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Innovation is never achieved by standing still and value is never generated by not taking risks. The evolution of the global business market has been intensively powered by technology—and now, technology is advancing itself through Artificial Intelligence.
For an organization to get the most business value out of AI solutions, it is critical to understand what AI actually is, its impact on the business world, how it creates that value, and how to implement an AI strategy. The goal of this whitepaper is to guide business leaders throughout all these topics, offering key advice and insights on artificial intelligence solutions and the role of IT Outsourcing Services in the matter.
In 2020, AI is as trendy as it has ever been. This has brought a lot of relevance to the topic, but also many misconceptions and uncertainties. However, it must be said that the value-driving capabilities of artificial intelligence are undeniable. Most industries, if not all, are already benefiting from this technology. And at the same time, companies have witnessed dramatic changes in the business landscape.
Now, implementing a successful AI strategy comes up as a difficult challenge for many organizations. But, when done properly, the returns are tremendous. With today’s market being more tech and talent-driven than ever, leveraging business strategies on cutting-edge technologies seems to be the only way to remain competitive. We want to provide you with the necessary knowledge to understand, adopt, and run AI solutions in a business context, while also getting the most value out of them.
Artificial Intelligence is an often misunderstood field, especially by companies that are not too familiarized with its applications. Some people believe AI is about human-like robots and magical code, while others think it is just too complicated to get into. However, the truth is that we are at a stage where AI is easy to understand, access, and implement at a basic level—which makes it all that more powerful for businesses.
In general terms, AI is defined as the ability of machines to perform human-like tasks by identifying patterns from large datasets. The field originated in the mid-1950s and its capabilities have expanded at an exponential rate.
Today, it is common knowledge that computers have far surpassed human skills in many domains. This has strongly attracted the attention of innovative businesses looking to scale the efficiency of their operations. But the success and value generated by any AI project require a clear understanding and smart expectations from the entire organization, particularly the C-level.
These three key concepts are the starting point of any business strategy that uses artificial intelligence.
Throughout the 2010s, more and more businesses began embracing the ever-growing capabilities of artificial intelligence. This way, many companies were able to integrate advanced deep learning algorithms into their existing models, with others invested resources into IT Outsourcing Services to design custom AI solutions in a trend that shows no signs of slowing down. In fact, the global market for Artificial Intelligence solutions amounted to USD 20.67 billion in 2018, and it is projected to reach USD 202.57 billion by 2026, exhibiting a compound annual growth rate of 33.1%.
By now, it has been proven that AI is not just capable of creating new complex solutions, but also excels at improving current business processes. And with the large amounts of data available for modern organizations, implementing artificial intelligence seems to be the best way to streamline operations, reveal opportunities, and save time. Thanks to the flexibility of customized AI solutions, this means that there is no industry or enterprise that couldn’t benefit from artificial intelligence.
AI is extremely powerful, which makes it capable of driving complex and laborious business tasks. And now it has become a mainstream alternative, an opportunity for business leaders all around the world. This year, we will witness an increasing consumer demand for higher-quality AI-enhanced products and services.
According to Jorn Lyseggen, author of Outside Insight: Navigating a World Drowning in Data, there are three macro trends in technology that have powered the fast development of the AI industry. These are:
Over the years, we have seen worldwide industries slowly transition into the digital environment by developing customized software applications for their business needs. Using cloud-based computing power has also made this process a lot smoother. Software services can now be hosted and provided from anywhere in the world, which has created a very competitive market for them.
But although AI advancements have undoubtedly come a long way, we are still far from being fully replaced by robots. Computers process data, but it is humans who understand it and build insights from it . Otherwise, data just becomes commoditized. We might be drowning in information like never before, but none of it has value unless humans put it to use. Insights are as valuable as the data fed to the algorithm. In other words, AI does not provide value by itself.
Instead, it enables people to make data-driven decisions. From automation capabilities that improve efficiencies, to correlational trends and predictive analysis, the only way to gain a competitive edge from artificial intelligence is to derive insights from data and to make sense of it.
Quick overlook at three models that you can use to measure the business value of AI solutions.
Proposed by IBM, the Cognitive Model defines AI as a cognitive technology that augments human capabilities. By simplifying processes, performing complex operations, or providing relevant information at the right time, AI unlocks new intelligence from the vast quantities of data it analyses and opens a lot of doors for human work.
The Cognitive Model describes three ways in which we can categorize the business value of AI: Engagement, Knowledge, and Automation. Organizations that generate value in any of these areas gain advantages over their competition by having a strong digital foundation that accelerates business outcomes and growth.
This model places the business value of AI in a 2×2 grid that measures its Type of Result and its Type of Impact. On one hand, the Type of Result measures the primary output of the AI solution (aka the answer or action generated by implementing it). On the other hand, the Type of Impact measures the raw data of the primary intended financial contribution of the technology.
The goal of the Result and Impact Model is to define clear starting points for organizations looking to measure the value of their AI implementations, as well as to direct the evolution and future goals of the AI application.
This is the customized approach BairesDev developed based on our industry experience. First, companies must understand how to provide customizable and scalable AI-driven experiences by capitalizing on data. This is done by tackling AI solutions from a cyclical perspective that encompasses data, insights, strategy, action, and learning.
After this, we can identify the opportunities to generate business value by analyzing data and work complexity. This way, we can determine when AI should be used to automate (to remove humans from the process) and when AI should be used to augment (to enhance and complement human capabilities).
Artificial intelligence can also be defined as cognitive technology. It augments human expertise to unlock new intelligence from vast quantities of data and to develop deep, predictive insights at scale. In other words, it maximizes the cognitive capabilities of humans, which are later applied to business matters.
This way, we can categorize the value of AI across every industry into three main cognitive branches. These describe the motivation for business adoption and the way value is provided.
The Cognitive Model also considers how organizations gain advantages over their competition by having a strong digital foundation. Paired with cloud computing, data analytics, and the proper cybersecurity measures, implementing AI from a cognitive intelligence standpoint has proven to be a key accelerator of business outcomes and rapid growth.
According to a study carried out by Deloitte, which reviewed close to 200 vendors and their clients, the value of AI in a business landscape can be mapped out following its type of result and type of impact on a 2×2 grid.
In this chart, the type of result axis is used to determine whether an AI produces known or unknown results.
The type of impact axis, on the other hand, distinguishes whether an AI delivers financial impact on the top or bottom line.
Of course, real-world AI applications will generate results that overlap throughout the 2×2 grid—especially as those solutions evolve over time. The goal of the Result and Impact Grid is merely to define clear starting points for organizations looking to measure the value of their AI implementations, as well as to direct the evolution and future goals of the AI application.
While both of the previous models are quite effective at guiding businesses on their AI journeys, we believe there is a better way to analyze the business value of artificial intelligence solutions. Our experience tells us that any AI business strategy must be targeted towards creating customizable automated and augmented services. This is achieved in two steps.
First, companies must understand how to provide customizable and scalable AI-driven experiences by capitalizing on data. This is done by tackling AI solutions from a cyclical perspective:
Based on the principles of the Value Generation Cycle, we can identify the opportunities to generate business value on a dual axis plane.
On the Y axis, we can find the Data Complexity, which can be determined based on the data set structure. For example, strings of code are linear and structured data that a computer can easily read, while images and videos are unstructured and much more complex.
The X axis represents the Work Complexity. This refers to how routinary and how many rules a type of work has to follow. The complexity is proportional to its predictability: the more rules and routines, the more it requires judgment, and the less predictable it becomes. For example, the process of a self-driving car making a left turn has a very high work complexity—the AI must follow many rules and judge whether or not it is safe to make the turn.
Based on these two axis, we can determine when AI should be used to automate and when AI should be used to augment. Automation removes humans from the process, while the goal of Augmentation is to enhance and complement human capabilities. This creates four areas in which we can identify the role of AI.
Following this dual axis plane, any company can easily identify how AI can provide value to their company and begin to measure it. If you are not sure of how to place your AI project into the plane, try to split it into smaller pieces with more specific goals. How a company uses this or any of the other models mentioned before will provide unique results and ideas for value creation.
Any successful AI strategy has a thorough focus on generating value for the company. Implementing it, however, requires more than software tools, resources, and talent. Just like any business strategy, implementation begins with putting the intangible fundamentals in place. The three main ones are proving how AI can provide value, getting leadership support, and spreading an adoption culture.
The era of Artificial Intelligence is upon us. It is evident that AI has already begun to create a tangible value in most (if not all) industries. The transformative capabilities that AI provides for businesses looking to gain a competitive advantage is undeniable—which means that the sooner an organization adopts the technology, the better. Any business that decides to understand and implement AI will be able to shape the value generated by their AI strategy as needed.
For those who have still not begun their artificial intelligence journey, the time to act is now. Accessing data is the key of everything. As long as a business is able to determine the type, quantity, and quality of data required to achieve its objectives, finding relevant use cases to pursue shouldn’t be hard. Otherwise, getting data from other public sources or partnering with an IT Outsourcing Company can help with achieving long term goals.
And for those looking to take the next step, don’t be afraid of failure. Even though many AI projects can be particularly challenging, require interdisciplinary collaboration, and bear high cost and time investments, achieving success is well worth it. Remember: failure is an integral part of innovation—and that is the basis of any Artificial Intelligence algorithm.
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