AI systems require ongoing maintenance and upgrades. As new data comes in, models must be retrained to remain relevant. The AI space evolves rapidly, so strategies need to be revisited to leverage state-of-the-art techniques.
Add all this up, and the actual costs of quality AI become clear. For large companies, reaching AI maturity can cost tens of millions in talent, infrastructure, engineering, and platform costs. And it takes years of concerted effort, not months. Stakeholders must drop any notions of AI being quick, easy, or cheap.
AI is worth every effort.
Does this mean AI isn’t worth it? Of course not – implemented correctly, AI delivers immense strategic value that boosts competitiveness, increases sales, and optimizes operations. But business leaders need an accurate perspective. Treat AI as a long-term investment equivalent to other mission-critical capabilities. With patience and sustained commitment, the benefits absolutely outweigh the costs. But there are no shortcuts to AI excellence.
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