It’s difficult to imagine anyone letting AI take over business forecasting in any company, however small. But that is not what “AI in business decision-making” really means.
Contrary to what we believed 2 years ago, AI isn’t going to “take over” human/business processes. It’s here to strengthen those processes and simplify tasks. Automating routine tasks will allow employees to focus on perfecting other processes.
Decision-making and planning seem too crucial to entrust to anyone else, yet it’s surprising how often leaders overlook it in favor of more immediate necessities. 85% of business leaders also go through decision stress, according to reports by Oracle and Seth Stephens-Davidowitz.
For three-quarters of them, the daily volume of this stress has also increased tenfold in the past 3 years. We need AI to close the data and business insights gap.
What is AI in Data-Driven Business Decision Making?
The principal way of utilizing AI for business decisions is to use algorithms, machine learning, and data analytics to extract insights from large datasets. Businesses collect vast amounts of data from various sources, including customer interactions, market trends, sales performance, and supply chain activities. This data can be overwhelming to process manually. AI tools analyze this information, identify patterns, and offer actionable insights.
For example, AI can help companies understand customer behavior, predict market trends, optimize inventory, and improve marketing strategies. AI tools use complex algorithms to process data quickly and present findings in an understandable format.
Generative AI can also analyze the probabilistic formats of text, image, video, and code. It can summarize and discuss this material and create hypothetical situations based on this input.
What Are The Benefits of AI in Business Decision-Making?
Most business decisions are taken in time-sensitive, high-pressure scenarios. Having accurately analyzed data at your fingertips is a huge advantage.
Accuracy: AI tools can analyze data without human biases.
Efficiency: AI speeds up data processing regardless of data complexity.
Scalability: AI can handle vast datasets and extract specific insights irrespective of the data volume.
Improved Predictive Analytics: Machine learning models can forecast market trends, customer behavior, and key performance indicators (KPIs).
Why Can AI Systems Be Expensive?
Implementing AI in business decision-making is prohibitively costly sometimes. While big tech or established companies have the leeway to take such a financial risk, small businesses don’t. They lose out on the ability to make faster and better decisions.
Here’s why:
Initial Setup Costs:
Setting up an AI system requires substantial software, hardware, and infrastructure investments. Advanced AI tools need high computing power and storage, so leaders need to buy expensive servers or pay for cloud services.
Skilled Talent:
The demand for AI experts, including data scientists, machine learning engineers, and AI developers, is high. Hiring these professionals is expensive, especially if sourced locally in regions with high labor costs like North America and Western Europe. AI specialists command high salaries due to their expertise in complex fields like deep learning, natural language processing, and predictive analytics.
Data Collection and Management:
Collecting, cleaning, and processing data is labor-intensive. A proper data management system also comes with its expenses. Maintaining data privacy and security also involves compliance with regulations and additional investment in cybersecurity measures.
Training AI Models:
Substantial computational power is needed to train AI models, especially for deep learning models. Training large datasets on complex algorithms can take days, if not weeks, and may require specialized hardware like Graphics Processing Units (GPUs). The costs of cloud-based computational resources add up over time.
Why AI Should Not Be Abandoned Due to High Costs
Abandoning AI implementation solely due to high costs isn’t the best way ahead. While AI adoption might seem expensive, it doesn’t need to be.
Global talent lowers financial barriers. One way to cut costs is to hire skilled professionals from regions with lower labor costs. Many businesses have found that by building remote teams in countries like India, the Philippines, and Latin America, they can access AI expertise without the high price tag.
What Are The Benefits of Hiring Global Talent for AI Implementation?
Cost Savings:
Countries in Asia, Eastern Europe, and Latin America offer talented AI professionals at a fraction of the cost compared to hiring locally in the U.S. or Western Europe so that small businesses can work with extremely gifted AI experts at a fraction of local prices.
Skilled Workforce:
Countries like India and the Philippines have a STEM-centric education system focused on output and growth. There is a steady supply of AI professionals capable of delivering quality work. India’s amazing tech startup culture also contributes to the solution-oriented mindset of Indian engineers.
Diverse Perspectives:
Diversity produces innovation. Hiring globally brings diverse perspectives and solutions to AI problems. A multicultural team can approach data analysis and strategic planning with a fresh eye.
Access to Global Markets:
A team in a different time zone and region allows businesses to cater to a more global customer base. They can tailor their AI-driven strategies to other markets. They will likely be able to read AI analysis of different markets more accurately. This gives businesses an added layer of security.
How to Build an Offshore Remote Team of AI Experts
Building an in-house offshore remote team is a game-changer for businesses looking to integrate AI into their decision-making processes. With this simple solution, we can combine the benefits of global talent with direct control over the work processes. Here’s how it can make data-driven decision-making easier for small businesses:
1. Cost Efficiency
Setting up an offshore team in countries known for their technical expertise and lower labor costs permanently solves the problem of AI expertise costs. Most businesses can hire top talent at a more affordable rate in offshoring. India, for instance, offers skilled AI professionals whose salaries are considerably lower than those in the U.S. or Europe.
2. Scalability
Businesses can start with a small team of AI experts and expand as their needs grow. This is more feasible than committing to the high costs associated with an all-local, in-house team. Offshore teams can also work around the clock, ensuring that projects move forward even when the onshore team is offline.
3. Direct Oversight and Control
Unlike traditional outsourcing, having an in-house team means the business controls hiring, training, and workflow management. Companies can align their AI projects directly with their strategic goals, ensuring that their AI applications fit the company’s unique needs. Apart from being maintained in-house, companies have complete visibility into every aspect of the work. It’s not possible to get this amount of transparency with traditional outsourcing.
4. Tailored AI Solutions
An in-house offshore team works very closely with the business, understanding its specific challenges and requirements. Creating more customized AI solutions tailored to the company’s strategic objectives is easier. Small companies can rapidly adapt their AI models to new data and evolving business conditions without needing extensive or scrupulously scrutinized plans.
5. Focused Data Management
Data is the foundation of decisions. An in-house offshore remote team can establish a streamlined data management system focusing on the company’s unique datasets. This team can handle data collection, processing, and analysis. That way, only high-quality, relevant data will inform business strategies. Errors will also be minimized, leading to more precise insights.
6. Security and Compliance
Businesses can implement their security protocols and data compliance policies with an in-house offshore remote team to reinforce confidentiality and adherence to data privacy regulations. These are important to safeguard sensitive business information. Overall, your data is much more protected when working with an offshore team, and that’s one worry off your plate.
How To Build an In-House Offshore Remote AI Team
Once you’ve decided to build an offshore AI team, the most vital thing to consider is who your offshoring service provider will be. The journey promises to be smooth and exciting with the right provider.
Identify Business Needs: The business’s specific AI needs, such as predictive analytics, customer behavior analysis, or market trend forecasting, need to be pinpointed and considered.
Select the Right Service Provider: The best providers can share their knowledge and expertise in offshore AI team-building to make the process easy and efficient for you.
Select the Right Region: Choose a country or region with a strong pool of AI talent, such as India or Eastern Europe, where professionals are skilled and labor costs are lower.
Recruit Skilled Talent: The service provider might use recruitment platforms or their resources to find qualified AI professionals. Focus on those with expertise in machine learning, data analysis, and algorithm development.
Establish Infrastructure: Provide the remote team with the proper tools and software to perform their tasks well. Cloud-based solutions can facilitate collaboration across different time zones.
Set Management Protocols: The communication channels between the offshore and onsite teams must always be seamless. Fantastic project management tools help teams with this, and onshore leadership can also have better visibility into what’s happening.
Invest in Training: Offer ongoing training to keep the remote team updated with the latest AI trends and technologies. This investment pays off in better, more customized AI-driven solutions for the business.
Conclusion
The availability of global talent makes implementing AI in business decision-making processes easier. Smaller businesses shouldn’t miss out on the opportunities AI provides because it’s too time-consuming or expensive.
The long-term benefits of AI-powered decision-making outweigh these expenses.
And this approach is a game-changer for small businesses. It facilitates the integration of AI into strategic planning and forecasting while simplifying the process and making sure it’s a long-term, ongoing task–not a one-time marvel.
InCommon specializes in recognizing the kind of talent small and medium businesses need to unlock AI’s potential. With us, in-house offshore teams find the opportunity to excel in tech fields that desperately need their talent. Our experts get much-needed chances to put their education and training to good use; businesses find themselves in the enviable position of being able to afford outstanding tech talent at reasonable prices. It’s a win-win for everyone.
