How AI in Business Gives You a Competitive Edge

In this guide, we take a deep dive into the world of AI in business, unearthing some of its greatest strengths and offering guidance into this evolving landscape.

As technology specialists ourselves, we understand that with all of AI’s powerful potential comes relentless responsibility.

For Ntiva, part of maintaining this integrity includes sharing our experiences as digital professionals to empower other businesses to pursue progress in their industries.

This guide provides an overview of all the AI landscape as we know it today. To stay up to date, consider subscribing to the Ntiva blog or the Ntiva Video Resource Center.

Technology Evolution Transforms Modern Business Practices

Up until very recently, company size put small- and mid-sized businesses (SMBs) at a disadvantage in the market when it comes to new technologies. Traditionally, sophisticated software solutions were only available to large enterprise with significant budgets. With the introduction of the cloud and Software-as-a-Service applications, the landscape has changed and now businesses of all sizes can take advantage of new technologies.

Nowadays, the widespread adoption of new technology allows smaller-scale businesses to better compete with their corporate counterparts. This also holds true for AI. There are many AI applications on the market today that are currently free or available for a low cost subscription.

However, there is a difference between what we call “individual AI” applications (ChatGPT, Perplexity, Microsoft Copilot, Gemini and others) and “enterprise AI” applications.

The latter may require a comprehensive upfront strategy which includes understanding the business problems to be solved, developing custom AI solutions if necessary, and last but not least preparing the technical and people environment for these operational shifts.

AI in Business Strategy

This swift evolution of technology directly affects the very foundation of business strategy. The introduction of AI tools for business permeates virtually every industry, creating waves for all departments within an organization. Wherever it’s used, implementation of AI solutions requires a strong AI business strategy that complements the bottom line and supports all stakeholders.

Employing AI solutions for business involves more than creating a
few new standard operating procedures. Developing an AI-conscious business strategy is a key step in a company’s digital transformation. Putting technology such as AI at the forefront of business plans means embracing the strengths of these solutions and understanding their limitations to streamline how business is performed.

For one, AI applications allow businesses to process and collect data faster, making forecasting more accurate and trends easier to predict. This visibility, in turn, inevitably leads to various tweaks and updates to the overall business strategy. Consider these examples:


General Electric (GE) employs AI-assisted sensors in its appliance products that assess historic performance to predict repairs, upkeep needs, and maintenance requirements of “purchase-once investments.” The collected data outputs give GE insight into how to better manage inventory, allocate resources, and optimize service requests.


Insurance companies such as Pacific Specialty use AI-powered data capture to better understand policyholder behavior through advanced analytics. This level of visibility into customer sentiment sustains these companies’ mission to continually create innovative new product offerings.


In both brick-and-mortar and e-commerce settings, retailers such as Amazon and Target offer personalized recommendations based on purchase history, digital footprint details, and buyer behaviors with the assistance of AI. These measures help guide the customer seamlessly through the buyer’s journey and reinforce a positive brand image.


SaaS platform host MDaudit used AI-powered automation to boost the efficiency of its external auditing workflow (EAW), reducing its dependency on antiquated IT systems and manual workflows. The solution,, saves time and improves data accuracy by automatically extracting key patient information from extensive audit letters. This capability allows SMBs like MDaudit to manage a significantly higher volume of demand letters.

As these examples demonstrate, regardless of business size, most companies have the same opportunities to leverage AI in business to achieve similar results.

Recognizing the Growth Potential of AI in Business

Today’s organizations have countless opportunities when it comes to the use of AI in business. By implementing a tech-first business strategy, small- to mid-sized organizations can overcome some of their greatest operational obstacles, including budget restrictions, team misalignment, wasted resources, and more.

Challenges Faced by Modern Businesses

All business leaders or executives have experienced one or more of the following setbacks, which ultimately affect how they fulfill their job responsibilities:


All businesses have limited resources in terms of finances, personnel, talent, and technology. This hindrance can restrict their ability to compete on a larger scale, innovate, or pursue new market opportunities.


Keeping pace with technological advancements is crucial to remaining competitive. Smaller or even mid-sized companies often struggle to integrate new technologies effectively because of complexity, cost, and lack of expertise.


Meeting the increasing expectations of customers in terms of personalized experiences, quality, and service can be demanding— not to mention that accurately assessing customer sentiment requires complex analysis of buyer behavior, trends, and preferences.


Adapting to near-constant regulatory changes and managing risks effectively is vital to maintaining steady growth. Small or limited legal and compliance teams can make this a significant challenge.


With so many options available to prospects, attracting and retaining skilled talent is crucial. In the job market, SMBs often compete with larger organizations that offer more competitive salaries and benefits.


Managing supply chains efficiently can be a challenge—especially in a global context—because of risks such as delays, quality control, and fluctuating costs. The need to optimize supply chain management activities such as procurement processes can further complicate these obligations.

The Role of AI in Overcoming These Challenges

It’s easy to claim that applying AI in business can increase profits and boost staff performance. But naturally, most leaders want to know specifically how these solutions can address their most prominent issues.

Below, we break down how AI can address and mitigate common business challenges:


To assist with demand planning, inventory management, and resource allocation, businesses can use predictive modeling to forecast future sales based on historical sales data, market trends, seasonality, and other relevant factors. This enables these businesses to optimize operations, maximize profitability, and reduce waste.

AI Use Case

AI-driven predictive maintenance uses algorithms to analyze data from machinery to predict failures. Siemens, for instance, uses AI for predictive maintenance to reduce downtime and maintenance costs.


A tech-first business strategy centered around AI best practices allows companies to evolve with technology, not in reaction to it. With access to real- time insights and industry intelligence, AI can anticipate changes and quickly adapt to new trends. This invaluable ability turns reactive problem solvers into proactive strategists focused on flexibility and scalability.

AI Use Case

AI applications in healthcare include supporting diagnosis and treatment decisions, clinical trials, and imaging diagnostics. This leads to enhanced accuracy and speed in diagnosis, better patient outcomes, and reduced healthcare costs.


With its ability to analyze large datasets, AI can leverage browsing behavior, purchase history, demographics, and preferences to segment a customer base. Using this intelligence, marketing teams can develop highly personalized messaging and offers to improve their effectiveness. AI is also capable of fast and accurate customer sentiment analysis, which can be employed for tasks such as churn prediction.

AI Use Case

AI improves efficiency in handling inquiries and personalizing customer interactions based on their needs. For example, Domino’s Pizza uses a chatbot for order taking and tracking, and Hilton Hotels employs an AI assistant for guest queries and bookings.


Using AI technologies such as neural networks and support vector machines, managers can identify potential risks and opportunities for their company and optimize compliance with local and federal regulations. AI risk management functionalities such as horizon scanning, obligation libraries, and regulatory change management help to enhance various areas of risk and compliance while contributing to increased profits, lower costs, and higher customer satisfaction.

AI Use Case

AI in the financial sector primarily focuses on fraud detection through real- time transaction pattern analysis. JPMorgan Chase, for example, uses AI for real-time fraud analysis. In banking, AI is also used to review large sets of transactional data, detect loan application fraud, and perform credit risk modeling. 


In addition to assisting with screening potential candidates and optimizing job descriptions, AI is useful for tailoring recruitment strategies based on diverse cultural norms and job market dynamics. Some businesses are even using AI to assess and predict a prospect’s cultural fit and likely success rate with the company.

AI Use Case

AI tools such as Microsoft’s Project Cortex extract insights from project documents and data, providing actionable recommendations for improved team collaboration and corporate training initiatives.


Similar to its applications in resource allocation, AI models can be used for supply chain management activities such as managing inventory and procurement. By analyzing historical data, global trends, and spending categories, these models can develop sourcing strategies and suggest the best ways to maximize the supply chain accordingly.

AI Use Case

AI analyzes vast amounts of historical sales data to identify patterns and make predictions, enhancing forecasting accuracy and streamlining inventory management. Salesforce Einstein, for example, predicts buying patterns and suggests effective sales strategies to avoid under- and over- stocking of inventory.

AI-Powered Automation

AI-Powered AutomationModern IT and AI solutions for business achieve much more for today’s businesses than overcoming technology setbacks. One of the biggest strengths of AI tools is their incredible aptitude for automation—AI automates repetitive learning and discovery through data, and it does so reliably and without fatigue.

Benefits of Automating Business Operations with AI

Virtually every profession nowadays uses some sort of machine or computer to perform job duties. But in a digitally fueled world, teams must find creative ways to take that technology and effectively apply it to their day-to-day responsibilities.

When businesses sufficiently automate more mundane tasks through advanced automation using AI programs, this is just some of what they can do:


Chatbots and other conversational AI tools can respond in real time, significantly decreasing turnover time for customer inquiries, requests, and service tickets.


Repetitive administrative tasks (such as staff scheduling, payroll, and performance evaluations) can be automated with AI solutions, freeing up time for employees to tend to more pressing matters.


Thanks to automation-enabled features such as personalized recommendations and sentiment analysis, customers can enjoy more productive and pleasant interactions.


AI capabilities can be employed to support various IT initiatives (including help desk, data backup, phishing detection, and risk assessment) with little human oversight.


The efficiency that results from implementing AI tools for business can help even the score between internal personnel, pushing everyone forward. When properly integrated into a business plan, AI functionalities have been known to turn C-level players into B-level players and A-level players into A+-level players.

Common Misconceptions Regarding AI in Business

Noticeably, the concept of AI receives lots of attention in the media today. “Artificial intelligence” has quickly become a buzz phrase. With so much information being shared regarding AI, the facts tend to get jumbled up, sometimes mutating into misguided assumptions.

This can be expected with virtually any new trend, especially when it comes to technology. No one can eliminate the spread of misinformation. We can, however, make an active effort and leverage our expertise to debunk these myths and spread a more truthful message.

Two prevalent misconceptions exist surrounding the widespread adoption of AI in business:

Misconception 1: AI Programs are a

Misconception 1: AI Programs are a "Magic Bullet."

Some people who are new to the concept of AI are under the impression that simply implementing an AI model for operational purposes will solve all their problems with little to no human intervention. Although many tech experts would classify such algorithms as “intelligent,” these models are still nowhere near capable of single-handedly running an organization. This misconception is concerning, because irresponsible or thoughtless interactions with AI can lead to a myriad of organizational threats. As many businesses witnessed with the phenomenon of Shadow IT, Shadow AI has the potential to cause irreparable damage to the business as a whole.

Yes, so far we’ve spoken to the vast potential and various use cases of AI in business, from generative AI in customer service to conversational AI for sales. But don’t let these promises of opportunity take away from an important overarching point: Using AI in business operations requires diligence, preparation, and a robust technological infrastructure. In short, these solutions can’t be implemented overnight. It takes a committed team of professionals backed by requisite experience and knowledge in technology architecture to successfully execute a digital transformation through the use of AI. Only with the proper strategy can businesses make a smooth and safe transition to using AI for business operations.

Misconception 2: AI Tools will Replace Humans.

Misconception 2: AI Tools will Replace Humans.

Perhaps the most persistent misconception regarding AI is that it will eliminate the need for human personnel. To some, that may seem far-fetched, but job insecurity around AI technology is a genuine concern for nearly a quarter of today’s workforce. For example, with generative AI’s expansive content creation capabilities, those in creative positions may worry about the stability of their careers as these models advance.

But using AI in business is less about replacing humans and much more about augmenting their abilities and making their jobs easier. Empirical evidence suggests that “the main effects of AI and automation are on the composition of tasks within a job, rather than on occupations in general.” In other words, AI is set up to take over certain obligations, specifically tasks that can be easily automated, but it has to be overseen by humans. It’s safe to say that responsible businesses likely have no intention of recruiting an all-AI workforce anytime soon.

They will, however, favor job candidates with AI experience who know how to effectively collaborate with these tools for better results. Although AI may not entirely replace people in business, people with the knowledge and willingness to learn about AI technology are expected to replace people who lack the same commitment to innovation.


Examples of AI Solutions in Various Industries

Most of today’s largest technology conglomerates employ some sort of AI, from Microsoft and Apple to Amazon and Meta. AI solutions have countless applications across virtually every industry and job role, including (but not limited to):

Retail and e-commerce


Sales and marketing

Real Estate

Project Management

Software Development


Customer Service

Banking and financial services

Take the following use case scenarios:


Healthcare providers at every level face the challenge of consolidating and reviewing patient information from various sources prior to appointments. This information may include past medical history, notes from previous visits, diagnostic reports, and data from wearable health devices. The traditional, manual process of collecting and accessing this information is time-consuming and error-prone with possible implications for the quality of care provided.

AI-driven systems can transform this process by automatically aggregating and analyzing patient data from electronic health records (EHRs), laboratory results, imaging systems, and other digital health platforms. Before a patient’s visit, the AI system uses natural language processing (NLP) and machine learning algorithms to review the patient’s history, identify relevant information, and highlight critical health trends or risk factors.

This process includes data aggregation (pulling together data from disparate databases), trend analysis (analyzing the patient’s historical data), risk assessment (using the data to forecast potential risks), and personalized preparation (customizing the data presentation on the specific needs of the upcoming appointment).


A patient with a history of Type 2 diabetes and hypertension schedules a routine checkup with their primary care physician. The patient has recently begun wearing a special device to monitor physical activity and blood glucose levels. They’ve also visited a specialist recently and have had several lab tests. The primary care physician has access to a comprehensive overview of this patient’s health status, so they can provide effective care.


One of the most important metrics in customer satisfaction is fast and accurate resolution of questions and problems. An AI-driven customer service solution takes a digital representation of a phone call, email, or even a web form and processes that information by leveraging NLP to understand the content and determine its sentiment. Combined with machine learning, the system can categorize the request, use predictive analytics to identify high-priority cases, and forecast the impact of the request on customer satisfaction. This effort drastically increases the effectiveness and efficiency of processing customer inquiries.

Incorporating AI into customer service processes allows businesses to manage customer inquiries more productively and ensures that each customer receives appropriate support. This not only enhances customer satisfaction but also streamlines operational processes, demonstrating the profound impact AI can have on customer service and other business operations.


A customer emails a support team expressing frustration over a delayed product delivery. The AI system instantly analyzes the email’s sentiment and identifies the customer’s frustration level. After the request is categorized as a high-priority shipping issue, it’s routed directly to the logistics support team. This team, already informed about the customer’s sentiment, prioritizes the inquiry, quickly addressing the issue and promptly communicating a resolution to the customer. The rapid, informed response helps to mitigate the customer’s frustration and prevents potential negative feedback or public complaints on social media.


Another compelling use case example of AI’s impact can be observed in the banking and finance industry, particularly in the domain of risk assessment and fraud detection. The integration of AI in these areas not only enhances the accuracy of fraud detection but also streamlines risk assessment processes, ultimately contributing to more secure and efficient banking operations.

Banks handle huge volumes of transactions daily, making it challenging to manually monitor and identify fraudulent activities. AI-powered systems that use machine learning algorithms can analyze patterns in transaction data in real time to detect anomalies that may indicate fraud. These systems learn from historical transaction data, enabling them to identify fraudulent transactions with high levels of accuracy. For instance, if an AI system notices an unusually large transaction from an account that typically exhibits low transaction volumes, it can flag this as suspicious for further investigation.

The AI process in this case involves several key steps, including risk modeling and pattern recognition that detects deviations from the norm. AI algorithms also analyze a wide range of data, such as credit and transaction history, and employ adaptive learning, in which AI systems continuously learn from new transactions and customer interactions. By leveraging machine learning and data analytics, banks can protect against fraud more effectively and make more informed decisions about credit risk, ensuring a safer banking environment for all customers.


An AI system analyzes a customer’s transaction history and notices that their card is being used simultaneously in multiple countries, a strong indicator of potential fraud. The system immediately flags these transactions and temporarily freezes the account to prevent further activity. Simultaneously, it alerts the bank’s fraud
team and the customer to verify the transactions. This rapid response mechanism significantly reduces the impact of fraudulent activities on both the customer and the bank.


Navigating the Buyer's Journey

Navigating the Buyer's Journey

Decision makers considering which AI solutions to implement for their company face an endless list of programs and applications that claim their AI capabilities will solve operational problems effortlessly. As discussed, this is an empty and unrealistic promise. Businesses should choose their AI solutions based on their specific organizational needs, capabilities, and company culture. Just like no laptop is designed to satisfy every type of person and no car fits everyone’s lifestyle, no single AI tool can address every need of every business.

First and foremost, businesses must know what results they expect from their AI solutions. What do they want AI to help them achieve? They must also know the risks of these tools because these limitations will ultimately dictate which ones have the capacity to support business growth and success.

Considerations of AI Solutions

When evaluating AI business solutions, keep the following in mind:


With so many tools and software programs available, the best way to narrow down your options is to let your teams play around with the technology. This experimentation not only helps familiarize everyone with the AI model’s functions but also allows them to witness its strengths and weaknesses firsthand. Ntiva CEO Steven Friedkin stresses the importance of exploring unfamiliar AI functionalities with an open mind, even for just 15 minutes per day.


Prompt injection attacks involve entering a text prompt into a chatbot that enables the user to perform unauthorized actions, such as gaining access to sensitive data that the AI platform
may have access to or been previously trained on. Be cognizant of what information and systems your team grants AI platforms access to and even what information they enter into tool interfaces such as AI chatbots. As of yet, AI systems don’t truly understand concepts such as data sensitivity or obsolescence. Explaining such concepts to an AI is extremely challenging, if not impossible by today’s standards. Remember that sharing sensitive, proprietary, or confidential data, even with a “secured” AI, can put that data—and the organization—at risk.


Properly evaluate all potential AI vendors and implementation partners before working with them. Confirm data sources, content validity, industry experience, and standardization procedures before closing the deal. Consider software vendors that offer a trial period so you can test the waters before committing.


Your chosen AI solutions need to reliably grow and evolve alongside your business. For example, a flexible, proactive, and scalable AI solution has limited hardware constraints and a robust infrastructure based on collaboration and business alignment.


To protect against the negative consequences of shadow AI, some industry experts recommend only allowing work within sanctioned or preauthorized AI systems. Offering an official list of approved applications can certainly give IT teams more control over what happens in a business’s tech stack. However, a centralized approach also comes with its own potential drawbacks. For example, when people feel monitored, they’re often less likely to experiment and innovate as freely as they would otherwise. The nuances of these decisions require an experienced and strategic perspective.

Strategies for Seamless AI Business Integration

Successful digital transformation depends on the ability to seamlessly integrate new technologies like AI into business strategy. This means prioritizing organizational change management to minimize disruptions and risks to IT infrastructure as the business evolves.

Perhaps most importantly, transformation requires all internal personnel to undergo ongoing corporate training to ensure maximum evolvability and demonstrate how an AI tool will benefit them. This stakeholder engagement can help create an environment for a more successful implementation, build buy-in, and inform prioritization of AI efforts. These measures help to promote a tech-first mindset within the company culture, further encouraging a sustainable infrastructure.

Furthermore, using AI in business requires proactive and regular communication and collaboration. As discussed, AI tools aren’t meant to replace humans but to augment their skills and help them perform their jobs better. Therefore, employees need access to channels in which they can communicate their needs, feedback, and concerns in a safe and structured environment. Staff members deserve to feel supported, not threatened, by the presence of AI in business.

AI-Powered Solutions for Sustained Business Success

By using AI to amplify human skills as opposed to replacing them, businesses guarantee a future-first mindset that sets teams up for success. With this in mind, businesses of any size can leverage AI models to push their limits and gain a competitive advantage.

The promise of AI is transforming the way business is performed and practiced in nearly every industry. With access to technology that was once reserved for huge corporations, smaller companies can maintain progress through the responsible use of AI-powered solutions for numerous tasks and obligations. This positive impact on efficiency is likely to continue—and even expand—for the foreseeable future.

As businesses prepare for an uncertain, AI-fueled future, they must keep in mind the importance of a strong, comprehensive, and agile IT strategy. Only with a progressive, technology literate approach to business can brands anticipate the numerous changes in store for them.

That’s where Ntiva AI consulting comes in. Our AI consulting services are centered on helping organizations weave AI into their current business processes to drive efficiency and growth.


Ready to push your business into the future? Schedule an AI consultation with the tech experts at Ntiva today.

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