How to Start Implementing AI in Business Processes

Analytics by Precedence Research demonstrates the strong dynamics of the AI market. It is expected that the value of the industry will exceed $2.57 trillion by 2032. It shows a high demand for technology and attempts to integrate it into the corporate infrastructure of companies of all types and sizes.

In theory, everything looks simple:?

• Take (purchase) a basic LLM.
• Train the model on relevant data sources.
• Change your technology stack and integrate LLM.
• Show the staff which buttons to press.
• Make a profit.

However, the process is much more complicated and involves overcoming challenges related to artificial intelligence integration, your team training, further maintenance of the system, etc.

Today, you will learn about the risks of implementing AI and strategies that simplify infrastructure modernization.

Challenges of Artificial Intelligence Integration

According to Forrester, more than 60% of business employees use AI to improve productivity and work effectiveness in 2024.? It is a positive trend that should be followed.

At the same time, it is necessary to understand the challenges companies face across different stages of artificial intelligence deployment. Here are some examples:

• Integration plan. Namely, a lack of a step-by-step strategy for integrating artificial intelligence into the existing infrastructure.
• Understanding the risks. Not everyone is aware of AI-related problems (like the reliability of data) and sees ways to solve them.
• Complexity of integration. The need for fundamental changes in current platforms.
• Cost. Even the exclusively technical component of integration is estimated at tens of thousands of dollars.
• Training. Untrained staff and IT teams cannot use artificial intelligence effectively.?
• Service. Updating data and validating information in data lakes may be a problem.?
• Privacy. There is a risk of leakage of corporate and sensitive data outside the company's digital space.
• Ethics. Even with regulatory laws in place, there are risks associated with the liability of staff applying AI.

I’d like to note that you can overcome these challenges with systematic work and adjustment of artificial intelligence integration processes. In other words, through the use of quality and complex strategies.

Strategies for Integrating AI Into Corporate Infrastructure: Description, Pros and Cons

Since artificial intelligence is still a relatively new technology, its integration process is not fully standardized. Thus, companies approach implementing AI into the existing business infrastructure differently. These methods usually include:

• Building a system from scratch. Creating or renting software compatible with AI.
• Network adaptation. Customizing existing solutions to ensure compatibility with AI.
• Combination. Using new and existing solutions to deploy artificial intelligence.

While the technical side of integration is crucial, the success of an AI project depends even more on the deployment strategy and its preparatory and organizational aspects. I'll discuss these in more detail below.

Internal Training

One of the most common AI integration strategies is to “grow” your staff. You gather specialists from various fields to teach them new skills and then involve them in the AI implementation process.?

This method is quite effective, as it has many advantages:

• Feasibility of training. The team understands the specifics of the business and uses the acquired knowledge to improve it.
• Volumes of training. You can contribute to the growth of the staff comprehensively, including specialists of all kinds.
• Internal education. You do not need to change the composition of the staff and negatively affect its coherence.
• Potential. Trained specialists remain in the company and have a high level of loyalty, which positively influences work efficiency.
• Long-term investments. The initial spending will pay off over the next few years through increased productivity.?

Among the shortcomings, I can single out the following:

• Cost. The initial investment can be prohibitively high, especially for a small business.
• Complexity. There are numerous factors to consider to begin integration.
• Payback. Do not count on large ROI in the short term.

This strategy is ideal for companies with a primary focus on AI, startups, or businesses with a sufficient budget.

Involvement of External Specialists

Another way to implement artificial intelligence is by hiring external specialists. It allows the company to acquire the right talent without investing heavily in training its own personnel.

The strategy has several advantages, for example:

• Hiring cost. Working with an external contractor is cheaper than maintaining a similar level of in-house staff.
• Speed of getting help. You can add an expert to your team in less than a week.
• Convenience of talent search. You can hire both a freelancer and an expert from a specialized company.
• A huge talent pool. You have access to a broad range of specialists with various skills and expertise.
• A short-term benefit. If you need to integrate AI here and now, hiring an outsourcer is the best option.

A few disadvantages of this type of strategy:

• Limited potential. The new team members need time to onboard and understand the capacity, challenges, and stoppers of the company.
• Typical outsourcing risks. The complexity of process management and communication.
• Interest alignment. Your expectations will not always be met by the actual result.?

This option is suitable for all types of companies because it is flexible and relatively inexpensive compared to other AI integration strategies.

Collaboration With Specialized Companies?

There are companies specializing in AI. Their employees have sufficient experience and skills to help your team integrate artificial intelligence effectively.?

Such service providers offer the following advantages:

• Niche expertise. Finding a vendor with experience in a similar project and industry expertise can speed up the AI implementation process, while onboarding will go smoothly and relatively fast.
• Maximum service quality. Contractors work for reputation, so they deliver top-notch work.
• Comprehensive cooperation with the team. Experts usually have strong communication and cooperation skills.?
• Work for the result. Specialists work hard to achieve specific outcomes, driven by the rewards they receive.
• The possibility of additional staff training. Hired experts can train your staff on the job for a fee.
• Long-term contracts. You can establish contracts with these companies for a specified period of cooperation.

But this model has some drawbacks:

• The difficulty of choosing a partner. Not all providers are reliable.
• Waiting time and queue. Expert contractors usually have a demand for services and a backlog of clients.
• Quite high tariffs. You pay more for quality services compared to hiring a freelancer.

An AI integration strategy involving professional teams is suitable for all types of companies, regardless of their scale and budget.

IT Staff Augmentation

The team augmentation model can be a good choice for companies that have their own specialists but need to bring in experts with specialized skills or to supplement their in-house manpower.

In this case, the business benefit from:

• Comprehensive assistance. The hired specialist performs tasks thoroughly and responsibly.
• High quality of services. Brand reputation is essential, so the expert strives to be 100% efficient.
• Full cooperation. The contractor directly interacts with your full-time employees.
• Effective project management. Your project manager takes care of all elements of the AI integration process.
• Leveling of risks. By signing the agreement, you eliminate potential challenges related to intellectual property.

However, this model has some peculiarities:

• Difficulty finding a provider. Although the pool of talents and providers is large, sometimes it is difficult to choose the right candidate.
• Limitation in the scope of cooperation. You may not always have full control over a professional's development in the direction you need.
• High cost. Companies with a good reputation usually charge higher rates for services, though it can be comparable with rates in the local markets of countries with high welfare rates.

Services of Consulting Experts

Perhaps you do not need an AI engineer but lack a specialist who would estimate your project comprehensively and indicate the right way to implement it. In this case, you will need the help of a specialized consulting agency.

Advantages of cooperation with analysts and consultants:

• Onlooking. Experts evaluate not only the strategy but also the existing infrastructure and its potential.
• Wide specialization. Specialists will help you, no matter what industry your business belongs to.
• Comprehensive assistance. In addition to the system evaluation, you will receive suggestions for infrastructure improvements and AI integration strategies.
• Risk assessment. Experts will help you identify potential problem areas and provide advice on their elimination.
• Calculations and estimates. Specialists will point you to the optimal LLM and help you determine the scale of costs.?

Disadvantages of this cooperation model:

• Lack of practical help. You only get infographics, calculations, and concepts instead of technical implementation.
• Possible differences of opinion. The strategy provided to you by the experts may not fully align with your company's long-term strategy.
• Project evaluation duration. Depending on the scale of the infrastructure, complex analytics and strategy building may take some time.

This model of building an AI implementation strategy is useful for all companies, as it is an analog of the discovery phase in classic development projects.

What Option is the Best?

To be honest, you don't need to choose. You will need all of these cooperation models if you plan to play the long game and gradually integrate AI into the business infrastructure. And even more so to build the last one from scratch.?

In my opinion, the following architecture of an artificial intelligence integration strategy is quite logical:

1. Comprehensive consulting and assessment of business needs and opportunities.?
2. Involvement of external experts to start work.
3. Internal staff training.
4. Expansion of the team with specialists (including mentors).
5. Collaboration with specialized companies to improve the transformation outcomes.

However, the final strategy will depend on the needs and financial possibilities of your business.?

Well, I will end the post on this optimistic note. If you have your own perspective on AI integration strategy or want to add something to the article, I will be glad to hear your thoughts in the comments.

My colleagues from the AdvantISS tech team have experience in different industries and with different types of projects, as well as practice cooperation using different business models. Feel free to let me know, if you need assistance with AI implementation in your business.