Synthesio recently published a new report – authored by its expert and CMO, Allen Bonde – called AI 101: Understanding Popular Artificial Intelligence Techniques and Top Tips for Putting Them to Work.
The report provides a brief history of AI (spoiler alert: AI is not a new topic, and has been through many boom-and-bust cycles over the past 70 years), and explores the many flavors of AI including “reasoning” and “learning” systems.
Yet, as Allen Bonde notes, today’s AI boom is driven by the convergence of a number of factors:
- An explosion of data from the Web and internet-connected devices.
- The broad availability of AI platforms.
- A new generation of engineers and programmers who grew up with tools like RStudio and Python.
- The emergence of “hybrid” approaches that make AI both more accurate and more scalable, and of course a focus on specific business use cases vs. trying to solve just “big” AI problems.
The boom has also partially been driven by the COVID-19 crisis. AI adoption has skyrocketed in recent years as companies turned to AI to improve efficiency, fuel digital innovation, and create new products and services. A recent study from PwC found that 52% of companies accelerated their AI adoption plans because of the COVID-19 pandemic, and 86% said that AI was becoming “mainstream technology” at their company last year.
So, it is time for data and insights teams, digital strategists, product planners, and even marketers to get on board. While there are countless ways to incorporate AI (like with a proven AICI platform), teams always benefit from starting small and looking for ways to apply AI to processes and data sources that bring out their value – for the broadest set of users.
Whether you’re just starting on your AI journey or scaling up programs, here are our top seven tips for putting AI to work (and a sneak preview of the new report):
- Sell value…not technology:
Sure, it’s fun to explore AI-powered techniques, but remember that most users don’t care about the tech - unless they are techies.
- Watch the scope:
Automating common, repetitive tasks offers a lot of initial value. So does processing and visualizing new data sets,and spotting patterns that could be an anomaly or even a trend.
- Apply Agile:
Especially with machine learning. The process of training is iterative! Also know that there are many flavors of learning systems as outlined, each suitable for the different levels of training data that you have - or don’t have.
- Get your data in order:
The best insights come from blending big data (like social) and small data (data in a volume and format that makes it accessible, informative and actionable - like surveys) to get the complete picture. Adding in search data can also give you unique insights on the path to purchase as well.
- Make sure there are human helpers at the ready:
Is your AI the coach or the player? Who is looking after bias and accuracy of results? Who can help you pick the right tool or platform? Who will set up initial models?
- See how/where you can embed insights:
If you are using a platform like Synthesio, this is about how different users will consume insights (via API, via reports, via an app, etc.).
- Target high value use cases:
For insights teams, it’s often about efficiency and accessing new unsolicited insights. For brands, it’s often about time to market. And don’t forget about innovation and customer experience (CX) as well!
To master “AI 101”, you can download the full report here.
Curious to see AI-enabled consumer intelligence in action? Request a demo with one of experts here.
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