Innovate, Optimize, Excel: The Role of AI Consultation

The size of the AI market is more than $200 billion as of 2023 and is predicted to rise twentyfold by 2033. This can be explained by the increasing number of businesses adopting AI solutions, meaning that only technologically advanced companies will have a chance to stay in the near future. So, no wonder companies tend to find AI consulting partners to support them on their digital transformation journey. And in particular, this concerns non-technical businesses.

In this post, you’ll discover more about the benefits and types of AI consulting. You will also learn the entire AI implementation process and how to choose AI consultants for your project.

Benefits of AI consulting

When requesting assistance from an AI consulting firm, you as a C-level executive expect them to contribute to your transformation journey in multiple ways. Here are some reasons why you need a trusted artificial intelligence consulting provider for your project:

• Identification of opportunities before large-scale investment. AI consultants provide a keen eye for spotting viable AI opportunities, helping businesses to pilot ideas effectively. This prevents unnecessary expenditures and prioritizes high-impact initiatives.
• Guidance through the entire AI / ML roadmap. From ideation to deployment, consultants offer expert oversight, ensuring that projects follow best practices and methodologies. They can also help make approximate time and budget estimates for each development step, as well as set requirements for team composition and divide the project into phases with clear deliverables for each. This structured approach accelerates time-to-value and fosters innovation.
• Assistance with data preparation. Data is the backbone of AI. Consultants aid in advising on curating, cleaning, and structuring data, ensuring the resulting models are both accurate and robust. Besides, they can advise on what data to collect, e.g., formats, volumes, etc., and even run data collection and cleansing themselves. If you doubt how to proceed with gathering appropriate data, don’t hesitate to request assistance from professional AI engineers.
• Assistance with testing. Through rigorous validation techniques, AI consultants ensure that developed models meet performance benchmarks, avoiding costly mistakes and ensuring real-world reliability.
• Identifying and mitigating potential pitfalls and challenges. With a wealth of experience, AI consultants can foresee common challenges, advise preemptive solutions, and help to sidestep costly setbacks.
• Provision of ethical assurance. In a world increasingly conscious of technology’s impact, consultants guide on deploying AI responsibly, ensuring solutions are fair, transparent, and in line with regulatory standards.

The listed reasons are completely valid even if you have AI expertise in-house. Partnering with a tech vendor is a must when your organization is not extremely well-versed in innovative technology as it’s the only way you can achieve high outcomes and avoid costly mistakes.

If you’re unsure where to start with the adoption of AI solutions, don’t hesitate to reach out to qualified AI and ML experts from Intelliarts.

Types of AI solutions for businesses

AI consultancy should serve a clear goal of advancing the business with advice on integrating one or another technology. Here are some of the AI solutions businesses may consider utilizing:

1. Predictive analytics

What’s this? Leveraging historical data to forecast future trends, businesses can anticipate customer needs or market shifts.

Examples: Forecasting stock demands, predicting market changes, customer behavior, or equipment failures.

2. Chatbots and virtual assistants

What’s this? Automated tools that handle customer queries, enhancing customer service efficiency.

Examples: Customer support chatbots on e-commerce sites that address common inquiries.

3. Recommendation systems

What’s this? Algorithms suggesting products or content based on user behavior.

Examples: Netflix’s movie suggestions or Amazon’s product recommendations.

You can learn how big data is used in Amazon’s recommendation system from the video below:

4. Natural Language Processing (NLP) tools

What’s this? Software that understands and generates human language, aiding in content analysis or creation.

Examples: Sentiment analysis tools gauging customer reviews or document data extraction.

5. Image and video recognition

What’s this? Technologies for identifying patterns within visual data.

Examples: Face recognition for security purposes or defect detection in manufacturing.

6. Autonomous robots and drones

What’s this? Machines capable of independent operation, optimizing task completion.

Examples: Drones for inventory management in large warehouses or robots assisting in surgeries.

7. Fraud detection systems

What’s this? Algorithms that can identify unusual patterns to detect and prevent fraudulent activities.

Examples: A banking system flagging suspicious transactions, telecom fraud detection, fraud detection in insurance, or Anti-Money Laundering (AML).

8. Sales forecasting

What’s this? Using AI to predict future sales, helping businesses to prepare and strategize.

Examples: E-commerce platforms anticipating holiday sales surges or manufacturing companies predicting seasonal demand.

9. Supply chain optimization

What’s this? AI-driven tools that ensure efficient product movement from manufacturers to consumers.

Examples: Predictive tools optimizing shipping routes or restocking schedules.

10. Business Process Automation (RPA)

What’s this? Software robots that perform repetitive tasks, increasing operational efficiency.

Examples: Bots entering data into CRM systems or automating invoice processing.

There are multiple other solutions, and more emerge over time. Having a consultation with a knowledgeable provider can shed some light on the actual prospects for your business, revealing the overlooked digitalization opportunities.

Selecting the right AI consulting partner

When selecting a company that will serve as a driver of your digital transformation, it’s crucial to choose the right fit rather than just the most well-known company. Guide your choice with the following considerations:

Expertise and experience. Evaluate the chosen consultant’s past AI projects to ensure they have the skill set tailored to your needs and are familiar with solutions similar to the one you’re keeping in mind.

Reputation and references. Assess the firm’s track record by seeking feedback from past clients or evaluating case studies. You may also consider exploring the Clutch platform or similar resources to gather customer reviews.

Domain-specific knowledge. Ensure the consultant understands the nuances of your industry, which can be instrumental in crafting AI solutions that truly resonate. In case you don’t observe the application of the desired expertise in the provider’s past projects, consider asking them for relevant cases.

Costs and pricing structure. Balance the value the provider can bring to your business with their pricing policy. You should ensure that there are no hidden costs about which you may not be informed and that the project quote aligns with your budgetary constraints.

Flexibility and scalability. In case you expect changes in the scope of the project as it goes, it would be better if you pick a consultant who can adapt to changing project requirements. Such a provider should have the capacity to handle growth or increased demands as your AI initiatives expand.

Most likely, you’ll end up having several suitable providers on your shortlist. The reasonable action here is to have an initial consultation with representatives of each company. Ask them the questions listed below to validate their expertise and fit.

• What previous AI projects have you worked on?
• Can you provide case studies or references?
• How do you handle data security and privacy?
• What is your pricing model?
• What is your perspective on the ethical use of AI?

Include other questions to address all uncertainties about companies you may have, gather additional information, or just make any significant matters clear. Choose AI consultants that are better suited to the intended project and your culture, maintain clear communication, have relevant expertise, and are generally the most trustworthy, and you’ll succeed.

Process for implementing AI consulting

Now, let’s get to know the process for crafting an innovative solution or adopting an ML model into your workflow when partnering with an AI consulting company. The involvement of the invited vendor may vary from solely consulting impact to execution of some or even all implementation stages, which include the following:

#1 Business analysis

The foundation of any AI-driven project starts with a comprehensive business analysis. This involves understanding the company’s objectives, current workflows, pain points, and potential AI opportunities. By collaborating closely with stakeholders, AI consultants can:

• Identify key areas where AI can have the most significant impact
• Provide a clear vision for the subsequent stages of the process

This phase is crucial for aligning the AI solution with the business goals and ensuring a meaningful transformation.

#2 Data collection and processing

Data is the cornerstone of creating and implementing AI solutions. In this stage, data from various sources is gathered, be it from internal databases, external platforms, or even IoT devices. Once collected, it undergoes a rigorous cleaning and preprocessing routine. Missing values are handled, outliers are identified, and data is transformed into a format suitable for model building. Proper data collection and processing ensure the AI models will be both reliable and accurate.

#3 Model building

With clean data in hand, the AI consultancy delves into the construction of a machine learning model tailored to the business’s specific needs. This involves:

• Data utilization. Using cleaned data for model creation
• Algorithm selection. Picking the right algorithms for specific tasks
• Training phase. Teaching the model how to use the data
• Optimization. Adjusting for better performance
• Refinement. Continuous model tweaks for accuracy

Throughout this process, the model needs to be refined multiple times to ensure it captures the patterns and insights embedded in the data.

#4 Testing and validation

Before deploying the AI solution in a live environment, it undergoes thorough testing. This step involves:

• Testing on a separate dataset. Testing on a new, unlabeled dataset that imitates real-life data
• Accuracy check. Ensuring the model’s predictions are reliable
• A/B testing. Ensuring the higher effectiveness of the latest model version in the production environment
• KPIs and business objectives evaluation. Measuring the obtained outcomes against set KPIs and objectives
• Performance evaluation. Assessing the model’s overall capabilities
• Issue identification. Spotting and rectifying discrepancies
• Deployment preparedness. Ensuring readiness for live implementation

If the results are not satisfactory, there will be the need to repeat the steps of data collection and processing, model training, and model testing. When any discrepancies or issues identified during this phase are addressed, this ensures the model is ready for real-world challenges.

#5 Implementation

Once tested and refined, the AI model is integrated into the existing systems and workflows of the business. This phase may involve software development, infrastructure adjustments, and user training.

The main goal here is to ensure seamless operation, where the AI solution enhances the business processes without causing disruptions. It may take time to integrate the AI solution into the digital infrastructure, so for a business, it’s better to strategize for downtimes in advance.

Additionally, an organization’s team will likely require training on how to work with a newly introduced solution, e.g. AI lead generation. Consulting service providers typically offer staff training.

#6 Monitoring and maintenance

Post-implementation, it’s vital to keep a close watch on the AI solution to ensure it continues to operate as expected. Monitoring tools are put in place to track performance, and any anomalies or deviations are addressed promptly.

Maintenance might also involve periodic retraining of the model, especially if new data becomes available or if the business environment changes.

#7 Continuous improvement

The world of AI is dynamic, with new advancements and techniques emerging regularly. As such, it’s essential for businesses to:

• Stay updated. Keeping abreast with AI advancements to timely detect the need for a new feature or any other modification
• Conduct model refinements as necessary. Undergoing stages of data preparation, training, and testing, based on new data or feedback
• Consider and implement feedback. Using real-world results to iterate
• Seek opportunities. Finding new areas for AI enhancement

By carrying out these activities businesses can ensure their newly introduced AI solutions will have an extended lifecycle and stay relevant for a longer period.

Are you looking for expert development assistance? Contact Intelliarts and let our team of AI engineers contribute to your best project.

Top AI consulting companies

If you’re choosing an AI consultant and not quite sure what companies to include in your shortlist, here are a few worth keeping an eye on:

Intelliarts as your AI and ML consulting partner

Intelliarts is a leading artificial intelligence consulting and development company. With more than 20 years of proven experience and over 85% of recurring customers, we are ready, willing, and able to assist you with your innovative development project, provide AI consulting services, or lend your in-house team a helping hand by sharing the workload.

Find out what customers of Intelliarts have to say about our partnership and review our portfolio via the Intelliarts Clutch page.

In addition, the Intelliarts team actively participates in the digital transformation of businesses. Get our free copy of the predictive maintenance white paper to learn how you can reduce downtime by 50% while also saving up to 30% on maintenance costs with the help of ML solutions.

Final take

In this post, we had AI consulting explained in great detail. This service line comes in use whether you own a technology company yourself or don’t have in-house expertise in AI and ML. Your AI partner will guide you through the entire IT solution implementation process and ensure your investments in your project will bring you an increase in revenue.

Selecting the right AI consulting partner is half the battle. Let the Intelliarts team aid you with our vast experience and expertise in innovative technologies and smart solutions. Our ML engineers will ensure that you undergo the digital transformation with flying colors.

Need AI consultancy?

Let's talk