machine learning

Less than 25% of companies have incorporated AI into their product or service, there’s clearly still no simple answer. to assist steel oneself against the inevitable point where AI and machine learning are not any longer a differentiator but rather, par for the course, here we layout steps you ought to fancy embrace AI and machine learning.

AI and machine learning aren’t a sci-fi dream. They’re here, and that they are for a short time.

However, up until recently, AI and machine learning lived within the background, baked into systems that few touched and fewer understood. Not only did it require enormous amounts of computing power (which nobody had) but it also required vast fields of knowledge to reap and learn from.

But now AI and machine learning are here. the mixture of enormous amounts of computing power available to literally everyone for nearly pennies, and therefore the vast amount of knowledge that’s generated, captured, and arranged via smart phones has created an ideal storm for AI.

And this trend continues.

The Internet of Things, Smart Homes, Smart Cities, the economic Internet of Things, and edge computing are tributaries to a very gargantuan data lake, which can then be mined to fuel machine learning, in term making AI outcomes a reality. In fact, that very data, and therefore the AI that processes it, is arguably the competitive difference that sits at the guts of Amazon, Google, Facebook, and Netflix’s runaway successes.

But that’s all old news. The increase of AI isn’t only well documented, but firmly understood at every level in businesses and organizations.

So the question naturally turns to how. Yes, Google, Amazon, and Facebook can turn that data lake into insights and, eventually, money. But how do other organizations, organizations not rooted in surveillance capitalism, maximize these innovations?

And, as long as 25% of companies have incorporated AI into their product / service, there’s clearly still no simple answer.

To help steel oneself against the inevitable point where AI and machine learning are not any longer a differentiator but rather, par for the course, here are our top 5 recommendations you ought to do to embrace AI and machine learning.


  1. Organize your data

The #1 thing you’ll do from a technical level is organize your data. Which means understanding the state of your data now.

Unstructured data is data in your organization that machines can’t easily parse. Think PDFs or videos sitting on a tough drive somewhere.

Structured data is data that’s organized and searchable by machine. sites exposure in Google search results are the simplest example of structured data. Machines can “read” (e.g. crawl) the page to know what it’s about and surface the proper content because it’s during a format designed for machines to try to do that.

Another, internal example of this is looking for numbers during a spreadsheet. The numbers are formatted in order that machines can search once you key within the figures you’re trying to find .

Siloed is when different parts of your organization all access different information, or worse, different copies of an equivalent data. An honest example of this is often when an enterprise organization has multiple instances of a database, like CRM or a product data management tool (PDM) across multiple locations or divisions.

Aggregated data is when all of your data feeds into a central repository and is pulled out as required, which suggests that machines can pull data from anywhere across your org to use in algorithms.

For machine learning and AI to figure , you would like the maximum amount of your data to be aggregated and structured.

  • Structured data gives AI and machine learning something to figure with
  • Aggregated data will give AI the potential to uncover insights that you simply can’t find manually
  • Structured, aggregated data creates the quantity of knowledge needed to truly run machine learning.

Therefore, organizations should start working towards the info lake end point by investing in integrated solutions, data scientists and practitioners, and sound data collection and governance.


  1. Establish clear data protection processes and practices

Consumer, client, and member data is a precious commodity. Quite that, personal data of any kind may be a grave responsibility that organizations must manage with care. Not only does one have to manage data with care, but store, use, and leverage with care across their own and purchased tools / solutions.

The reason this is often important is twofold.

First, consumers are less and fewer tolerant of privacy breaches. What’s more, the privacy breaches that do happen are having a bigger and bigger impact on brand value and consumer trust.

Second, the more data you aggregate, the more valuable it becomes for your organization… but the more valuable it’s for others to focus on and steal. At an equivalent time, the aggregation, structuring, and data exposure needed for AI and machine learning are equivalent forces that make vulnerabilities for your organization.

Fortunately, there are steps you’ll take now to urge before potential data problems.

Build out a knowledge protection team: Start building out your data protection and governance team, who will set policies, enforce decisions, and manage the structure of knowledge , including establishing and enforcing best practices like limited access, data deletion, password management, and more.

Make security a part of your culture: The reality is, most security breaches are thanks to the humans at the guts of the operation. For instance, a hacker can either attempt to crack a randomly generated, 99-digit password for months, or they will be placed on high-vis vest, stroll into your office, and just start doing work on a station, claiming “they’re from head office.’ That second option may be a lot easier. Therefore, to organize for AI, organizations have to think twice about building a culture of security, where people hallenge things that do not seem right, treat security as a core part of their value, increase the organization, and train regularly to know and manage meeting threats.


  1. Build an iterative culture

So far, we’ve talked about AI and machine learning like it’s getting to happen.

The reality, though, is that AI isn’t just getting to happen.

It’s really, really hard. It takes an extended time. And failure and rejection are an enormous and (often) necessary part of the work .

Therefore, you would like to create a culture of iteration, trial and error, and of failure so as to leverage AI and machine learning.

And this isn’t just at the engineering level. This is often at every level of your organization, from ICs all the thanks to executives.


  1. Set expectations on your outcomes

If you’re a business, then you would like to make sure that everybody understands what AI means for your organization.

And, even as pertinent, what it doesn’t.

For example, let’s say you create a software package , and you’re getting to embed an AI engine into it to power a recommendation engine.


AI goes to form your product better, and (hopefully) you’ll make extra money .

But here’s what AI won’t address:

  • A product that doesn’t solve a true problem
  • A go-to-market strategy that doesn’t have a targeted audience
  • A product that doesn’t have product/market fit

AI and machine learning will make most products and services better. However, they won’t resolve underlying business or organizational problems.


  1. AI / machine learning isn’t s solution

This isn’t such a lot of something to organize for, but it’s something to stay in mind. Because AI and machine learning still have a little bit of sci-fi shine, it’s important to know that neither of them is going to be a solution for your product, service, organization, or business. Unless you’re an AI developer who’s entire service is developing AI, then it’s only ever getting to be a component of what you deliver.

What’s more, AI will increasingly be an unsexy piece of our product, solution, or service, more just like the wiring within the car instead of the shiny rims that get you excited.

Ultimately, consumers, members, and businesses don’t care about your AI or machine learning functions — they’re much too curious about what they get, when AI is basically how they catch on .

Therefore, it’s important to treat AI and machine learning as what they’re — extremely powerful and useful product features. Your business or organization will have to stand on its own, which suggests that you simply must resist the temptation to place all of your eggs within the AI basket.



AI and machine learning are here. Not as an idea , or a thought , but here as a reality. Granted, it’s thin on the bottom, especially once you progress down from enterprise organizations.

That means there’s still a touch time to know AI and cash in of it once you see an opportunity:

  1. Organize, aggregate, structure, and optimize your data.
  2. Establish strict data protection and governance policies for your organization.
  3. Build an iterative culture and a culture of failure from rock bottom of the org to the very top.
  4. Set expectations for what AI can and may not do.
  5. AI isn’t a solution .

In order to achieve success , AI and machine learning are getting to be a part of the story — but not the entire thing. So don’t be frightened of AI and machine learning. By moving now, you’ll still be an early adopter of subsequent waves of technology and innovation.