Tldr: Corporate AI failures can be ascribed to poor Intuition, Process, Systems, People
The promise of AI is real. We are at the crossroads of the next industrial revolution where AI is automating industrial processes and technologies that were hitherto considered state-of-the-art. AI is expected to create global commercial value of nearly USD 13 Trillion by 2030 (McKinsey Global Institute). Given the immense commercial value that AI can unlock, it is no surprise that businesses of all kinds and sizes have jumped on the AI bandwagon and are repositioning themselves as ‘AI-first’ or ‘AI-enabled.
However, the groundbreaking progress and transformation that AI has brought across industry belies the stark reality of an increasing number of failed AI projects, products and companies (e.g. IBM Watson, and many more).
How can startups and large enterprises battle these tough odds to drive innovation and digital transformation across the organization? In this blog, I will examine from first principles common themes that typically underlie failed AI projects in corporations, and questions business leaders and teams should address when embarking on AI projects.
I have classified these under four broad areas and will tackle each of these themes individually in future blog posts:
- Intuition (Why)
- Process/Culture (How)
- People (Who)
- Systems (What)
Part 1: Intuition (Why)
Commercial AI projects often fail due to a lack of organizational understanding of the utility of AI vis-a-vis the business problem(s) to be solved. More often than not, throwing a complex AI-based solution at a problem is not the right approach, where a simpler analytical or rule-based solution is sufficient to have things up and running. It is therefore paramount to decode the business problem first and ask whether an AI approach is the only and best way forward.
Unlike software engineering projects, the fundamental unit of AI is not lines of code, but code and data. In an enterprise, data typically belongs to a particular business domain, and is generated by the interaction of customers with specific business products or services.
2. Deep Learning in Self-Driving Cars
3. Generalization Technique for ML models
4. Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own)
Here, a customer-centric approach is critical to understand the context in which this data is generated so that AI models may be developed to predict or influence user behavior to meet well-defined business objectives with clear success criteria. Wherever possible, the data scientists should themselves use and experiment with their company’s products/services by donning a ‘customer’s hat’ to decode the customer mindset. It’s hard to understand the nuances of training data if you don’t intimately understand the customer ‘persona’ to begin with.
Data reflects more than just mere numbers. Making sense of data requires a holistic cross-functional understanding from a business, product, customer as well as technical perspective. Typically, these functional roles are played by different teams within a company, necessitating a strong collaborative effort to demystify the business problem, question the existing solutions and come up with new hypotheses, test and prove or disprove these hypotheses quickly via iterative experiments to hone in on a feasible solution and strategy.
Here, the importance of domain knowledge or subject matter expertise cannot be stressed enough. It takes years to gain deep domain expertise which enables practitioners to develop better intuition for the business problem and the underlying data to propose feasible solutions or strategies.
As data scientists typically lack expertise in business domains, it is imperative they complement their algorithmic data science skills with expert knowledge from those who work closely with the customer and understand the business problem intimately.
Tldr (Part 1/4):
Ask why is AI needed for your business problem? Is it the only way to solve the problem? And if yes, build and test hypotheses by leveraging the collective organizational knowledge and intuition across cross-functional teams that specialize in data science, business, product, operations.