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Artificial intelligence strategy: How do you develop an effective one for your company?

# Artificial intelligence strategy: How do you develop an effective one for your company?

Artificial intelligence strategy: How do you develop an effective one for your company?

Table of Contents

Andrii Kuranov


Andrii Kuranov

Content Manager

Category: Business
3 min read

Technological development has become a key challenge for top managers around the world. According to Accenture's global CEO survey, 85% of leaders cite think of this technology as a force that puts pressure on business. The business digitization turns out to be a real test for the top management, and this is crucial for managers who are implementing artificial intelligence systems.

It is when the questions arise: how exactly to restructure business processes? How do you explain to people that the AI will not steal their job? How to adjust the interaction between employees and their software "colleagues"? You can find answers to some of these questions in our previous article AI in business: what is worthy to know to implement it in your company. In this article, though, we will focus on the aspect of developing an AI implementation strategy.


What is an AI implementation strategy?

An AI implementation strategy is an action plan for implementing AI technology, machine learning, or deep learning to the organization’s workflow. An AI strategy defines your priorities, goals, milestones, mission, and vision for AI implementation.

Generally speaking, its development is quite a decisive factor. So what should you do to create it? We have collected some practical recommendations for you.


How to create a successful AI implementation strategy?


.1 Become familiar with the AI

Generally, if you are a business owner, it is enough for you to know a short definition for AI. Generally speaking, artificial intelligence (AI) is a set of technologies that allow computers to learn from their own experience, adapt to the parameters set, and perform those tasks that previously were manual. In most cases of an AI implementation strategy, starting from voice assistants to auto piloted cars, the possibility of deep learning and processing of natural language is extremely important. With the help of these technologies, computers can be 'taught' to perform certain tasks by processing large amounts of data and identifying patterns in them.

However, it is recommended to devote some time to studying and understanding what artificial intelligence in its modern form can do. There are many different services,, courses and resources with comprehensive information for this matter. It is not necessary to dive deep into various technical nuances - simple understanding of the basic concepts of AI will be enough. Online seminars and courses can be an easy and affordable way to begin your acquaintance with an AI. This can expand your knowledge in areas such as machine learning and organization predictive analytics.

Online resources from the list below would be an excellent start. There are both paid and free services available:


.2 Provide a clear understanding of the issues that AI is required to solve

Having understood the basic concepts, the logical step for any entrepreneur developing an AI implementation strategy, will be the analysis of an existing business infrastructure and search of ways for its improvement by means of AI. At this stage, it is important to understand which goals and needs the company has. It’s also important to figure out how exactly the opportunities pr-ovided by AI can solve the tasks and problems your business has, by complementing or improving existing products and services. Consult with experts, if more in-depth expertise is needed. Moreover, it would be helpful to examine specific cases in which the AI has addressed similar business challenges or provided visible value. After that, it is possible to develop a forecast tailored to your specific needs. A good example is the use of AI in video surveillance. Companies that are similar to this specifics may be able to analyze quite a few cases in a given industry and eventually understand what aspects can be improved by adding machine learning to the process.


.3 Determine a specific business and financial value

After that, you need to make an assessment of the potential business and financial value in the context of the potential ways of implementing an AI. While creating an AI implementation strategy is easy to get confused about, but linking initiatives directly to business values is a great help in setting benchmarks. Additionally, you need to set strategic AI goals and highlight what you need to achieve in the near future.

For proper prioritization, you can use a 2x2 matrix, which will clearly show the size of potential and feasibility. With its help, you will be able to do adequate short-term prioritizing and find out what is the financial value of AI implementation into the company. At this stage, it’s especially important to obtain the company’s managers support and assistance.



.4 Discover gaps in internal capabilities

There is a big difference between what organizational capabilities you have and what you want to achieve. It is crucial for business to be adequately aware of its capabilities in the context of technologies and business processes. Only after this assessment is given you can start a full-scale AI implementation.

Sometimes it can take quite a long time to eliminate the internal gap in capabilities. It is conditioned by the necessity to define what processes should be developed and what resources will be required for an AI implementation. If necessary, the business may consult with third-party experts.


.5 Search for specialists and create a pilot project

By the time the business is ready from both organizational and technical point of view, it is time to start implementing. At this stage it’s most important be patient and move step by step, while being guided by the project's key goals. The most important thing is to understand the level of expertise concerning AI. Here it is necessary to involve external experts on artificial intelligence, as their contribution will be difficult to overestimate. After all, if you want your AI product not just well designed, but also build by the business logic approach to fully solve the business problems, you need to look for a developer company that provides a comprehensive service. You look for service that will help you implement an AI from the discussion of your business ideas to support and further finished product evolution.

As a rule, for a pilot project you may need relatively short time - 2-3 months. One of the important roles of the business owner at this stage is to combine internal and external specialists in a small team. Only then you can focus on simple and easy goals. After completing the pilot project, you can decide what to do next.


.6 Prepare staff to work with AI

Another problem is that people tend not to trust the results shown by artificial intelligence. Companies’ employees often doubt the conclusions drawn by the machine. This is because many businesses today have a division: these tasks are for artificial intelligence, these are for a human employee. However, between these two poles - that one only AI can do and that one remains manual - there is an area where man and machine collaborate. For example: people complement, train, and sustain machines. This is when an AI empowers people with superpowers. This is so-called "missing middle," and those professions will form the basis of the future technology company

That's exactly what you should do. First, train key employees to work with artificial intelligence: tell them what tasks they solve, show the real result in numbers. Second, involve the corporate culture specialists to explain what and how to do. Employees from the "missing middle", who understand the AI's principles of operation, can at least superficially explain them to the rest of the team.


.7 Take care of the ethical and legal aspects

Systems using artificial intelligence technologies are becoming more and more autonomous in terms of tasks complexity they can perform, their potential impact on the world, and the diminishing ability of humans to understand, predict and control their functioning. Most people underestimate the real autonomy of these systems. They can learn from their own experiences and perform actions that were not originally intended by their creators. There is a rather well-known experiment - the ‘Trolley problem’. It raises a number of important ethical issues directly related to artificial intelligence. Imagine that an unguided trolley is carried on rails and five people are tied to it. You are standing by a lever that can be used to switch the arrow, so the wagon will turn and go on another track, where one person is tied to the rails. Will you pull on the lever?


As for artificial intelligence, such a situation can occur, for example, on a road on which an autonomous vehicle is moving, if the accident is insurmountable. There is a question whose life should be a priority - passengers, pedestrians or both? MIT has even created a special website dedicated to this problem, where the user can try different scenarios and choose what to do in this or that situation.

Although you probably did not plan to create an new Tesla, this case is indicative in the context of the ethical and legal aspect of an AI. Replace living people with information about them and you will face the same problem. Will AI consider non-disclosure and confidentiality of personal data? Will it give preference to one group or another? No matter how you use AI, consent and confidentiality will be key considerations. You also want to ensure that your AI is free from bias and discrimination, and that your use of AI is ethical. Remember, AI must be used to benefit the business, its employees and customers. It is enough to be guided by universal moral standards and regulations such as GDPR.


.8 Create a working group to integrate data

An important stage in the artificial intelligence implementation strategy creating is data preparation. Before integrating AI or machine learning into the business, it is necessary to put the data in order to work with it, otherwise there is a risk of a "garbage in, garbage out" situation.

Internal corporate data is usually distributed across different repositories in different legacy systems, and sometimes it is in the hands of different business groups with completely different priorities. Thus, it is especially important to form a cross-specialty group, unite different data sets and eliminate inconsistencies to get the most accurate and complete database that contains all the necessary metrics.


.9 Start with something small during AI strategy

It is best to start using AI technology with a small sample of your data. Starting with something simple and small, you can see how the technology behaves, analyze the feedback and gradually expand.

Choose carefully what AI information you want to receive. For example, choose a specific problem to solve, focus the AI resources on it, and give it a specific question to answer. Downloading the entire dataset at once will be counterproductive.


.10 Data storages as part of an AI strategy implementation plan

Once a large sample of data has been accumulated, it will also be useful to consider storage requirements. For the best results, continuous improvement of algorithms is needed but this requires even more data to enable and build more accurate models. This is why the creation of a fast, optimized storage needs to be designed on the planning phase of a system with AI on board.

It is also important to optimize the storage from which the AI will draw its data in advance. By taking the time to work through the options, you can have a huge positive impact on the system once it’s connected to the network.


.11 Strive for balance while developing an AI strategy

When creating a system with artificial intelligence, remember that it requires a combination of business, technology, and project needs research. This may seem obvious at first glance, but it often occurs that the AI system is developed around specific aspects of plans on achieving its research goals without understanding the requirements and limitations of the hardware and software. The result of this approach is non-optimal, and sometimes non-functional, systems that are unable to achieve the goals they were designed.

To achieve this balance, business needs to create enough bandwidth to store, process, and transmit data across the network. In addition, the security aspect is often overlooked. To accomplish its mission, AI needs access to a wide range of data. Consequently, you need to determine in advance what types of data will be used and whether your usual security measures are sufficient to protect them.


Summing up, having a strategy is half the battle. The issue of paramount importance is its implementation by competent specialists with a proper level of technical expertise. In addition, we don’t recommend looking for simple contractors. It’s better to focus on finding people who can become real partners and will deeply understand your business needs, goals, problems, based on which the strategy will be made. Simply put, look for a company that combines business and technical expertise. What are we talking about, though? You've already found it! Just press talk to the expert button to proceed.