When making a strategic cloud decision, organizations can follow either one of two ideologies: open or closed.

In the past, major software technologies have been widely accepted because an emerging market leader simplified the initial adoption.  After a technology comes of age, the industry spawns open alternatives that provide choice and flexibility, and the result is an open alternative that quickly gains traction and most often outstrips the capabilities of its proprietary predecessor.

After an organization invests significantly in a technology, the complexity and effort required steering a given workload onto a new system or platform is, in most cases, significant. Switching outlays, shifting to updated or new software/hardware platforms, and the accompanying risks may lead to the ubiquitousness of large, monolithic and complex ERP systems – reason not being that they offer the best value for an organization, but rather because shifting to anything else is simply – unthinkable.

There’s no denying that these are critical considerations today since a substantial number of organizations are making their first jump into the cloud and making preparations for the upsetting shift in how IT is delivered to both internal and external clientele. Early adopters are aware of the fact that the innovation brought about by open technologies can bring dramatic change, and hence are realizing how crucial it is to be able to chart their own destiny.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

No matter what type of business you’re in, boosting your bottom line is always in the back of your mind. In a rough economy, it can be tempting to focus too much on pulling in more money and not enough on containing it from within.

That’s right, containing it. You have the potential to lose hundreds of thousands of dollars every year right under your nose from something as simple as lack of or ineffective computer training.

How much do the employees you have working for you right now really understand about technology? How good is your IT tech department? Technology changes faster than a blink of an eye and oftentimes, businesses struggle to keep their top employees trained.

With millions of dollars being lost to Internet espionage, file corruption and other computer crimes, staying on top of changes is essential. Recently, online learning centers have been emerging as the go-to method for quick and inexpensive learning. Unfortunately, this type of computer learning isn’t always the best solution.

Is it possible for anyone to give Microsoft a fair trial? The first half of 2012 is in the history books. Yet the firm still cannot seem to shake the public opinion as The Evil Empire that produces crap code.

I am in a unique position. I joined the orbit of Microsoft in 1973 after the Army decided it didn't need photographers flying around in helicopters in Vietnam anymore. I was sent to Fort Lewis and assigned to 9th Finance because I had a smattering of knowledge about computers. And the Army was going to a computerized payroll system.

Bill and Paul used the University of Washington's VAX PDP computer to create BASIC for the Altair computer. Certainly laughable by today's standards, it is the very roots of the home computer.

Microsoft became successful because it delivered what people wanted.