The focus is on solutions that privilege the user experience. According to John-David Lovelock, an executive at Gartner, one of the world’s most recognized research and consulting companies, AI promises to be the most disruptive technology in the next 10 years.
Among the particularities, advances related to computational power, volume, speed and variety of data, as well as advances in deep neural networks are gaining evidence.
That is why we elaborated this post talking about Artificial Intelligence in companies.
Artificial intelligence: what it is and where it is going
Basically, Artificial Intelligence can be conceptualized as a set of technologies that allows machines to reproduce some types of human resources, such as:
- the ability to listen;
- to reason; act and predict
- and, mainly, learn from past experience.
Today AI is used in texting, research, e-commerce, social media, industries, healthcare, finance and in various other segments. Sometimes we are aware of its presence, as in the case of autonomous cars, but in other situations it works behind the scenes of applications and other tools.
Investments in Artificial Intelligence continue to increase substantially. Innovation, experience and investments are combined, which means that AI is ready to gain strong momentum within companies.
Uses of Artificial Intelligence
Although AI allows machines to reproduce human behavior, it is essential to remember how they differ from humans.
On the one hand, issues such as computation and pattern matching stand out, but essentially human attributes are lacking, such as feelings, values, and contexts.
To bring them closer, they must be “trained” to recognize goals, languages, and feelings, as well as understand some of the human peculiarities. While some futurists think that machines will exceed people’s capabilities, today’s problems are more pragmatic:
- How will we use AI?
- In which situations is it good and in which is it not?
- Where are the real opportunities?
- How will it affect customers, employees and shareholders?
Business Intelligence Case Study
In a case study extracted from the survey AI in the Enterprise: Real Strategies for Artificial Intelligence June 2018, Stripe, an online payment platform, aimed to prevent fraud and improve the Customer experience.
Basically, Stripe was looking to make the economy more accessible to people. The idea followed the line of Google Adwords, which made it possible for any company to start advertising.
In this context, one of the main challenges was to reduce fraud with the help of machines, which automated decisions based on a few billion data from the platform. For that, Stripe’s algorithm would evaluate metadata about the company and its transactions.
Besides Google and Facebook
Cases like this one from Stripe show us that AI will increasingly be applied beyond a restricted group of academics and tech companies like Facebook and Google. While this is not necessarily for all corporations, Artificial Intelligence in business has enormous potential in several areas.
This advance, especially in organizations, can be evidenced mainly through 5 specific recommendations:
1. Use high-quality data
One of the most efficient ways to reduce risk for AI and machine learning products is to rely on a high-performance data platform. This is very evident in a Harvard Business Review article titled “If Your Data Is Bad, Your Machine Learning Tools Are Useless“, In Spanish:” If your data is bad, your machine learning tools are useless. ”
2. Choose high-impact cases
One of the great challenges is choosing to tackle problems that will be considered a great victory within the business. The ideal, therefore, is to focus on ideas that AI can solve, but that are interesting and unique.
3. Find the right talent and tools
One of the biggest challenges for AI is still immature tools, which can impede democratization and innovation within companies, at least until they mature. On the other hand, it is also necessary to find talents, which are scarce.
This is not to say that there are not talented data scientists, but the requirements for putting Artificial Intelligence into practice in companies with thousands of employees and billions in revenue are extremely high. Although this is a problem, it is possible to align multiple actors around a single goal.
4. Think AI to augment and not to replace your employees.
Some experts, like the late Stephen Hawking, said that Artificial Intelligence will replace much of the workforce. Others, like Susan Lund and James Manyika of the McKinsey Global Institute, say AI will create new jobs.
The fact is that, as with technological changes, some works will disappear and others will be created. For now, AI requires the right level of oversight and governance to be successful, even in the most sophisticated organizations.
In this context, it is worth saying that taking advantage of AI is synonymous with rethinking the human and technological resources of the company, so that in the future they are allocated in the best possible way.
5. Understand customer experiences and the ethical implications of AI.
Artificial Intelligence modifies many rules that govern interactions between companies and people. Chatbots, for example, have created new and challenging models of interaction. Tools like facial recognition, in turn, have changed our understanding of privacy.
In addition, AI introduces a level of information asymmetry between companies and people that has never been seen before. The results of issues like these can have important ethical implications, as well as essentially resonate with the overall customer experience.
Although Artificial Intelligence has big changes in technology, the big question is to start small and apply it to the right problems.
The biggest challenges to apply AI in companies
As was clear throughout the text, there are several challenges for the application of Artificial Intelligence in companies, and most of them are structural.
At the very least, it’s worth remembering that AI is machine learning and not a ready-to-go solution. Adopting it in your organization requires specific sets of resources and skills, as we will see below:
The AI talent gap
One of the first challenges for organizations is finding talent. You will need to have a team with the technical skills necessary to train Artificial Intelligence systems: how to use marketing data to optimize campaigns or leverage customer support data to automate feedback. This type of training requires very particular skills and, unfortunately, the talents on the market are still scarce.
Creating a culture of AI in companies
While recruiting talent is a great challenge, incorporating Artificial Intelligence into the company can be easier. However, as noted, the organization may run into structural issues such as research and development in favor of adopting AI in a real corporate environment.
Most of the options for IT companies are the software or hardware you want to use to do what you need. The problem with Artificial Intelligence is that it requires a lot of training, at least in the beginning, and working with data, so that the expected results are delivered.
In this sense, it will be necessary to invest heavily in R&D (Research and Development) and this is not something that is within the reach of most companies. After all, they never had to do something like this to make the technology work. In other words, there are costs and most organizations are not prepared to bear them.
¿Adopt the wait?
Faced with this crossroads, the question remains: should we adopt Artificial Intelligence in the company or not? In general, there are five types of actors:
- early adopters;
- early majority;
- late majority;
As a general rule of thumb, most managers will avoid being innovative (given the uncertainties, talents, and costs) or laggards (in this case, the rest of the industry will have already taken advantage of the ROI from AI).
Being innovative requires a lot of resources and valuable companies like Facebook, Google and Microsoft are on this team and conducting research on the use of Artificial Intelligence in companies to deepen their knowledge.
As it is very difficult to enter this exclusive group, the best path for most companies will be the middle ground. Therefore, managers should pay attention to real case studies and their applications. When companies really benefit from AI, it can be worth investing and exploring.
Among so many news and uncertainties, the truth is that the world has never been so Black Mirror, if you don’t believe it, look at what they are doing voice assistants in the market and society.