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Artificial Intelligence has firmly entered our lives. It helps us with our small daily tasks like finding the best route or choosing a movie for the evening; and globally changing our reality as in the case of leveraging AI for clinical research.
At the same time, many small and medium businesses are still looking at the possibility of applying AI within their organization. In this article, we’ll explore how to get started with your own in-house AI approach, and the 10 steps you need to take on your way to deploying your first AI project.
What AI Is and What It Is Not
If you are at the very beginning of the journey and Artificial Intelligence is more a popular term for you than an understandable technology – start from step zero – get a general idea of how AI works and how it can be applied in your business. This will save you from unreasonable expectations and help clearly formulate your goals and objectives with AI.
Study who among your competitors is already successfully using AI and what opportunities the technology gives them – this may be a good lesson on what to do or what not to do.
How AI can affect your business
For each industry, the applications of AI will differ depending on the tasks, limitations, and opportunities. But there are general benefits that we can highlight for any business.
- Improve employees productivity by automating routine tasks and freeing time for more complex tasks.
- Save time and money by replacing manual systems with automated software.
- Reduce risk of human errors in complex mathematical calculations and analysis.
- Improve your marketing activities by predicting customer expectations and providing a highly personalized experience.
- Improve strategic planning and achieve better business results by providing your executive team with valuable insights and forecasts based not on a guesswork but on the calculation of real data.
Feeling inspired?So are we! But don’t forget that implementation of AI into any business can be a challenging task. Here are a few complexities to keep in mind.
Common Challenges with AI Implementation
Implementing AI within your business will require the deployment of expensive, high-performance hardware and software. To stay within a limited budget you have to think through every step of the process. If you don’t have enough experience it will be better to invite the external experts at the start. This will cost upfront, but will definitely help you avoid much higher costs in potential missteps down the road.
Business AI systems need to process huge amounts of data to get results. Therefore in order to have correct and applicable results, you need to have accrued that data and more than that – be confident in its quality.
Organizations implementing AI need to understand the inner workings of AI-based solutions or technologies in order to be prepared for the results. Continuous usage of AI or Machine Learning models requires a skilled workforce that can be challenging for businesses to implement and manage without prior experience.
While the adoption of AI can be challenging, the benefits it provides are well worth the effort for most businesses. Let’s take a look at the ten notable steps you’ll need on the path to successfully introducing AI technologies in your company.
How to start AI implementation in your business
1. Define your goals with AI
It may sound obvious, but setting clear goals at the start is critical to the success of your project. Consider what problems or areas for improvement exist in your organization that could benefit from AI. How can AI capabilities complement your existing products and services? Describe specific use cases where AI can solve business problems and deliver provable value.
2. Prioritize and start small
In all likelihood you’ll come up with more than one idea which means you will need to prioritize. When we talk about AI, experts recommend not trying to stir up the whole ocean at once, but start the implementation in stages, achieving small victories at first.
Get proofs of concepts and gradually implement the technology in various business processes across your organization.
To help prioritize, you can use a 2x 2 matrix. Place indicators of the potential profit and feasibility of various ideas on the frame. This will allow you to get the short-term outlook and quantify the company’s value over time with AI.
4. Plan Thoughtfully
Expectations for results
As you move towards implementing the detailed plan, go back to your initial expectations. Make sure they are realistic and achievable. Keep in mind that your AI model will need time to learn and improve. All stakeholders should be prepared to accept a range of results with 60%-99% accuracy while the model is training.
The budget is one of the tricky questions in planning an AI project. Try to be as careful as possible when allocating your finances and take into account all possible expenses.
- Include internal headcount, contract resources, and IT infrastructure (including licenses for applications and cloud resources) to calculate your total budget
- Review the cost of data collection (from internal or external sources) as part of the overall budget
- Calculate the predicted value for your business from an AI project over a period of 12 to 36 months
On average, AI projects can take from 3 to 36 months, depending on the scope and complexity of the use case. Decision makers often underestimate the time it takes to prepare data before an AI algorithm can be built.
For some use cases, multiple iterations may be required to reach the levels of accuracy needed to deploy AI models in production. Error analysis, user feedback incorporation, continuous learning and training should be integral parts of your AI model lifecycle management.
5. Define Internal Capability Gaps
Now that you have clear goals and an understanding of the project, let’s look at the current status of your company.
Before embarking on a full-scale implementation of AI, you must clearly understand what you are capable of from a technical and business processes point of view. You need to identify existing gaps and understand what you will need to acquire and internally develop in order to achieve the desired results with AI.
6. Prepare Your Data
Data is fuel for AI systems. Preparing data for training AI takes up most of the time in any AI solution development, Up to 80% from the beginning of the project to production.
Find out if you have all the necessary data and how it’s accessible. Depending on the size and scope of your project, you may need to access multiple data sources at the same time. Additionally, you may need to purchase or enable external data sources. Keep in mind that different data sources and data types will have different data governance and privacy controls, you’ll need to meet.
Be sure to include storage in your plan. As your AI initiatives evolve, you will need to store more and more data. Incorporating a fast, optimized data warehouse into your roadmap in the early stages will help you avoid the hassle of scaling.
7. Research your Domain Expertise and Required Skill Set
Bringing AI into business processes requires varied roles such as data engineers, data scientists, and machine learning engineers. Review your current team’s skill sets and determine your HR strategy.
You may need to hire new specialists, repurpose existing resources, or upgrade and train current staff. You may want to involve external consultants or contractors, especially in the beginning as earlier suggested
For example, you can bring in a third-party IT services partner to provide the required IT skills for data modeling and software implementation.
8. Prepare IT-Infrastructure
Determine who will develop and implement the technological solution in your project – will you go for in-house development or search for outsourcing partners?
For internal development, you must ensure that your IT team has the necessary skills to roll out an AI project. Though AI projects are still IT projects, they require specific knowledge and experience from the development team. If your internal resources are insufficient, consider inviting external experts to complement an existing team, or take over the entire project.
Check what off-the-shelf solutions are already available on the market. Perhaps there is a product solving your problems that you can implement in your business processes. In this case, you need to determine with what resources the solution will be deployed and whether there is a need for refinement and customization to suit your needs.
9. Implement and Keep Training
No AI model will provide perfect solutions and predictions on the first day of deployment. AI models need to be retrained with feedback for corrections and improvements. Therefore, it is important that your AI solution provides mechanisms for this feedback from domain experts. Your vendors should give you the ability to fully manage the lifecycle of the AI model so that it can take feedback, learn from it, and analyze errors.
10. Embed AI in Your Business Culture
AI systems help optimize, automate or support decision making in a company. Therefore, the introduction of AI into business processes undoubtedly affects the way people do their work. Your employees may gain free time to tackle new challenges, or you may discover new business opportunities that require expansion in other directions. Perhaps automating certain tasks will require fewer workers to complete them.
Analyze the expected results and their impact on your business. What are your next steps once the AI solution is successfully adopted? How do you use the freed up resources or apply knowledge about new opportunities? In order to get the most out of AI, you must be prepared to make adjustments to the business culture and workflow of your employees.
Implementing the first AI project in your organization can be a complex and challenging task. But the further you go the more familiar you’ll become with the technology, the more value you’ll get for your business, the more you’ll be convinced of the amazing possibilities that AI has for business development and creating competitive advantages. We still often think of AI as something from the future, but in fact – AI is today. It is already everywhere. Don’t let the Fourth Industrial Revolution pass you by…
Our team of highly qualified engineers has extensive experience in working with artificial intelligence projects. If you want to go through these ten steps with confidence, we can become a reliable trusted partner along the way. Start a conversation and get advice from our AI expert today to build a new future for your business tomorrow!