What Every Executive Needs to Know About AI to Build an AI-driven Company: Artificial Intelligence for Business Leaders

What Every Executive Needs to Know About AI to Build an AI-driven Company: Artificial Intelligence for Business Leaders

Mateusz Łach - February 10, 2022

If you’re an executive, it’s essential to stay ahead of the curve on new technologies that can impact your business. One such technology is artificial intelligence (AI). Many people are getting excited about the rapid development of artificial intelligence and its potential to change how we live, work, and play. But what does every executive need to know about AI to build an AI-driven company? What’s the baseline knowledge of the concepts and challenges of AI for executives? This article focuses on the key concepts and considerations for business leaders considering moving towards AI adoption.

Business leaders need to fully understand and appreciate how AI can help them. A CEO doesn’t really need to be an AI expert. Still, a certain level of knowledge helps to feel confident among developers and understand the key concepts for business decision-making.

Get Your Terms Right (AI vs. ML vs. DL vs. Traditional Software)

To confidently navigate the tech landscape connected with AI and ML, you must understand the key terms.

You need to be able to differentiate between machine learning and artificial intelligence. You may have heard different definitions of these terms depending on whom you asked.

Machine learning is just a subfield of artificial intelligence at a most basic level. In business contexts, the terms are often used interchangeably to refer to machines that learn from data independently. In marketing, the term AI has been so violently abused it’s lost its original meaning – AI is a technology used in vacuum cleaners, TVs, and toothbrushes.

Let’s set the record straight.

Traditional Software

Traditional software is built around deduction. People are responsible for coming up with rules and coding them in the system. Then, these rules are applied to data.

traditional software vs. machine learning technology

traditional software vs. machine learning technology

Artificial Intelligence

Artificial intelligence is usually used when referring to software that can solve problems by itself. The term is very robust and covers abstract cognitive solutions and various machine learning approaches.

Machine Learning

As the name suggests, machine learning means that machines are used to learn from data and derive insights, conclusions, or actions. ML is a form of data analysis that automates the analytical process, making it possible to learn from data and make predictions on new data. Machine learning involves induction. A machine learning algorithm is fed with examples (hence the term learning) to discover rules automatically. These rules are then applied to new data. For example, you can provide a machine with hundreds of images of cats to help it develop its own rules to identify cats in the future.

In supervised learning, a computer system is given a set of labeled training data, which it uses to learn how to make predictions about new data. Unsupervised learning algorithms are given only data without labels, and their goal is to find patterns and groupings in the data.

Deep Learning

Deep learning is a subset of machine learning. It uses algorithms capable of learning from data without human intervention or assistance and drawing conclusions on their own. These techniques are usually more complex and have had a lot of success in image recognition and natural language processing.

Reinforcement Learning Models

Reinforcement learning is an area of machine learning that deals with how agents can learn to behave in specific ways when interacting with their environment. The aim is for the agent to receive feedback on its actions, which will help it make better decisions in the future.

Convolutional Neural Networks (CNN)

A convolutional neural network (CNN), also known as ConvNet, is a deep learning algorithm used for object detection and identification, facial recognition, and character recognition.

With the basic terms out of the way, let’s discuss what you need to know when adopting AI technology.

Get Your Foundational Technical Knowledge on Artificial Intelligence Technology

Get a Solid Understanding of Common AI Opportunities

To adopt AI technology, first, you need to understand its possibilities to identify opportunities beneficial for your particular business. AI can be applied to various typical problems businesses face. Some common AI applications include:

  • image recognition and analysis for automated computer vision software,
  • natural language processing tools that can understand human language to extract data or generate new text-based insights,
  • voice/sound analysis and generation
  • fraud detection and anomaly detection to discover events that could be fraudulent or unusual,
  • recommendation engines for personalization,
  • predictive analytics for forecasting and building insights,
  • advanced modeling for massive amounts of parameters.

Data is Paramount

Data is the bread and butter of AI. All business decisions are based on data, so executives need to have at least a basic understanding of how data works. Data can be structured or unstructured. Structured data is when information is organized in a specific way, like in a table or spreadsheet or a series of labeled images in the same format. Unstructured data is data that’s not in a defined structure, like text or voice. In many cases, businesses have more unstructured than structured data.

AI technologies take lots of well-structured data to work correctly. AI systems like neural networks make better decisions when vast amounts of historical data are available. The primary benefit of advanced ML and deep learning models is their ability to process and get insights from unstructured data.

Do you have enough data? Where will this data come from? Is the company ready to provide it? Providing the data allows them to produce models that organizations must constantly refine as new data comes into the organization.

Many businesses use infrastructure built by multiple teams and developed over many years. This typically leads to a fragmented information landscape, with data stored in different systems that are not connected. De-siloing is a foundational practice for modern, digital-first organizations. It takes a mixture of leadership and investment in technology to break some organizational and technological barriers.

The role of a future-minded CEO is instrumental in the process of AI adoption – leading from above, enlisting key allies in the organization in a joint effort to unify the organization’s data architecture. To learn more about data strategies for AI-based products, head over to our article on AI Data Needs.

Coding From Scratch Is Not Always the Best Option

Whether deciding on a ready product or building one from scratch through outsourcing or in-house development, the success of any project is contingent on the team’s expertise, which is also true for AI development. Depending on the scale, goals, features, and budgetary constraints of the project, a company must choose the most convenient and efficient model for software development.

Building From Scratch

Coding from scratch takes longer, especially with the emerging crop of developer toolkits that accelerate productivity, carving weeks or even months off development schedules. Building a custom AI solution is often considered the best way to get started with AI and turn it into business value for your business.

Ready-Made Solutions

There are two main ways to approach AI development: through custom AI software development or with ready-to-use AI products. Many ready-to-use AI products can be easily implemented in the organization and solve your problem without unnecessary development costs on your side.

However, for some businesses, it may be better to focus on custom AI solutions while others might find their needs could be met by one of the prebuilt solutions available today.

For a more thorough comparison of ready-made AI solutions and using custom development services, read another post on our blog.

Now, let’s focus on the factors which make your business a likely use case for AI and machine learning technology.

When an ML Solution Will Benefit Your Business

A couple of scenarios make an ML solution more likely to elevate your business, whether it’s ready-made or self-developed.

You Already Have Lots of Well-Structured Data

If you have lots of clean, well-structured data, the odds are high your business will benefit from implementing AI. Some of the most promising use cases for AI tools include natural language processing, predictive analytics, precision medicine, and clinical decision support.

Reliance on data in decision-making processes is a good sign that you would derive value from an ML solution. ML and AI algorithms allow businesses to optimize and unearth new statistical patterns and predictive analytics.

You Have Lots of Data and Apply Rules

Suppose hand-crafted rules or heuristics are already applied on various datasets in your organization. In that case, machine learning will just kick you into the fifth gear, helping you find patterns and complex rules based on existing data.

For example, if you are running an e-commerce store, you can recommend new products to them based on their previous purchases and browsing history. With an ML solution in place, you can improve the existing hard-coded rules and automate the manual labor it requires.

When an ML Solution Won’t Benefit Your Business

Leveraging ML may not always be the best option for your business. It could be a terrible fit in a couple of scenarios.

The Repetitive Tasks You Want to Automate Are Not as Repetitive

Specific tasks only appear “repetitive” instead are anything but. It turns out they can’t be fully defined and automated. This is very typical of sales processes, which involve writing sales emails and handling support cases. This seems like a great use case for an ML solution, but it won’t significantly improve these business processes or completely replace them.

Many human processes rely on empathy, creativity, and improvisation, which can’t be coded as part of the algorithm.

There’s Not Enough Data

Launching an AI-driven app without a solid amount of data to drive it is a common mistake. It’s almost always better to focus on gathering and organizing the data and gaining insights manually before considering machine learning. There is little benefit to implementing ML preemptively, and expect it to benefit the business before gathering data.

Use cases for ML can be found in almost every business, but this is always a balancing act between investment and return. In a small business with little data, AI processes won’t make much difference, and analyzing the data manually might still be a better option.

The Common Problems of AI Development

AI development is associated with potential pitfalls and problems which you should be aware of before moving forward:

You Lack AI Skills Within Your Team

Are you planning to launch an ML solution? Be sure to have the necessary AI expertise in your team. If you decide to go down the path of developing your ML solution, it’s essential to have a team that knows its way around this technology. It requires various skillsets and expertise, from machine learning experts and data scientists to software developers.

If you don’t want to build these skills in-house, make sure to hire experienced partners.

You Require Significant Investment

From an initial project estimate to the final delivery, you will need a good amount of capital from hiring the right people, purchasing software development services, and paying for software licenses. That’s why it might be worth considering collaborating with an experienced company instead (especially if you are not experienced enough in this field yourself). To learn more about artificial intelligence development costs, check out this post.

The cost of future maintenance increases proportionally with its complexity, and the computation needs it will produce.

People Don’t Trust Your Model

It’s the CEO’s role to convince the team and explain to your customers why they should trust your model. Even if it consistently generates accurate predictions, you should expect pushback.

To many people, the workings of ML are nothing short of magic – and that’s normal. It’s a good idea to understand the critical requirements regarding explainability and interpretability to tackle such concerns. It might be a legal requirement in areas such as pharmacology or credit scoring.

The AI Solution is Unmaintainable

It’s important to understand that development is an ongoing, iterative process, meaning your solution will need to be constantly updated and fixed. It’s no different with AI solutions. Your AI model might work today, but it requires proper maintenance to ensure it is reliable tomorrow and adapts to your changing needs.

Having an experienced AI team on board might cost you extra, but it is an excellent way to make sure the model evolves to fit your needs.

A Ready-Made Solution Might Be Just Fine

Custom development makes sense for many scenarios. However, choosing a ready-made, off-the-shelf solution might be a more sensible and efficient strategy for specific use cases. There is an ever-growing number of ready-to-use AI products, and choosing one will shorten the time to market.

For a more in-depth take on the develop vs. buy dilemma, read another article on our blog here.

Your Model Is Biased

No matter the processing power, computers are fallible if the data machine learning model was trained using biased data. Such was the case of the COMPAS system, a piece of software helping determine which prisoners could be released earlier. The system was found to have a significant racial bias – tagging Black defendants as two times more likely to commit recidivism.

For example, you can build an image recognition model using thousands of images of cats. However, the software will still be unable to identify a can when it’s wet – because it wasn’t fed with images of wet cats.

Should You Outsource or Build Your Own ML Team?

Outsourcing AI Project Development vs In-house AI team

You need to make the primary choice between building your own ML team or hiring a consultancy. Creating your own team or a whole artificial intelligence laboratory can take many years, and it’s probably only the right choice for your corporate strategy if you don’t need to see results urgently and if you expect ML to be the key differentiating factor between you and your competitors. Otherwise, a good partner skilled in developing advanced software solutions will also be able to complement your in-house team or take over the development completely and guide you through the process of building the technical environment and setting up a full-fledged AI system within your company.

AI for Executives - Last Words

Tech-minded CEOs tend to force ML where it can’t add value to stay modern and relevant. Others fail to take advantage of it where it would add value. You need to understand why you need an AI solution in the first place. Are you trying to solve a problem, planning to beat a competitor, or only exploring the potential use of the technology in the business? Understanding what’s possible and what’s not possible will allow you to set your expectations about AI right and reduce the pushback from non-believers within your organization.

Even if investing in AI is not on your agenda today, it’s definitely something worth considering. If you’re looking to develop your own AI product or trying to find a provider of AI software development services, our team of experts will discuss your project.

Our AI Design Sprint is a low-investment workshop that aims to kickstart any AI implementation project and give strategic benefits. We apply a set of tools for each step of the design-thinking process to help our clients leverage the power of AI and machine learning algorithms and turn these technologies into a tangible competitive advantage. Get in touch with us to start your AI adoption and digital transformation journey.

AI Design Sprint workshops

AI Design Sprint workshops organized by nexocode

About the author

Mateusz Łach

Mateusz Łach

AI & Digital Business Consultant

Linkedin profile

Mateusz is a digital strategist and innovation enthusiast. He enjoys building new products and concepts, often with the help of AI. Mateusz joined Nexocode with the mission to consult startups, mid-size companies, and enterprises on their digital transformation journey and help them benefit from custom artificial intelligence solutions.
Responsible for overall business development and sales activities. A geek of new technologies.

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Becoming AI Driven
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