Next, ask yourself how it ties into mission criticality. Be ready for these costs: engineers to work it, support to keep it running, and training specialists (data scientists / ML experts) to improve it. You must be reasonable about the logistics of making AI possible. Will I have to actually train the inner models in the process of tuning this solution?Īnd even if you know the answers to these questions, that same demo experience you saw may be untenable if you have to train the AI yourself.How much customization does it require to solve my problems?.Ok, this AI is good, but can I wield it?.So, how can you successfully navigate the world of AI? It starts with asking the right questions, things like: The learning curve is steeper than ever, and the stakes are even higher. Each layer adds more complexity, making your solution more expensive, brittle, and likely to fail.Īs chronicled through my journey as CEO of Clinc, I saw countless companies spend millions trying to create, configure, and train virtual assistants, only to fail. When the quality of your AI requires specific training to your use case, production-grade AI is extremely complex and often requires a dedicated team of experts in machine learning, computer and data science, and training specialists. The more niche and customized your use case is, the harder it will be to realize the AI quality demo’d into reality in your environment. You still have to adapt that AI for your use case, train it, deploy it, and improve it. How will you apply that solution to your needs?Ī promising demo isn’t everything. It can happen, right? Take a step back and think about the bigger picture. Maybe you beat the odds and found that perfect AI solution. Antidote 2 - You will have trouble with certain solutions: the training dilemma. Trust your senses, they will guide you through the noise. You have to look past the canned experiences, the lofty promises, and see what AI looks like in practice-within your industry or use case.Īnd if something sounds fake or unbelievable? It probably is. It only take a few minutes of interaction to tell if another human is intelligent, and similarly, you know right away if a conversational AI is intelligent from actually interacting with it. Throughout my experience creating novel conversational AI technologies, I know the power of an unforgettable experience. Trusting this type of intuition must be applied in all realms. They’re clear as you can actually see it in action and see it working well. These solutions use deep learning and reasoning to draw conclusions from billions of analyzed pixels. AI is being used in healthcare to detect breast cancer, in agriculture for crop yield forecasting, in autonomous driving to improve safety. Simple, purpose-built AI solutions have transformed many industries.
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