Why take the risk management approach?

Balancing risk and reward

Contrary to popular belief, risk management doesn't mean slowing down and saying "no" to everything.

It means learning how to place smarter bets and steering clear of pitfalls so you can improve your chances of winning.  It can reduce your exposure to problems while leaving you open to the potential upside.  You'll move faster and experience fewer upsets along the way.

When you apply a mindset of risk management and risk-taking to your AI efforts, you can improve the whole spectrum of activity: strategy development, project planning, product management, hiring, vendor evaluation, and due diligence procedures. All while limiting your exposure to project failures, reputation problems, and ethics issues. That makes your company more attractive to prospective customers and potential hires. And also to investors.

Risk-thinking in AI: your guiding light

If AI is a dark road, then a risk management approach lights the way for:

• Company leadership: Vendor pitch materials and press success stories vastly oversimplify AI and paint too rosy of a picture. Understand what AI can (and cannot) really do and see how to put your company's data to good use. Establish controls and policies to prevent costly PR mishaps.
• Product management: AI requires a shift in how you design and implement products. Create AI products and embed AI into products, in a way that will avoid embarrassing and expensive problems down the road.
• Project execution: Compared to traditional software projects, AI projects exhibit a special set of pitfalls and failure modes. Learn to steer clear of those problems so you can make the most of your effort and reduce your time-to-market.
• Investors: Sadly, there's no shortage of fraud in the startup scene. The hype around AI gives startups an extra smokescreen to cover bad practices. Get extra eyes for your due diligence efforts before investing in or acquiring that startup.

Who's behind this?

I’m long-time AI consultant, researcher, and published author Q McCallum.

I’ve been in this field since the terms "predictive analytics" and “Big Data” were still getting traction. I've written books and papers on the intersection of AI and business. I've discussed AI strategy with the C-suite, developed and deployed AI models at the tactical level, and worked closely with product teams on embedding AI in their company's offerings.

In short: I have taken companies from zero to AI and beyond.

I've also seen what happens when AI goes wrong. Some companies leave their entire AI operation to chance, or throw away money while chasing an ill-defined AI dream. Others sit on the sidelines and missed the opportunities AI has to offer. And then you have the companies that plow ahead, ignoring potential dangers that eventually come back to haunt them.

My take? Risk management is the solution to this field's biggest problems.

Don’t believe me? Ask successful traders, lenders, and insurers how they ultimately make their money. They'll tell you that risk management is a key ingredient. It's how they place smart bets and hedge against potential problems.

It's time we brought that same mindset to AI.

How do we begin?

Do you want to see around corners and place smarter bets in AI?

Are you ready for experienced guidance to see you through your AI transformation and AI strategy?

Then we should talk.

You can browse the list of services, or reach out to get started.