How to Invest in AI
How to Invest in AI
It’s important to understand more about this technology and how to implement it quickly and cost-effectively.
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Improved agility, better customer experience, reduced costs. These are the top three benefits fueling organizations’ sustained interest in artificial intelligence and machine learning.
If your organization is sitting on data, you’re well-positioned to leverage AI by introducing machine learning into your processes and systems. However, AI and ML are the latest IT buzzwords that can easily be dismissed for fear of being introduced for the sake of it or with the wrong intention.
That’s why it’s important to understand more about this technology and how to implement it quickly and cost-effectively. So you can put in the foundations for technology that will soon be ubiquitous, improve your operations, and gain competitive advantage.
AI, ML, and Deep Learning: How Do They Fit Together?
AI is the all-encompassing term used to express the idea of incorporating human intelligence into machines. This includes all aspects of the notion, from the broad, highly advanced concept of almost-human robots to deep data analysis.
The difference between machine learning and other forms of software? Most processes are controlled by software that’s based on engines made up of rules and instructions. Machine learning is a set of techniques that can be used to analyze patterns in data and perform predictions.
Machine learning is a subset of AI that enables computers to apply learning algorithms (usually created by data scientists) to data to arrive at accurate predictions.
Deep learning (DL) is the evolution of ML that aims to replicate the human brain’s method of problem-solving. Instead of being fed pre-labeled data, DL uses patterns in the data to label and categorize data on its own. To do accurately, DL requires enormous amounts of data and significant training.
What’s the Best Use of ML Right Now?
- To solve problems involving large amounts of data and processes that cannot be handled by other systems, particularly when there’s no clear path between the data and the outcome
- In situations where the correlation between the data and the outcome is too complex or time consuming for human analysis.
- To provide supporting information for decision making.
Examples include event prediction, trend analysis, data classification, and decision automation. ML can even be applied to create computer vision which can be used to carry out traditionally human tasks like visually checking items for issues.
Breakthroughs in other areas of AI, like natural language understanding (NLU) and natural language processing (NLP), are also shaping online customer service. The days of “press one for sales” are coming to an end. And voice recognition is so advanced that soon we won’t be able to tell the difference between a computer and a person at the other end of the phone.
One of the major benefits of ML is that it can analyze entire data sets rather than extrapolating and applying results from a smaller amount of information and hoping it’s correct. This results in more accurate analysis and enables businesses to incorporate real-time data into models and decision making.
Where Are AI and ML Headed?
While humans are fast and slow thinkers, ML is pushing the boundaries of fast and fast thinking. Daniel Kahneman, a winner of the Nobel prize in economics, clarifies how this two-speed system works:
System One — Thinking Fast
Humans are sometimes called intuition machines because we rely on an instinctive way of thinking. System one thinking uses associations and memories, pattern matching and assumptions to help us arrive at conclusions fast.
System Two — Thinking Slow
This is our analytical way of thinking. System one continuously creates impressions, intuitions, intentions, and feelings and only reverts to system two in situations where something unexpected is encountered. System two reflects, analyzes, and solves problems, the results of which form beliefs and actions.
Machine learning is quickly creating the equivalent of a ‘think fast and fast’ machine by applying superior computer power than the brain. Or, in more human terms, enabling machines to use intuition as the foundation of their intelligence.
As we come to understand and apply machine learning more broadly, it won’t always be the separate aspect of technology that it is today. It will become ubiquitous, throughout the workplace across a variety of industries and everyday life.
What does this mean for businesses? Early adopters of AI and ML already have years of experience under their belts and now that they’re up and running with this technology, the risks have significantly diminished.
It’s highly likely that you’ll apply this technology in your business at some point, which is why it’s critical to understand the business case for AI investment in your organization.
Using AI in Your Business
When applied to the right business challenges, AI can demonstrate a great return on investment.
Many firms have already integrated AI and ML with their business processes. Here are some examples:
1. Automated vehicle checks that enable staff to tackle other priorities
A major delivery company manually checked for damage to their delivery vans between trips. Using automated video cameras and AI that could spot damage, the checks were carried out faster, freeing workers up for other value-adding activities.
2. Anomalous transaction identification without human intervention
An energy company implemented machine learning to analyze consumption patterns. By clustering transaction information into unforeseen groups, the system could build a model, recognize patterns and analyze and predict customer usage.
This approach takes data patterns and recognition beyond traditional approaches. And it enables businesses to identify and correct problems faster and with a greater degree of accuracy.
3. Highly accurate component failure predictions
Knowing when to replace manufacturing components is critical to optimizing equipment and maximizing revenue. ML can be used to more accurately forecast component failure by combining predictive data with real-time analysis of factors like speed, accelerations, and temperature. By combining both kinds of information and building a model that identified the correlation between the factors and failure rates, the system delivered superior predictive power. This enabled issues to be flagged sooner and more accurately, alerting workers that fixes were required and enabling superior workforce planning.
Deciding how to use AI and ML in your business requires an accurate project scope: identify specific use cases and build a list of ventures so you can work on multiple projects, quickly apply your learnings and add value. Gartner recommends staying on track by aligning your project with your business goals and intended metrics for measuring success.
Investing in AI and ML needs to have a sound business case and rationale. By being really clear on the problem you want to fix, understanding the value to the business and setting clear measures, you’ll be able to demonstrate your return on investment.
How Should You Adopt AI?
AI development exists on a spectrum with inventors at one end and installers at the other. Depending on your business project, you may find that the middle position is the sweet spot where you get the best of both worlds:
Creates new models and techniques. This requires in-house skills and significant time and investment. You’ll hold the intellectual property rights to what you’ve created but it’s unlikely that you’ll need to invest this much time and money unless you’re a futuristic business that isn’t served by existing AI software.
Develops existing technology frameworks with embedded AI to improve processes. This route involves taking off-the-shelf models and customizing them to fit your business requirements. You’ll need in-house data scientists to do this kind of work or you can outsource to an experienced team with fast project delivery capability. This helps you prove the value of AI techniques without making an enormous financial commitment. And by adapting software to solve your customer or business problems, you’ll gain a competitive advantage through bespoke solutions that can’t easily be copied.
Uses and tweaks pre-existing technology. A prepackaged software solution is sometimes good enough and if it gets you to where you want to go quickly and easily, it could be an option. However, it might not give you the best outcome. It likely won’t give you much competitive benefit as your rivals are able to buy exactly the same piece of software and implement it in exactly the same way as you.
Whichever route you pick, you’ll need to ensure you operationalize your AI. This means managing the model on an ongoing basis and anticipating its evolution as your business changes.
What Do You Need to Get Started?
Before you take your first step on your AI journey, consider carefully whether AI is the right tool to solve the problem. Traditional tools like constrained optimization and business intelligence systems can often be perfectly adequate for what’s required. You might want to consult with a trusted development team to understand whether AI is the right option to add value to your business.
Once you’ve decided AI is the right route, you’ll need to gather the base data that decisions will be founded on and metadata (data about the base data). This requires a build-up of structured information with a significant degree of detail. Generally, the more data you have, the better the result.
As with any software development project, you’ll need a plan and a process. It is a common misconception that AI projects need to be large-scale and lengthy. We often find the best approach is to take a specific problem and move it from research and development into the live phase and proof of concept very quickly. In a matter of weeks, not months.
This can mean working with an experienced development company who can quickly trial and test products allowing you to move forward with confidence.
If you have legacy systems in place, don’t worry — they’re not a barrier to AI. The best technology companies will be able to connect old systems with new enabling data to stream smoothly, without impeding innovative new tech.
AI and ML are on their way to becoming an indispensable part of the way we work. To make your first move into this new arena:
- Identify a number of potential projects/challenges and review how AI could provide a solution. Determine what success looks like and ensure the solution can meet this.
- Review where your data is coming from now and in the future and clarify how much work is going to be required to clean and transform the data.
- Understand, with supervised learning, your model is only as good as the pre-labeled data it is provided with. During development, testing and once your model is released, continuously review the process for validating labeled data.
- Develop a plan for retraining your models. This could be due to higher volumes of information, enabling you to provide better insights, or having to react to changes in your underlying business environment. Either way, your model will likely need to be retrained to ensure quality.
With your first intelligent system running successfully, you'll have solid foundations for more AI business cases and the continuous improvements on offer.
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