Few companies understand how to implement AI in business and scale value
Most companies lack the experience, personnel, and technology to get started with AI and unlock its full business potential. Unless they collaborate with experienced AI consultants, of course.
According to Deloitte’s 2020 survey, digitally mature enterprises see a 4.3% ROI for their artificial intelligence projects in just 1.2 years after launch. Meanwhile, AI laggards’ ROI seldom exceeds 0.2%, with a median payback period of 1.6 years. Another study conducted by Fortune journalists found that, while 90% of businesses have already begun to use artificial intelligence, only 20% of companies achieve tangible results from AI implementation.
Enterprises that see measurable and quick returns on their artificial intelligence investments establish the foundation for AI initiatives from the outset.
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PwC echoes the sentiment, claiming that AI leaders take a holistic approach to AI development and implementation and tackle three business outcomes—i.e., business transformation, systems modernization, and enhanced decision-making—all at once.
Integrating AI into your business operations can position you as a leader in your industry. However, achieving success is no quick fix. You can avoid common pitfalls and ensure a smoother AI implementation journey by adhering to ITRex’s practical steps.
How to implement AI in business: a 5-step guide for companies undergoing intelligent transformation
Here’s how to implement AI in business:
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Familiarize yourself with the capabilities and limitations of artificial intelligence
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Identify your goals for implementing AI
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Assess your company’s AI readiness
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Start integrating AI into select tasks and processes within your organization
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Learn from your mistakes and aim for AI excellence
Scroll down to learn more about each of these AI implementation steps and get in touch if you need help with your project.
Step 1: Familiarize yourself with AI’s capabilities and limitations
Companies eyeing AI implementation in business consider various use cases, from mining social data for better customer service to detecting inefficiencies in their supply chains.
On a broader scale, the use of artificial intelligence in business falls to:
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Scheduling
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Forecasting (including “if-else” analysis)
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Process enhancement and automation
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Resource management and allocation
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Reporting
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Cybersecurity management
This list is not exhaustive as artificial intelligence continues to evolve, fueled by considerable advances in hardware design and cloud computing.
Algorithms that facilitate or take over standalone tasks and entire processes differ in their data sourcing, processing, and interpretation power—and that’s what you need to keep in mind when working on your AI adoption strategy.
Let’s take supervised machine learning, for instance. AI engineers could train algorithms to detect cats in Instagram posts by feeding them annotated images of our feline friends. When faced with unfamiliar objects, these algorithms fall badly short.
However, if we remove labeled data from the ML model training process, we will end up with unsupervised machine learning algorithms that crunch massive amounts of data—again, using cat picks as an example—to produce meaningful insights. Unsupervised ML models still require some initial training, though. For instance, we could tell algorithms that a particular database contains images of cats and dogs only and leave it up to the AI to do the math.
If you’re considering AI implementation in your company, you should also be aware of reinforcement learning. This technique involves releasing algorithms into the wild to propose solutions to business problems and learn from their own mistakes. This type of AI can help summarize long texts or predict stock market trends.
Finally, there are deep neural networks that make intelligent predictions by analyzing labeled and unlabeled data against various parameters. Deep learning has found its way into modern natural language processing (NLP) and computer vision (CV) solutions, such as voice assistants and software with facial recognition capabilities.
There’s one more thing you should keep in mind when implementing AI in business.
Regardless of the accuracy of artificial intelligence solutions’ predictions, human specialists are necessary to oversee the AI implementation process and steer algorithms in the correct direction in certain cases. For instance, AI can save pulmonologists plenty of time by identifying patients with COVID-related pneumonia, but it’s doctors who end up reviewing the scans to confirm or rule out the diagnosis. And behind ChatGPT, there’s a large language model (LLM) that has been fine-tuned using human feedback.
There are several areas where implementing AI makes little sense without efficient monitoring:
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Generating creative content, such as opinion articles and conversion-optimized copy. OpenAI’s fans may challenge us on this, but we’re convinced algorithms will not be able to produce story-driven strategic content anytime soon.
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Coding complex software systems. Tools like GitHub Copilot and Tabnine can indeed predict and suggest lines of code inside your editor. Meanwhile, the omnipresent ChatGPT enhances or automates many aspects of programming, such as debugging and code annotation. However, we don’t recommend entrusting software development tasks to AI unless you have your code reviewed by senior software engineers. This recommendation is also applicable to AI-powered no-code and low-code development tools.
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Making judgments and ethical decisions independently. For better or for worse, artificial intelligence systems do not possess consciousness, intuition, or emotions. That’s why algorithms can miss nuances and context when analyzing data and acting on it. Getting rid of artificial intelligence bias is another issue to be addressed before AI implementation in business.
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Developing innovative, unconventional solutions to real-world problems. Out of sheer curiosity, we asked ChatGPT to investigate the micro-environmental factors influencing our company’s operations in the volatile IT market and devise a bold marketing strategy. Despite the well-crafted prompts, the algorithms barely scratched the surface of what our marketing department can do when faced with a similar task.
So, if you’re wondering how to implement AI in your business, augment your in-house IT team with top data science and R&D talent—or partner with an outside company offering technology consulting services.
Step 2: Define your goals for AI implementation
To start using AI in business, pinpoint the problems you’re looking to solve with artificial intelligence, tying your initiatives to tangible outcomes.
For this, you need to conduct meetings with the organization units that could benefit from implementing AI. Your company’s C-suite should take part in and drive these discussions.
Also, review and assess your processes and data, along with the external and internal factors that affect your organization. To that end, you could use several techniques and frameworks.
These include the TEMPLES micro- and macro-environment analysis, the VRIO framework for evaluating your critical assets, and SWOT to summarize your company’s strengths and weaknesses. Another great tool to evaluate the drivers and barriers to AI adoption is the Force Field Analysis by Kurt Lewin. This list is not exhaustive; still, it could be a starting point for your AI implementation journey.
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Experts believe you should prioritize AI use cases based on near-term visibility and financial value they could bring to your company. That’s why you need specific objectives and ways to measure them.
Going back to the question of payback on artificial intelligence investments, it’s key to distinguish between hard and soft ROI.
Here’s the hard ROI your company could achieve from implementing artificial intelligence:
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Time savings driven by the automation of laborious tasks
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Productivity gains stemming from AI-assisted decision making
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Labor and operational cost reduction due to increased automation and employee productivity
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Revenue increase thanks to the customer base growth and higher value of provided services
The soft ROI artificial intelligence adoption could provide spans:
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Personalized client experience, which positively affects customer satisfaction and loyalty
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Skills retention, which revolves around constant research and validation of new AI implementation concepts and contributes to the development of in-house artificial intelligence capabilities
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Organizational and digital agility, which empowers your employees to revamp technology systems and entire workflows in response to new challenges and opportunities
All the objectives for implementing your AI pilot should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, your company might want to reduce insurance claims processing time from 20 seconds to three seconds while achieving a 30% claims administration cost reduction by Q1 2026.
To set realistic targets for AI implementation, you could employ several techniques, including market research, benchmarking against competitors, and consultations with external data science and machine learning experts.
Step 3: Evaluate your AI readiness
The artificial intelligence readiness term refers to an organization’s capability to implement AI and leverage the technology for business outcomes (see Step 2).
Once you’ve identified the aspects of your business that could benefit from artificial intelligence, it’s time to appraise the tools and resources you need to execute your AI implementation plan.
According to Vitali Likhadzed, ITRex CEO and Co-Founder, your AI implementation strategy will rely on five key building blocks:
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AI development talent. Do you have in-house IT specialists and subject matter experts (SMEs) who know how to implement AI—both on the tech and business side—within a timeframe specified in the previous step? If not, do you have a budget to outsource AI development to a third party or purchase and deploy a SaaS solution? With the latter option, though, you’ll still have to hire AI developers to configure and customize the software.
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Software development, procurement, and maintenance costs. Depending on your business objectives, you could opt for a SaaS-based artificial intelligence tool or take the custom software engineering route. Both approaches have their advantages and downsides, such as the trade-off between longer AI implementation cycles and limited customization options. The total cost of ownership (TCO) for AI systems, either bespoke or SaaS-based, will also include vendor and maintenance fees, as well as the price of setting up and operating a cloud infrastructure (more on that later). The cost of SaaS-based data analytics platforms, for instance, could range between $10,000 and $25,000 per year, with licensing costs comprising a small fraction of the final estimate.
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Data. AI algorithms are only as good as the data you feed them. Images, videos, audio files, PDF documents, sensor readings, and other data that are hard to interpret and modify (i.e., unstructured data) comprise up to 90% of all information stored across your company’s IT infrastructure. Locating, aggregating, and preparing data for algorithm training is a critical step in developing accurate, high-performance solutions and implementing AI in business.
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Computing and storage resources. Microsoft Azure, Amazon Web Services, Google Cloud, and other prominent cloud computing vendors provide the resources to train, deploy, and run machine learning models in the cloud. Your data will live in the cloud too—in a neatly organized data warehouse, in data lakes, or in hybrid data storage solutions known as data lakehouses. Tapping into cloud computing services is thus key for the implementation of AI. And you should configure your cloud infrastructure properly; otherwise, the cost of implementing AI may exceed your potential revenue.
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Employee training. Even if you partner with experienced AI developers, you’ll still have to educate employees on the new technology so that they can perform their jobs effectively—both now and in the future, when you get close to enterprise-wide AI adoption.
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According to Intel’s classification, companies with all five AI building blocks in place have reached foundational and operational artificial intelligence readiness. These enterprises can carry on with the AI implementation plan—and they are more likely to succeed if they have strong data governance and cybersecurity strategies and follow DevOps and Agile delivery best practices.
If your organization does not meet these criteria, you could partner with a digital transformation services company to upgrade your IT infrastructure and consider AI implementation options.
Step 4: Start integrating AI into select processes and while planning for scale
At ITRex, we live by the rule of “start small, deploy fast, and learn from your mistakes.” And we suggest our customers follow the same mantra—especially when implementing artificial intelligence in business.
Gartner reports that only 53% of AI projects make it from prototypes to production. One reason for this may be companies’ failure to replicate the results they’ve achieved with their AI proof of concepts (POCs) in sterile test environments in real life, with algorithms consuming data from multiple sources and enhancing different processes.
A pragmatic approach to integrating AI into business is to have a bigger picture in the back of your mind instead of focusing on isolated POCs for the selected use cases, even though the latter might look like a low-hanging fruit compared to ambitious moonshot initiatives.
By creating a blueprint for your company-wide AI adoption strategy early on, you’ll also avoid the fate of 75% of AI pioneers who could go out of business by 2025, not knowing how to implement AI at scale.
Also, a reasonable timeline for an artificial intelligence POC should not exceed three months. If you don’t achieve the expected results within this frame, it might make sense to bring it to a halt and move on to other use scenarios.
Step 5: Achieve AI excellence
After launching the pilot, monitoring algorithm performance, and gathering initial feedback, you could leverage your knowledge to integrate AI, layer by layer, across your company’s processes and IT infrastructure.
For this, you need to set up:
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A robust data governance framework ensuring secure and efficient data management across your entire company
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An integrated data ecosystem for collecting, storing, and organizing information for algorithm training
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An AI excellence center where your in-house team will work hand in hand with third-party experts, acquire new skills, continuously improve AI performance, and test new concepts
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A foundation facilitating Agile decision making and continuous business process redesign: as artificial intelligence will enhance or automate more processes within your organization, you’ll need to validate that humans and AI agents augment and complement each other’s work
The incremental approach to implementing AI in business could help you achieve ROI faster, get the C-suite’s buy-in, and encourage other departments to try out the novel technology.
Understanding artificial intelligence is the first step towards leveraging this technology for your company’s growth and prosperity.
How to implement AI in business: closing thoughts
Innovation for its own sake won’t do your company any good.
Sometimes simpler technologies like robotic process automation (RPA) can handle tasks on par with AI algorithms, and there’s no need to overcomplicate things.
In other cases (think AI-based medical imaging solutions), there might not be enough data for machine learning models to identify malignant tumors in CT scans with great precision.
And occasionally, it takes multi-layer neural networks and months of unattended algorithm training to reduce data center cooling costs by 20%.
Even if your company invests in generative AI development services, artificial intelligence won’t magically increase your employees’ productivity and boost your bottom line. Yet, AI technologies have solid potential to transform your organization from within, unlocking opportunities for optimization, cost reductions, and growth.
AI implementation FAQs: addressing unanswered questions
1. How do I calculate the total cost of AI implementation beyond development expenses?
AI implementation costs are not limited to development. Your company will also need to factor in:
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Infrastructure costs (cloud computing, on-premise servers, or hybrid storage solutions)
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Data collection and preparation costs (including the expenses associated with cleaning, labeling, and integrating structured and unstructured data)
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Ongoing support and maintenance (fine-tuning custom models, retraining closed-source and open-source AI algorithms, and keeping data pipelines functional)
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Regulatory compliance expenses (ensuring your AI adheres to industry-specific regulations like HIPAA in healthcare or financial compliance laws)
Understanding these costs upfront can help you avoid budget overruns and ensure a more successful AI implementation.
2. How can businesses ensure AI models remain unbiased and ethical?
AI bias is caused by skewed training data or poorly defined algorithms. To minimize bias, you should:
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Use diverse, representative datasets and conduct regular bias audits
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Create AI governance frameworks that follow clear ethical guidelines
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Evaluate AI decisions across various demographics and edge cases
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Implement human oversight in AI-driven decision-making, especially in sensitive fields like healthcare, finance, and HR
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Engage third-party auditors to review the model’s fairness and compliance
3. What are the security risks associated with AI, and how can they be mitigated?
Besides traditional software security vulnerabilities, integrating AI into business systems introduces new cybersecurity challenges, such as:
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Adversarial attacks, where hackers manipulate inputs to mislead AI models
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Data poisoning, where bad actors introduce biased or malicious data to corrupt AI decision-making
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Model inversion attacks, in which attackers derive sensitive data from AI models
Mitigating these unique security risks of AI implementation involves:
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Encrypting AI model data and using secure APIs
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Scanning AI systems for vulnerabilities through red teaming and penetration testing
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Implementing robust authentication and access control mechanisms
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Using explainable AI (XAI) principles to detect anomalies and unexpected behavior in AI decisions
4. How do I integrate AI into legacy systems without causing disruptions?
Integrating AI into legacy infrastructure can be challenging, but businesses can take a phased approach to minimize risks:
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You could first complement existing workflows using robotic and intelligent process automation (IPA) solutions
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Next, you should integrate AI capabilities into existing software via APIs instead of replacing core systems
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Combining cloud-based AI models with on-premise legacy systems could help balance scalability and security
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Consider replacing outdated components one by one rather than overhauling everything at once
5. What are the biggest misconceptions about AI implementation?
Many businesses have unrealistic expectations regarding AI. Below, we will debunk some of the most common misconceptions.
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“AI will immediately replace human jobs.” In reality, AI enhances human work rather than replacing it entirely, especially in critical decision-making roles.
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“AI can be implemented without high-quality data.” AI models are only as good as the data they are trained on. Without clean, structured data, AI’s effectiveness is limited.
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“AI provides instant results.” AI implementation requires continuous testing, refining, and training before delivering significant ROI.
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“Pre-built AI solutions work for every business.” Many AI and generative AI models require customization to meet specific business needs, and off-the-shelf solutions may not always address your needs adequately.