To remain competitive, most companies will need to eventually implement artificial intelligence. However, for many business leaders, doing so can be daunting.
We sat down with Andrew McKishnie, a Senior NLP Engineer at Multimodal, to ask for his insights and best approach to artificial intelligence implementation. We will also address common challenges and misconceptions to help you navigate the AI implementation confidently.
This guide, based on Multimodal's experience, industry reports, and insights from Andrew, provides step-by-step instructions on how to incorporate AI into your business.
Before You Start: Understand the Capabilities and Limitations of Artificial Intelligence
Understanding artificial intelligence is the first step toward leveraging this technology for your company’s growth and success. So, before developing your AI implementation strategy, make sure to familiarize yourself with AI’s capabilities and limitations.
Andrew told us that one of the biggest issues is that some leaders overestimate, while others underestimate AI:
“On the one side, you have clients that think AI is like magic and already knows how to do everything you want it to do. On the other side, people underestimate AI simply because they don’t understand how it works.”
Businesses that overestimate AI typically underestimate the effort that goes into building it, particularly when it needs to be tailored to them. They often don't realize the importance of their involvement in the process.
Demos, on the other hand, are very effective for those skeptical about AI's capabilities. “We show them hard evidence that AI actually works,” says Andrew, which helps dispel doubts and build confidence.
Purpose-Built AI vs. Off-the-Shelf Solutions
Off-the-shelf AI solutions can help you kickstart automation. They are generally less expensive and quicker to implement than purpose-built solutions, but may not align with your specific needs. They also come with significant limitations:
- Integration issues: They often can’t integrate with existing software stacks, leading to disparate data silos, more room for human error, and reduced overall efficiency.
- Lack of adaptability: They struggle to adapt to changes like new regulatory requirements, limiting scalability.
- User friction: They create friction for workers who must adapt to new processes, interfaces, or apps, often leading to failed tech adoption.
- Limited accuracy: Most importantly, they aren’t tailored to specific business needs, struggle with accuracy, and often force teams to change their processes.
Fine-tuning fixes these issues by:
- Adapting AI to existing workflows rather than forcing teams to adapt to it.
- Enabling seamless integration and data flow.
- Allowing AI to easily adapt to new business needs, making scaling easier.
- Equipping AI with company- and domain-specific knowledge, leading to more accurate results and better outcomes.
By investing slightly more in tailored solutions in the early stages, companies can save drastically in the long run.
Purpose-built AI beats ready-made solutions in every area, including long-term cost efficiency.
AI Readiness
Once you've identified AI's potential benefits for your business, you must evaluate your readiness for it. Assess the necessary tools and resources to execute your AI strategy effectively and determine how you will measure success.
For more information, consider the most important questions you need to answer before implementing AI in your business.
Be S.M.A.R.T.
In the same way you apply the SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) framework to project or employee performance management, you should use it to set your AI implementation goals.
When considering AI integration, businesses must define the specific outcomes they aim to achieve. Identifying the main obstacles that could hinder these outcomes is essential for planning effective solutions.
Understanding how AI can drive your business toward success involves evaluating its potential to enhance efficiency, improve decision-making, and create new growth opportunities.
Managing Upfront Costs
Additionally, businesses must decide whether to build in-house, buy off-the-shelf, collaborate with an AI partner, or outsource entirely, weighing the costs, benefits, and resources required for each approach.
These upfront costs can be a barrier for many businesses. However, these investments will save you money in the long run.
“You may spend more in the next 3 months, but you’ll save so much more over the next 3 years.”
Common Challenges
The primary challenge is securing buy-in from all stakeholders. This problem is typically threefold:
- There's a general distrust of AI, particularly concerning complex use cases involving automated decision-making.
- There are often significant upfront costs that many businesses are unprepared for or unwilling to pay.
- There's still a lack of education about AI, especially among employees. This often leads to improper use of AI tools and poor results.
Andrew recommends beginning with simple tasks such as data entry or extraction to tackle these issues. These quick wins help establish trust and demonstrate AI's value.
Conversely, automating multifaceted processes, like loan application assessments, can be more challenging. There is often skepticism regarding AI's ability to handle high-stakes decisions, and achieving a satisfactory ROI may take longer in these cases.
Another common challenge is poor data quality, which can hinder AI projects. That is why you may want to work with a partner who will help you ensure your data is adequate and improve it as necessary. More on that later.
Step-by-Step Guide to AI Implementation
There are several steps each enterprise needs to take for a successful AI implementation:
1. Identify a Specific Problem to Solve
Andrew emphasizes, "Definitely start with a problem you want to solve. To get ROI, you need to identify a real problem that needs solving."
This first step is crucial. Begin by analyzing your business processes to pinpoint areas where AI can add the most value. Common starting points include automating repetitive tasks, enhancing customer service, or optimizing supply chain management.
That being said, your first AI project doesn’t need to address ALL your business problems immediately. There's often a gap between what you want to develop and what you can achieve within a given timeframe. But you definitely need to plan for scalability later on.
2. Choose the Right AI Technology for Your Business
AI encompasses various technologies, from machine learning (ML) and natural language processing (NLP) to computer vision (CV). Select the technology that best addresses your identified problem.
For example, if you want to automate customer service, consider AI-powered chatbots that leverage natural language processing.
Also, using cloud services such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud is beneficial for efficiently training, deploying, and managing AI models. These platforms provide secure and scalable data storage solutions essential for handling large datasets.
Leveraging cloud resources allows businesses to harness AI capabilities without investing in extensive on-premises infrastructure, facilitating smoother integration and scalability of AI initiatives.
3. Assess Your Data
The success of AI implementation is only as good as its data, so ensure you have high-quality, relevant data to feed into your AI systems.
Andrew notes, “Another problem we sometimes encounter is low-quality data. That’s why we work very closely with clients in the initial stages of the project. We tell them exactly what we need and easily recognize if their data is not great. Then we work with them to improve it or find better data.”
Work with your team to gather and clean data. This might involve setting up new high-quality data collection processes or improving existing ones. We can help you with your data preparation.
4. Integrate AI with Existing Systems
“The easiest way to implement AI is usually to integrate it with your existing systems.”
What to automate first?
“Start by automating something that takes a lot of time but doesn’t take a lot of mental effort. Menial tasks, like data entry or data extraction, are a great place to start,” Andrew suggests. These quick successes can help build trust and showcase the value of AI.
Deployment
AI can be deployed on your own or a partner’s infrastructure, functioning like a SaaS solution. “Multimodal offers clients both options,” says Andrew. Choose the deployment strategy that aligns with your business goals and technical capabilities.
Implementation
Be mindful that integrating AI when you already have established processes and software stacks can be challenging. It may require you to:
- Reconfigure your existing systems
- Train your staff on new technologies
- Invest in specialized hardware or software
Here are two ways to avoid these issues:
1) Opt for Easy-to-Integrate Solutions
Flexible API solutions offer versatility, allowing you to leverage AI capabilities in multiple ways, such as accessing them via web apps, using them directly, or integrating them with your existing applications.
2) Tailor AI to Your Existing Processes
Although upfront costs may be higher, purpose-built AI quickly pays off by saving time and money otherwise spent on restructuring workflows, retraining employees, and managing inefficiencies.
5. Tailor and Test the Model
“[Multimodal] do[es]n’t just customize the model, but also how you’re going to use it,” Andrew explains. Tailor the AI model to fit your specific needs and test it thoroughly.
As mentioned earlier, off-the-shelf models have many limitations, and fine-tuning the AI model to be company-configured makes all the difference.
You can also conduct pilot projects to evaluate the AI solution's performance and make necessary adjustments. This phase is helpful in ensuring that the AI solution meets your business requirements.
6. Train Your Team
The worst scenario is when a team member joins the project late and questions why the solution lacks certain features. Andrew emphasized that the most important thing is getting everyone to understand the purpose of the AI you’re building.
“Everyone on the team should be aware of the purposes of the solution you’re building and the limitations that come with that.”
Project leaders need to thoroughly understand the specific requirements of their users, whether they are customers or employees. They must plan accordingly and in advance to ensure the AI project effectively meets all of their needs.
Lastly, educate and upskill your employees to use AI effectively. Provide training sessions and resources to help your team understand and utilize the new AI tools. This step ensures that your workforce can leverage AI to its full potential.
The Bottom Line
Implementing AI in your business is a multifaceted process that requires careful planning, integration, and training. By following these steps and leveraging expert insights, you can navigate the complexities of AI implementation and unlock significant benefits for your organization.
Remember, starting small with well-defined problems and scaling up as you gain experience can make the transition smoother and more successful.
Partnering with experienced AI vendors like us can further streamline this journey, ensuring you achieve the desired outcomes smoothly and effectively. Schedule a free 30-minute call with our team to see our AI Agents in action and discuss tailored strategies for your business.