How to make sure your enterprise doesn’t miss the AI boat. (part 1)

From Watson to Tensor Flow to Neural Networks to Jon Connor, Artificial Intelligence is on practically every executive’s mind.

If it isn’t, it might be time to throw in the towel.

As the datasets available to corporations increase exponentially, computing power continues to explode and open libraries/tools like Watson and Tensor Flow gain popularity, deploying AI-driven solutions has gone from years and millions of dollars to months and affordability. Never before have engineers and subject matter experts been more equipped to capitalize on this shift.

Given those realities, we’re often asked why corporations with massive budgets have barely started deploying AI and machine learning technology internally. While the adoption challenges are vast across the Fortune 500 — disjointed data, lack of cloud-thinking, security fears, executive naivete and so on — the most common theme we see is much more fundamental. After a decade of trying (somewhat successfully) to adopt a ‘mobile-first’ & ‘cloud-first’ approach to solving problems, leadership teams have been caught by surprise and are struggling to transition to an ‘AI-first’ way of thinking.

It makes sense — how can we expect executive teams to tackle the clear barriers to adoption without a mindset rooted in the technology.

One of the easiest ways to begin thinking about your business as an AI-First operation is to start with low hanging fruit. Often I see product executives become overwhelmed with a high-level & overly scientific white-paper on the subject instead of starting with a clear internal problem and applying core principles of AI/machine learning.

Over the next 5 weeks, we’re going to dive into the following 5 applications for Watson in financial services to talk executive approach, implementation, technology stack and machine training strategy. If you’re lucky, I might even build you a POC or two. Check in every Wednesday, 11am and let’s go!
The subjects:

  1. Analyst Research Superpowers
    An AI driven assistant to pull articles, sentiment analysis and more for whatever industry or stock you want to learn about.
  2. Sales Chatbot
    Query your entire data universe and public data with a natural language interface to power your sales team.
  3. Compliance Automation
    Onboard customers and power internal compliance tools with natural language processing that learns in real-time.
  4. FinCen and Regulatory Automation
    Handle data requests with machine learning to automate the vast majority of your compliance requests.
  5. Internal Emotional Sentiment
    Analyze customer care and sales interactions for language sentiment to drive the next level of KPIs.

Each of those ideas are totally buildable (I promise, I’ve done it) by combining Watson tools, Watson Knowledge Studio and a bit of coding. Follow along over the next 5 weeks and leave comments if you want us to explore a different application (give me a week’s notice).

For the first time, getting a real machine learning product to market has never been as accessible— we should all start thinking about solutions given that reality and empower teams and leadership to do the same.

Remember — in the last 15 years, 52% of the Fortune 500 have disappeared.

AI is the next antidote. We got ya.