Embarking on an artificial intelligence (AI) implementation can feel like navigating uncharted waters. From virtual assistants to computer vision to deep learning, the knowledge gaps companies face can be vast.

However, before you become a data scientist, remember that an AI project is fundamentally a business project. The key to success lies in strategic planning and business alignment.

Today, we’re helping you strategically plan for successful artificial intelligence (AI) implementation and adoption by providing expert insights and examples from our  ERP project recovery consultants

Expert Tips for a Successful AI Implementation in Business

Artificial intelligence (AI) is not just a buzzword but a powerful force reshaping industries across the globe. If you want to use AI to boost your competitive advantage, the following implementation tips are essential.

1. Get Input From All Departments

The capabilities of artificial intelligence transcend departmental boundaries. This means you need input from various departments as you evaluate solutions and create an implementation plan. 

Identify key stakeholders and departments that will be directly impacted by the AI initiative. Then, ask about their pain points and how AI technologies might address them. 

For example, the marketing department might be interested in using AI solutions for customer segmentation and personalized campaigns. Meanwhile, the operations team might prioritize using AI applications for predictive maintenance. 

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SAP, Oracle, Microsoft, and Infor each have a variety of systems that can support data-driven decision making. We surveyed customers of these four vendors to find out what their selection and implementation process was like.

2. Define Your Business Goals​

It’s easy to get caught up in the bells and whistles of AI, without considering how those features will help you achieve your business goals.

Before meeting with vendors, take the time to clarify your overarching objectives. In addition to being specific and measurable, each goal should be aligned with the capabilities of AI technologies that are relevant to your industry and business operations. 

Consider a logistics company aiming to enhance customer satisfaction by reducing delivery times. To achieve this, the company wants to implement AI algorithms to streamline delivery routes and anticipate potential delays. This targeted use of AI would directly address the company’s objective to reduce delivery times. 

3. Focus on Data Reliability

Without reliable data, AI tools cannot function effectively, leading to skewed insights and costly decisions. 

Here’s how you can ensure data reliability across your organization:. 

  • Data Cleaning: Implement regular data cleaning routines to eliminate errors, duplicates, and irrelevant entries. This improves data quality and ensures AI models are trained on accurate information.
  • Data Organization: Develop an organization system that categorizes data logically and makes it easily accessible for AI processes. Consider using cloud-based platforms that offer scalability and accessibility across the organization.
  • Integration: Ensure seamless data integration from disparate sources to provide a holistic view of operations. Integrated data systems reduce silos and enable advanced analytics.

Imagine a field service company embarking on an AI initiative to enhance its diagnostic capabilities. This involves the development of advanced image recognition models that can identify equipment issues from images captured on mobile devices. 

To improve the accuracy of this image recognition, the company undertakes rigorous data cleaning. This ensures that the learning models are trained on high-quality data, reducing the likelihood of misdiagnoses. In turn, technicians can use real-time insights to resolve equipment problems more swiftly and effectively.

4. Set the Right Budget and Timeline

Many organizations underestimate the resources required for data preparation, model training, and integration.

In addition, AI projects often require adjustments and ongoing investment, including model retraining, data re-evaluation, and system updates. Acknowledging this can help you set more realistic expectations for budgeting and timelines.

Consider a company in the financial sector using AI for fraud detection. Developing a robust model requires investment in data security measures, algorithm training, and continuous model updates. The company accounted for these factors during their initial planning, so they have no problem moving forward with these ongoing needs.

(Learn about AI in accounting.)

5. Integrate Other Business Systems

Maximizing AI’s benefits often means integrating it with business systems, such as ERP and CRM software. This integration can unlock advanced analytics, automate complex workflows, and enhance decision-making.

For example, a pharmaceutical manufacturing company might integrate AI with its supply chain management system to more accurately forecast demand for various medications. The system takes into account factors such as seasonal trends, global health indicators, historical sales data, and more. This precise demand forecasting enables better planning of production schedules and more efficient allocation of raw materials.

(Learn about using AI in manufacturing.)

6. Prioritize Ethical AI Use

As AI systems become more embedded in business processes, ethical considerations must be at the forefront of implementation strategies. This means ensuring transparency, fairness, and accountability to build trust among users and stakeholders.

For instance, a financial institution using AI for credit scoring should ensure its algorithms do not inadvertently discriminate against certain groups of people, adhering to ethical standards and regulatory requirements.

7. Foster a Culture of Continuous Learning

AI is an evolving field. Encouraging a culture of continuous learning among employees can keep your organization at the forefront of AI advancements.

Consider an agricultural company committed to enhancing crop yields and sustainable farming practices. The company might adopt AI for precision agriculture, while at the same time, initiating a learning program that offers employees online courses and interactive webinars on integrating AI into farming. 

Implementing AI into Your Business with Strategic Acumen

A successful AI implementation process can equip you with the tools to enhance customer experiences and make data-driven decisions. However, the path to AI success is laden with complexities that demand a strategic approach. 

Organizations should ensure their AI implementation strategy and initiatives are not only technologically sound but closely aligned with their business objectives.

Contact our enterprise software consulting team below to learn more about AI implementation strategy and where this process could take your business.

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