According to computer scientist, Alan Perlis – a year spent in Artificial Intelligence is enough to make one believe in God. AI is a super powerful and fast-growing tech that businesses are using to improve how they work. For example, in finance, there’s an AI search engine called Aphasense that looks at keyword searches to analyze market trends and changes. In the same way, Bank of America has a chatbot called Erica that can answer around 500,000 different questions and has already helped 10 million customers.

There are multiple AI technologies available such as machine learning, predictive analytics, and computer vision, so it’s important to start with a strong foundation and clear plan for how you’ll use these technologies to your advantage.

AI Implementation

An experimental approach works best when putting AI into action, therefore it’s essential to start off by forming hypotheses and then testing and measuring the results of those tests. It’s often better to implement AI gradually instead of all in one go since initial ideas can be flawed.

Here are ten steps toward successful AI implementation:

1. Establish data fluency

To engage in practical discussions around AI, business leaders need a basic understanding of how data drives the process. According to a 2020 report by Forrester Research, 90% of data and analytics decision-makers see increased use of data insights as a business priority, while 91% said that applying these insights is challenging. This may be due to a lack of advanced analytics skills required to drive business results. Executive understanding and support are necessary to navigate this maturity process and drive lasting change.

2. Identify top business drivers for AI

Uncover the most valuable use cases for your organization by learning from other industry players. Evaluate the value and feasibility of AI implementation in various business scenarios, and consider who might champion the project. Also, think about external data sources, how to monetize your data, and how to maintain project momentum.

3. Automate business areas that generate significant ROI

Concentrate on areas with high variability and significant potential return on investment. Create teams with business stakeholders who possess the technology and data expertise to assess the metrics and impact of AI implementation on your organization and its employees.

4. Assess internal capabilities

Once use cases have been identified and prioritized, businesses must evaluate how their existing technology and workforce align with these applications. Look for ways to bridge the technical skills gap within your organization, such as through education, training, or acquiring external expertise.

5. Recruit suitable talent for AI implementation

Recruit the most experienced individuals to test AI deployment like invoice matching or IoT-based facial recognition. It’s important to involve as many experts in this process as you have to be experimental.

6. Launch an AI pilot project

To convert a potential AI application into an actual project, a team of AI, data, and business process experts is needed to gather data, develop algorithms, roll out controlled releases, and measure impact and risk. 

7. Develop a baseline understanding

Lessons learned from early AI projects can improve understanding across the whole company, making it easier to see how an actual AI deployment supports or contradicts the initial hypothesis. Maintain human involvement to build trust and engage your business and process experts with your data scientists.

8. Scale up gradually

Incremental wins build confidence across the organization and inspire others to test similar AI applications from a more substantial, more established starting point. Adjust algorithms and business processes for scaled release and embed them into normal business and technical operations.

9. Develop overall AI capabilities

As AI projects expand, teams need to improve the overall lifecycle of AI development, testing, and deployment. The three core strategies for maturing overall project capabilities are building a modern data platform, developing an organizational design, and creating the processes and technology needed to manage critical data elements.

10. Continuously refine AI models and processes

Once the system is in place, identify opportunities for continuous improvement in AI models and processes. It is important for teams to monitor feedback and resistance from employees, customers, and partners, and understand that AI capability can only mature at the rate that data management can mature.

Final Words

As businesses consider utilizing artificial intelligence (AI) solutions, avoiding common mistakes is essential to ensuring success. These include unclear project objectives and KPIs, insufficient data or skills, accelerated implementation with production-ready POCs, adequate data generation mechanisms if needed, a skilled team including domain experts, and the right AI tools. Establishing measurable goals and consulting with an AI expert can help clarify objectives and visualize the end product. Starting projects early also allows for greater flexibility and cost savings in the long run. Having appropriate talent on board and close collaboration between data science teams are key to avoiding potential pitfalls in natural production environments. Finally, utilizing the right frameworks can provide excellent results in various industries.