AI Implementation: Not as Simple as “Just Use ChatGPT”

AI Implementation

The importance and prevalence of AI over the last few years have skyrocketed, placing us on the edge of a new era where business, industry, and the rest of the world will never be the same again.

In the words of Nick Bostrom, machine intelligence is the last invention humanity will ever need to make. And that was back in 2019.

You’ve probably seen the explosion of AI services and platforms over the last few years. ChatGPT, released in November 2022, was the fastest-growing app in history, hitting 100 million users in just two months.

Even though AI has existed since 1957, it’s safe to say that the technology has finally found its space in the mainstream spotlight, both in the eyes of businesses and consumers worldwide.

It’s transformed from tech jargon into a catchphrase resonating everywhere as a household name. And it’s working wonders for businesses.

83% of companies say that AI is a top priority in their upcoming business plans, with 77% of companies exploring and/or using new AI platforms and estimated global AI spending forecasted to reach $154 billion in 2023 alone.

AI technology is changing the game, but there’s a problem.

Experimentation and use of ChatGPT and open-source tools for digital transformation are just the tip of the iceberg. Contact us to get a comprehensive assessment and implementation strategy to learn where AI can make the biggest impact on your core business.

Modern businesses, specifically small to mid-sized companies, often view AI as an easy fix to a company’s issues, rushing to integrate AI into their workflows in the hope of instant optimization.

The issue is that without strategic implementation, the grand promises of AI can’t be fulfilled – which can do more harm to a business than good.

Untouched marble is nothing but a rock, and AI isn’t too different. It needs precise crafting to facilitate smooth operations, intelligent decisions, and bespoke experiences.

In this guide, we’ll explore how to harness AI’s potential not by adopting it and ‘letting it do its thing’ but by strategically deploying it to enhance your business while avoiding pitfalls.

Let’s dive into the process of making AI truly work for you.

An Introduction to AI Implementation

AI implementation has become increasingly relevant as technology continues to advance. To tap into the true potential of AI, you must follow a step-by-step approach to integrating the technology effectively within your organization.

AI implementation is about incorporating AI tools into your systems and understanding your organization’s requirements, selecting the right AI solutions, fine-tuning your strategy, and continuously monitoring and refining implemented solutions.

Successful AI implementation will help you achieve your business goals and improve overall productivity. Poorly implemented AI will leave you with various problems and a loss of time, money, and energy.

Maybe even worse.

Let’s take a look at an example: Company A.

In the quest to enhance business operations, Company A identifies AI as the way forward.

The prospects of automating their content creation process, upgrading their customer engagement mechanisms, and employing a diverse range of marketing tools and apps for managing employees spark excitement in the management.

The senior leadership embarks on a mission to acquire these tools. They commence by researching the best tools in each area, purchasing annual subscriptions, and deploying them across departments to kickstart their usage immediately.

This sudden shift intends to catapult the company into new heights of success — to usher in a phase of growth and expansion unprecedented in its history.

Although the underlying rationale—AI’s potential to revolutionize business performance—is valid, Company A’s approach exhibits a common pitfall: hasty and abrupt AI implementation.

Despite their enthusiasm and expectations of immense gains, the likelihood of Company A experiencing setbacks is high.


Because they bypassed a crucial aspect of AI implementation — meticulous planning and strategic execution.

Embarking on the AI journey is undoubtedly exciting, laden with promises of greater efficiency, cost reduction, and increased innovation.

However, the pathway to reaping these benefits involves more than just choosing and deploying AI tools. It entails aligning AI with your business strategy, preparing your organization for the shift, and iteratively monitoring and refining the AI tools post-implementation.

Thus, adopting AI tools without a clear understanding of your business needs or a strategy to guide AI implementation can lead to more harm than good, underscoring the importance of careful planning and mindful implementation for realizing the true potential of AI.

The Issues with Poor AI Implementation

The problems you’ll face with poor AI implementation will vary depending on the tools you implement, the approach you take, and the strategy, or lack thereof, you have in place.

These issues may be evident at first, or they could be taking time to light fires in the background before you suspect a thing.

Here are a few common complications to give you an idea of what you need to avoid:

  • Lack of training: If your employees don’t know how to use the new AI tools effectively, it’ll lead to mistakes, inefficiency, and extra costs. Staff training is a critical part of any new tool implementation.
  • Resistance to change: People are naturally resistant to change. The sudden introduction of AI tools might meet employee pushback, affecting morale and productivity. You can reassure your staff by letting them know AI is there to help them, not to push them out.
  • Decreased Customer Satisfaction: Poor AI implementation in customer service, like those repetitive chatbots confused over user queries, can result in negative experiences, customer frustration, and a muddied business reputation.
  • A mismatch between tools and needs: Of course, you’re excited to bring AI to your business, but make sure each solution is tailored for a specific use case and has a target goal to achieve. By understanding a tool’s services, you’ll ensure that what you’re looking at fully aligns with your company’s needs and processes.
  • Inadequate resources: Quite often, proper implementation of AI requires software and hardware to get it doing what it’s supposed to. Make sure you’ve got everything you need installed and ready to go before you dive in!
  • Lack of a strategic implementation plan: Diving in too quickly without an appropriate plan can lead to potential costly oversights, misspent time, and wasted resources.
  • Neglecting data privacy and security: Proper safeguards are needed when dealing with AI, particularly with tools handling sensitive customer data. An oversight could lead to breaches and regulatory issues.
  • Dependency on vendors: Relying upon a single vendor or tool might leave the company helpless if the service is discontinued or the company goes out of business.
  • Lack of performance monitoring: Without regular checks on the performance of the tools, it is difficult to understand the return on investment or to identify any issues that may arise.

All of that and more, just because you rushed to adopt a load of AI tools without really thinking it through properly and having an AI implementation strategy in place.

Why Businesses are Turning to AI (The Benefits of Proper AI Implementation)

On the flip side, taking your time to plan your AI implementation approach, or how you adopt AI into your company, how you choose the tools you’re using, and what your plan is to get them working to their full potential, you open the door to a whole range of new benefits.

These include, but aren’t limited to;

  • Improved Decision-Making: By using AI to analyze market trends and competitor data, businesses can make strategic decisions to improve their products and services in response to industry changes.
  • Automation of Repetitive Tasks: AI can automate tasks like content personalization, sending out regular emails, or analyzing customer feedback, freeing employees to work on more strategic and higher-value tasks.
  • Enhanced Customer Experience: Businesses can field routine customer questions using AI chatbots, providing efficient 24/7 customer support and freeing human agents to handle more complex issues.
  • High Accuracy & Reduced Errors: AI algorithms can help manage business finances by streamlining processes like invoicing and expense tracking, reducing the likelihood of errors due to manual input.
  • Data-Driven Insights: AI can analyze customer behavior patterns across multiple channels, providing valuable insights to develop targeted marketing strategies and enhance the overall customer experience.
  • Cost Savings: Businesses can leverage AI to optimize their operational processes or supply chain, reducing waste, lowering costs, and improving efficiency.
  • Predictive Capabilities: AI tools can help forecast demand for products or services, enabling businesses to prepare for fluctuations in customer needs and better manage their resources.
  • Enhanced Security: AI can be used to monitor network activity and identify cybersecurity threats, helping to protect sensitive data and reduce the risk of breaches for businesses of any size.
  • Increased Productivity: Businesses can alleviate bottlenecks and improve their teams’ efficiency and productivity by employing AI solutions in project management or workflow optimization.
  • Scalability: AI technologies can quickly adapt to a business’s changing needs and the scope in which they’re working, allowing them to scale their operations up or down, depending on market conditions, without a significant investment in time or money.

Long story short, getting your AI solution implementation right is crucial.

The AI Project Cycle

The AI Project Cycle

Let’s talk about the actual artificial intelligence implementation process itself.

Sure, it can feel a bit daunting, but it’s highly rewarding when done right. To do that, we always start by taking the time to understand the typical AI project cycle.

Of course, the core steps involved in a standard AI project cycle can differ from company to company but typically cover the process from the initial conceptualization to the final integration and evaluation.

Let’s break down the five-stage process into manageable stages to pave the way for more efficient and successful AI implementation.

Stage 1: Objective Definition

Embark on your AI journey with a clear understanding of your objectives, ensuring that they align with the challenges your small startup or mid-size business faces.

Are you seeking to streamline your marketing efforts by adopting AI-driven automation tools like Zapier?

Do you want to enhance customer service with AI chatbots like Drift or Intercom? Or maybe you aim to automate repetitive administrative tasks using AI-powered project management platforms like Asana or Notion?

By explicitly identifying your goals and tying them to practical, AI-powered solutions, you establish a focused, goal-oriented roadmap for your AI implementation process.

Stage 2: Selecting the Appropriate AI Tools

Selecting the right AI tools to implement requires careful consideration and should go beyond just looking for open-source tools or pre-existing solutions.

Focus on aligning the AI tools with your specifically outlined objectives. Consider your budget constraints and how easily the chosen solution can be adopted into your current operations.

Will it require substantial staff training, or is it user-friendly and intuitive? Consider the implementation period and assess if it will cause a significant disruption to your workflow.

Additionally, consider the solution’s compatibility with your existing systems and whether it will be able to grow and evolve along with your business.

Picking an AI tool is more than going for the most popular one – it’s about finding the best fit for your business’ unique context and needs.

Stage 3: Choosing the Tool and Setting Up Infrastructure

Setting up the necessary infrastructure is the next step once you’ve decided on the appropriate AI tool for your business. This phase represents the physical implementation of the AI tool within your business operations.

First, you’ll need to ensure that your existing systems are compatible with the chosen AI solution. This could involve some software or hardware upgrades. Depending on the complexity of the AI tool, you may need technical support from an IT specialist.

Next, you have to install or enable the chosen AI tool. This process varies based on the tool. It could be as simple as creating an account and setting a password or more intricate tasks requiring API integration with your existing systems.

Then, configure the AI tool accordingly to match your business objectives outlined in Stage 1. This step might involve tweaking settings, importing data, or setting up specific functions catered to your needs.

At this stage, you should conduct thorough testing to catch any problems or glitches before officially deploying the tool. This testing could be as simple as running a few tasks through the tool to verify its accuracy or as complex as simulating to mimic real-world conditions.

Finally, have a contingency plan. Have a backup tool or plan if a tool fails or doesn’t meet expectations. Preparing for all scenarios will ensure a smoother transition when incorporating AI into your business.

The key to this stage is meticulous planning, rigorous testing, comprehensive preparation, and proactive problem-solving. With these steps adequately managed, businesses can ensure a flawless setup and integration of their chosen AI tool into the infrastructure.

Stage 4: Employee Training and Familiarization

Employee training and familiarization is the most critical component of AI implementation. Successfully adopting any AI tool depends on how well your team understands and utilizes it.

Begin by providing comprehensive training on the chosen AI tool to your staff. Ensure they understand the technical aspects, purpose, use-case, and implications of incorporating this technology into their daily work.

You may even want to organize workshops and interactive sessions to familiarize your team with the tool’s functionalities. Hands-on training can go a long way in reinforcing the technology’s practical implications.

You could invite experts to deliver seminars on best practices, proper utilization, and troubleshooting. Many AI tools offer onboarding sessions and webinars in which you may want leaders, individual employees, and your teams to participate.

Hiring AI-savvy personnel or collaborating with consultants to guide your team through this process can also pay. These specialists can assist in identifying potential roadblocks while offering valuable industry insights.

As with all big business chances, it’s important to consistently monitor employee progress and address their queries and concerns throughout the training phase. Ensure your team feels confident and supported while navigating the new AI tool, which directly translates to their ability to use it effectively.

Investing time and resources in employee training and familiarization is imperative for long-term success.

By doing so, you’re empowering your team to tap into the full potential of the AI tool and avoid any pitfalls or misuse, ultimately reaping the maximum benefits that AI offers.

Stage 5: Monitor and Continuous Improvement

The final stage in implementing AI focuses on monitoring the tool’s performance and continuously seeking improvements. It’s key to ensure the technology continues to deliver on its promises and aligns with the evolving needs of your business.

Start by implementing a robust system to monitor the performance of the AI tool continuously. This system may involve regular efficiency, accuracy, and user satisfaction checks. Adjustments might be necessary as you gain more insights about the tool’s performance in real-time, and these changes should be made promptly to avoid any disruption.

Embrace an iterative improvement process as well.

This approach ensures your business remains dynamic and adaptive. It could involve repeating certain stages, like further staff training to improve tool utilization or developing new strategies to leverage AI capabilities. Sometimes, it might even mean switching to a different tool if the current one doesn’t meet your expectations or business requirements.

Remember, no AI implementation is a ‘set and forget’ operation. It’s a continuous integration, observation, refinement, and evolution cycle. Adopting AI within small to mid-size businesses relies heavily on this cyclical, proactive approach.

By committing to this process, businesses successfully integrate AI solutions and ensure that they continually derive maximum benefits from adopting AI tools.

Steps to Implement AI in a Project

As more businesses appreciate the significance of AI in their operational framework, it is crucial to understand the steps required to successfully implement AI in your projects. But how exactly do you embed AI in your project workflow? Here’s a simple guide:

1. Set Clear Objectives and Goals

The first step towards embracing AI is defining your goals. What problems are you seeking solutions for? In which business processes do you wish to integrate AI? Understanding these objectives provides a clear roadmap for AI implementation.

Setting SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals is essential to guide the project direction and evaluate its success.

2. Assessing Organizational Readiness

Having a goal is a great start, but assessing if your organization is AI-ready is equally important.

This involves evaluating your technical infrastructure, the preparedness of your workforce, the availability of quality data, and the willingness of your leadership to support this technology transformation.

Making necessary adjustments at this stage paves the way for smoother AI integration.

3. Identifying the Right AI Tools and Technologies

To harness the power of AI, you must understand the array of AI technologies and tools available. Different AI tools are designed for different tasks – machine learning is excellent for predictive analysis, natural language processing helps in understanding and utilizing human language, whereas computer vision allows your machines to interpret visual data.

And don’t forget, it’s crucial to select AI tools that match your business needs and project goals.

4. Building a Multidisciplinary Team

Implementing AI is not just a technological shift; it also involves an organizational culture shift.

Building a diverse, multidisciplinary team that combines the expertise of data scientists, ML engineers, business analysts, project managers, and other relevant stakeholders can foster innovative solutions and ensure the successful implementation of AI.

The team should also invest in training and professional development to keep up-to-date with the latest AI trends.

As you can imagine, implementing AI in a project is not a one-time task but a long-term investment.

It requires a strategic approach, considering factors such as clear goal-setting, readiness assessment, appropriate AI tools, and assembling the right team.

These steps can set your business on the right AI path, unlocking incredible productivity, innovation, and growth opportunities.

Overcoming Challenges in AI Implementation

AI Implementation Challenges

AI holds enormous potential to revolutionize business operations.

But as with any new technology, it can present a unique set of challenges during implementation. Understanding these difficulties and how to overcome them can accelerate your path to a successful AI transformation.

Why Can AI Be Difficult to Implement?

For businesses unfamiliar with AI, the complexity of the technology can be daunting.

The lack of an in-depth understanding of the concepts, a shortage of skilled talent, and the need for vast, quality data sets can further complicate the process. Costs and computing power required for AI projects might also challenge smaller companies.

Why Do AI Implementations Fail?

While AI holds great potential, implementing it effectively can be complex. There are a few common reasons why AI implementations often fail:

  • Data Quality: AI is only as good as the data it feeds on. Poor quality data, insufficient volume, or lack of diversity can lead to inaccurate predictions and outputs. Furthermore, training AI with biased data can exacerbate existing issues rather than resolve them.
  • Lack of Expertise: Implementing AI requires various skills, from data science to business acumen. A lack of expertise in these areas can lead to poorly designed models, misconstrued insights, or unsuitable applications.
  • Misalignment with Business Goals: AI initiatives must align with the larger business strategy. Implementations can fail when designed in isolation or pitched as a catch-all solution without considering the specific business needs.
  • Underestimating Implementation Difficulty: AI implementation is not a plug-and-play process. It requires consistent monitoring, optimization, and iterative learning. Underestimating this complexity leads to unrealistic expectations and, ultimately, perceived failure.

Understanding these hurdles can inform smarter AI initiatives – ones that are realistic, well-designed, and bound to deliver value, ensuring your AI implementation becomes a success story.

Common Pitfalls to Avoid

It’s easy to get caught up in the AI hype and fail to address vital aspects like aligning AI projects with business goals, considering end-user experiences, and maintaining data integrity.

Sounds trivial?

Yet, such factors can distinguish between a successful and a botched AI implementation.

Ethical and Bias Issues

AI systems learn from the data they are trained on. If the training data include inherent biases, AI may replicate these biases, leading to faulty assumptions and unjust practices. Ethical issues can also arise, especially when dealing with data privacy and human interaction with AI.

Addressing these challenges requires understanding and mitigating the sources of bias and strictly following ethical guidelines during AI development and deployment.

Addressing Technical, Data Privacy, and Security Issues

Privacy and security of data are of utmost priority in AI implementations. Adhering to privacy laws, encrypting sensitive data, and ensuring a secure infrastructure are essential in building trust in AI systems.

On the technical front, leveraging robust and scalable platforms, employing good data management practices, and having a competent team of data scientists and AI engineers can help mitigate the risks of runtime errors, system crashes, or slow AI model performances.

By anticipating and addressing these challenges, businesses can confidently embrace AI, unlocking its true potential in shaping their future.

AI Implementation: Time and Cost Considerations

A critical factor in any AI project is understanding the time and cost required for successful implementation. This helps set realistic expectations but also assists in managing your budget, resources, and timelines efficiently.

The Time Factor

The duration of an AI project varies based on the complexity of the solution, the quality of the available data, and your team’s expertise.

Simple projects with pre-existing data could be completed in a few weeks, while more complex projects may require months. The key to successful time management is to break the project into smaller tasks, agilely iterating on the models while optimizing and monitoring its performance.

The Cost Factor

Several factors can influence the costs associated with AI projects:

  • Data Collection and Preparation: Acquiring high-quality data and investing in data management and cleansing processes. You may also want test data for setting up your system correctly.
  • Infrastructure: The computing power required for idea generation, setting up a strong data pipeline, validation, AI model training, and ultimately deploying AI models.
  • Human Resources: Hiring skilled professionals, including data scientists, AI data engineers, and project managers.
  • Intellectual Property: Licensing, developing, and maintaining AI frameworks, algorithms, and tools. Data governance is crucial and will only become increasingly important as we move forward.

To minimize costs, you can leverage open-source AI tools, opt for cloud-based systems, and invest in training your existing workforce instead of hiring new talent.

Ultimately, a thorough assessment of time and cost involves striking the right balance between your business objectives, available resources, and the potential return on investment from AI implementation.

The Role of AI Implementation Consultants

As AI revolutionizes industries, organizations turn to AI implementation consultants to unlock their potential and optimize business performance. The role of AI consultants is critical in guiding organizations towards successfully adopting AI solutions.

  • Expert Guidance: AI consultants possess a wealth of knowledge and experience, helping organizations select the most suitable AI technologies and methodologies. By leveraging their expertise, organizations can mitigate risks associated with AI adoption and maximize return on investment.
  • Bridging Skill Gaps: Successful AI implementation requires a diverse skill set, including data science, project management, and domain-specific knowledge. Consultants bridge these gaps, augmenting internal teams with their skills and experience to ensure seamless execution.
  • Aligning AI with Business Goals: AI consultants help organizations identify business use cases suitable for AI intervention and strategize its implementation to support overarching business objectives. They also assist in setting feasible goals and KPIs to measure the success of AI initiatives, ensuring alignment with the desired outcomes.
  • Change Management: Integrating AI within an organization necessitates a shift in culture and processes. AI consultants facilitate change management by educating the workforce on the benefits and implications of AI, fostering a growth mindset, and addressing concerns to dispel resistance to change.

By leveraging the insights of AI consultants like our team here at Orpical, your organization can navigate the complex world of AI implementation with ease, ensuring that their AI initiatives lead to substantial and sustainable impact.

What are the Future Trends in AI Implementation?

While we have AI implementation on our minds, it’s worth taking a moment to see what the current trends in the AI implementation world are. How are other companies and businesses implementing AI? What sort of approaches are they working on? What sort of opportunities are out there for you to pay attention to?

Here are some key trends that are shaping the way we implement and interact with AI systems this year:

  1. Democratization of AI Tools: We’re seeing a surge in user-friendly AI tools that don’t require extensive coding knowledge. This “low-code/no-code AI” trend is set to persist, making advanced AI capabilities more accessible to non-technical users and small businesses.
  2. Expansion of AIaaS (AI-as-a-Service): Like SaaS platforms, AIaaS offers AI processing and services via the cloud, reducing high costs and technical barriers to entry. This is ideal for small businesses leveraging AI without extensive upfront investments.
  3. AI in Cybersecurity: We’re already seeing the impact of generative AI on cybersecurity. Moving forward, AI’s predictive capabilities will increasingly be used to detect and prevent cyber threats, offering an enhanced line of defense for businesses of all sizes. This new level of security will be expected by customers moving forward.
  4. Responsible AI and Ethics: As AI further permeates society, there will be a heightened focus on ethical and fair AI practices, driving the development of regulation-friendly AI systems.
  5. Explainable AI(xAI): Future AI will not just deliver decisions or predictions but also clarify its reasoning process. This transparency will be vital for business stakeholders to trust the AI’s outputs.

These future trends indicate the ongoing efforts to make AI more accessible, usable, and trustworthy, aligning technological advancements with societal needs and business objectives.


The ongoing wave of AI innovation presents an unparalleled opportunity for businesses. Keeping ahead with these trends and integrating AI can offer countless advantages.

Yet, careful planning and professional guidance from experienced partners like Orpical Group can ensure a smooth transition. Don’t make the mistake of just letting your employees and artificial intelligence run wild.

Experimentation and use of ChatGPT and open-source tools for digital transformation are just the tip of the iceberg. Contact us to get a comprehensive assessment and implementation strategy to learn where AI can make the biggest impact on your core business.