In every field, companies invest in AI tools and techniques to simplify operations and better serve consumers. In reality, Markets and Markets analysis estimates predict that the AI industry will hit $191 billion (£ 148 billion) by 2025. Chipmaker Nvidia has invested heavily in AI as it seeks to drive the technology in the future. AI needs more computational power than conventional algorithms, so it is imperative to invest heavily in new chip designs, causing a land grab among top vendors.
Nvidia has already invested heavily in cases of use in the aerospace, electronics, manufacturing and healthcare. The most prominent is its collaboration with Nuance to use its chips and software for deep learning to introduce AI to medical imaging. The AI-powered imagery service allows medical professionals to use new tools for radiology study of x-rays and other uses.
But, why is it that a few companies are fighting AI - instead of embracing it?
I believe the fast revolutions of technologies have led many businesses to be a bit clueless about what they should be focused on. The shift from data centres to cloud, from the web to mobile web to native apps, and from big data to AI, didn't make it easy for businesses. The discussions between the CFO and the CTO in particular, who on the one hand wants to reduce costs and on the other side wants to have the best possible technology at his / her disposal, can lead to indecision, leading to no-decision.
Another reason I think companies are struggling with AI is that the lack of evidence that AI can have an impact leads many people to believe it's just a hype and will eventually go away. Many industries, including Financial Services, Transportation and Insurance, have used data and computerized decision-making to have an impact on their business, but many other industries that don't have the same level of data are more difficult to convince that AI can affect their business.
Here's how tech giants worldwide are engaging in artificial intelligence to create better consumer products and services.
How to overcome the Cold Start problem with Artificial Intelligence?
Just as business digitization requires a different mindset, so does the algorithm. Competition has increased and commoditised many industries with the technology being available to everyone. The first but most important step in solving the Cold Start Problem with AI is by embracing an AI mentality as a business and acting accordingly.
It's better for new technology startups because there's no precedent and no one in the business has to be told it's needed. So from the moment a company develops and writes its program, embracing the AI mindset will have a tremendous impact on the decisions to be made and how data will be used to build a better business/product/service.
Incumbents are facing a bigger challenge now. Because although top management may be convinced that going the Google, Amazon and Microsoft route to becoming AI-first is the way to go, most employees won't be easily persuaded. And as with any initiative, ensuring everyone is aboard is a critical factor for progress.
So the real question then is, what are we going to do?
Operating from past business changes, we know the true change comes from within. Through implementing the strategy of the AI-mindset can have a huge impact on the rest of the organisation. But actual AI projects that focus on using AI to change a process, are where you can change the opinion of people. Of course, starting a one-off AI project, which does not affect the way you do business, will not cut it. It's best to start multiple AI projects that address different parts of the business, hedging your bets, but also encouraging the organization to share best practices.
Organizations must consider which systems execute what kinds of tasks and the capabilities and weaknesses of each before embarking on an AI program. For example, rule-based expert systems and robotic process automation are transparent in the way they do their job, but neither can learn and improve. On the other hand, deep learning is great learning from large volumes of labelled data, but understanding how it creates the models it does is almost impossible. For highly regulated sectors such as financial services, this "black box" phenomenon can be troublesome, in which authorities rely on learning whether decisions are made in a certain manner. We came across several companies that we're spending time and money implementing the wrong technologies for the job in hand. But if they are prepared with a good understanding of the different technologies, organizations are better positioned to decide which specific needs can ideally be met, which suppliers to deal with, and how easily a solution can be applied. Acquiring understanding involves continuing research and education, typically within IT or a community of inventions. In particular, the capabilities of key employees, such as data scientists, who have the statistical and big-data skills needed to learn the nuts and bolts of these technologies will need to be leveraged. A principal factor of success is the willingness of your people to learn. Some will jump at the opportunity while others will want to stick to the resources they learn. Strive to get the former to a high percentage.
If you don't have in-house data science or analytics capabilities, you will likely have to build a near-term ecosystem of external service providers. If you expect longer-term AI projects to be implemented, you'll want to recruit in-house expert talent. Anyway, it is important to advance with the right skills.
Given the scarcity of cognitive technology talent, most organizations should establish a pool of resources— may be in a centralized function such as IT or strategy— and make experts available throughout the organization for high-priority projects. When demands and expertise proliferate, it may make sense to assign groups to specific business roles or departments, but even then it may be helpful to coordinate tasks and jobs with a single organizing mechanism.
If the desired results are to be achieved with scale-up, firms must also focus on improving productivity. For example, many are planning to grow their way into productivity— adding customers and transactions without adding personnel. Companies citing headcount reduction as the primary justification for the AI investment should ideally plan to realize that goal over time by attrition or by eliminating outsourcing.
How to get off to implementing Artificial Intelligence (AI) fast and easy?
To have maximum impact, AI projects should be aligned with unique, well-scoped possibilities, challenges, and concerns. To introduce AI-based automation in your business, here are a few guidelines: begin by identifying the problem and determining where you can use AI to improve efficiency Identify the data source and concentrate on collecting data from the appropriate touchpoints. Create an AI-based solution to help algorithmic decision making. Once the solution is developed, enforce the same and provide the necessary training C Now the bottleneck is in the creativity, execution and management of companies. It is important for business leaders to get a strategy to do AI work in the company. Initial AI programs may be postponed or underdelivered but there is a high risk that businesses will become non-competitive by avoiding AI.
One of the main challenges facing companies when implementing AI is the lack of the necessary skill in the workplace. Many companies lack the organizational competencies required to plan and execute AI programs. While there is a growing demand for data scientists, data engineers, and other technology professionals, such skills are in short supply. Forward-thinking organizations, educating existing staff to become (citizen) data scientists face this challenge. Business leaders are also looking to increase in-house capabilities of third-party consulting companies that can provide a range of services, ranging from the development of use cases, pilot projects, training staff, and distribution of managed services. The impact of artificial intelligence will be exacerbated in the coming decade as nearly every company can change its key processes and business models to take advantage of one another. Now the challenge is in the creativity, execution and management of companies. It is important for business leaders to get a roadmap to do AI work in the company. To conclude with, initial AI projects may get postponed or underdelivered but there is a high risk that companies will become non-competitive by avoiding artificial intelligence. Artificial Intelligence usage will be a necessity by 2025.