The final directive to the assembled pre-teens at the first Monday caddy school was to “keep up!”  What our caddymaster Pat Higgins really meant was “stay ahead!” What golfer who has the privilege of taking a caddy wants to look for his own ball?  When I first started caddying I would lag behind my golfer when I wasn’t entirely sure where the ball had traveled thinking I would be forgiven for being a couple of steps slow but not for failing to follow the flight of the ball.  What I learned was they knew I didn’t see the ball and thought I was a couple of steps slow.  The lesson I learned was to get ahead of the game, be the first to arrive at the most likely landing area and furiously survey the area to increase the likelihood of being the one to discover the right answer.

I didn’t realize I was neglecting this lesson early in my CPA firm career until an audit manager said “make a decision and move on, we didn’t hire you because you know what to do every time, we hired you because we believed you were smart and will make good decisions about what to do!”  I struggle at times to do the work that allows me to keep up – listening to and learning from others (in formal training sessions and informal settings) and making time to read.  At a recent Ohio Society of CPA’s continuing education session the instructor (a former national firm partner) said something I made a point of writing down and capturing in the Evernote app on my phone – “Your only consistent value add is in decision making and that comes from the training and practice that informs your sound analytical thinking combined with your objectivity and judgement.”

By now we have all heard the buzz. Siri has plenty of company. Alexa and Google have invaded the home. Tesla’s artificial intelligence is, well, driving itself. Google’s Deep Mind is running the company’s multi-billion-dollar search business. Even they don’t know how it works — it just does. IBM’s Watson has its own office and is predicting cancer treatments for real patients. You can’t even date online without artificial intelligence (A.I.) hooking you up.

There are even A.I.-based autonomous ships, bots, games and investment and commerce platforms that are emerging. Eventually A.I. will be everywhere. Are you ready for the A.I. revolution? Of course, the answer is no one is ready, because this is only A.I. 1.0 — the beginning of time. So let’s review.

Artificial intelligence has finally emerged, and the prospects for it transforming the enterprise are excellent. Every enterprise should be looking at what blend of A.I. they want to investigate. From machine learning to neural nets, recommendation engines and decision bots — the landscape of the marketplace is full of choices. The real question I would pose is, “What should CIOs be telling their CEOs about the A.I. revolution?” The answer: “Show me the business strategy.”

I would challenge CIOs to work out the strategy question first. What is the business strategy that would benefit and even justify adding artificial intelligence? How could we generate a competitive advantage from investing in machine learning or automated decision support that A.I. could offer? There needs to be a business value or competitive advantage that investing in A.I. makes clear. If not, then you are not ready. Or don’t invest much until you can answer this strategic question.

Too many companies are so impressed with A.I. buzz that they are moving headlong into A.I. without clarifying what the business strategy case could be. Are we going to be able to generate better customer service? Help employees make better decisions? Enable our customer to use more of our products or services? How about fix that problem that those pesky humans don’t seem to be able to handle? How will we be able to monetize artificial intelligence?

Westboro, MA – February 13, 2017 – Trends Equity, Inc. has announced that AI World Conference & Expo 2017 – www.aiworld.com – will be held September 18-20, 2017 at the Boston Marriott Copley in Boston, MA. Now in its’ second year, AI World has become the industry’s largest event focused on the state of the practice of enterprise AI and machine learning. AI World’s mission is to enable enterprise business and technology executives to learn how to successfully harness intelligent technologies to build competitive advantage, drive new business opportunities and accelerate innovation efforts.

Which mainstay IT certifications should be in your list of credentials? What’s the next up-and-coming certification? It can all be very confusing. Global Knowledge Group recently published a survey identifying the top paying IT certifications in 2016. 6 of those Top 15 are in IT Security, which is reflective of what we’re seeing in the employment market. Information Technology is hot, and Security is extremely hot.
This article can help you answer these questions by providing a review of the 15 top-paying certifications. They provide a brief description of each, as well as the average salary that each certification commands based on the 2016 IT Skills and Salary Survey that Global Knowledge conducted in the fall of 2015.  Checkout the survey results:

http://blog.globalknowledge.com/2011/05/27/15-top-paying-it-certifications/

FROM THE HBR.ORG INSIGHT CENTER “FROM DATA TO ACTION” | 2© 2014 Harvard Business Publishing. All rights reserved.
WHAT TO ASK YOUR “NUMBERS PEOPLE” BY TOM DAVENPORT
If you’re a manager working with the analysts in your organization to make more data-driven business decisions, asking good questions should be one of your top priorities. Many managers fear that asking questions will make them appear unintelligent about quantitative matters. However, if you ask the right kinds of questions, you can both appear knowledgeable and advance the likelihood of a good decision outcome. In my new book (co-authored with Jinho Kim) Keeping Up with the Quants, and in a related article in this month’s HBR, we list a lot of possible questions for various stages of analysis. But in this short article, I thought it might be useful to mention not only a couple of the most important questions you can ask about data, but also what some of the ensuing dialogue might involve.
1. Questions about assumptions You ask: What are the assumptions behind the model you built? You think in response to the answer: If your numbers person says there are no particular assumptions, you should worry—because every model has assumptions behind it. It may be only that you’re assuming that the sample represents a population, or that the data gathered at a previous time are still representative of the current time. Follow-up: Is there any reason to believe that those assumptions are no longer valid? You think in response: You are really looking only for a thoughtful response here. The only way to know for sure about whether assumptions still hold is to do a different analysis on newly gathered data— which could be very expensive. Perhaps a particular relationship holds only when the values of a variable are moving in a particular direction (e.g., “This mortgage risk model holds true only when housing prices are going up”—nah, that could never change!).