Machines have been leveraged by humans to increase their productivity at the workplace since ages, when agriculture was the only way to earn a living, e.g., tractors. Over the years, humans have discovered innovative ways to fulfil their needs, requirements, and desires in different aspects of life, and with this, machine roles have also evolved.

In the Digital era, Artificial Intelligence and Data Science can be leveraged by businesses to execute intelligent tasks with similar or better outcomes than humans in some cases. Building such AI-based machines, systems, or applications requires huge upfront investments, but at the same time is complex and requires know-how in different fields: business domain, technology, statistics, programming, etc. for its successful execution.

As a SME with 17+ years of experience in AI and Analytics across top-tier organisations – Accenture, KPMG, and TCS, I would like to mention different aspects that enterprise leadership can consider for successful adoption of AI.

Define Clear goals.

Identify business areas that are critical to the growth of an enterprise and prioritise the one that brings the most value. Clearly define the goals and objectives you want to achieve through AI adoption. Whether it’s improving efficiency, enhancing the customer experience, or driving innovation, having well-defined goals will guide your AI implementation strategy.

Define the AI Roadmap 

Once business goals are defined, look for use cases that can address existing challenges or enhance existing processes. Conduct a thorough analysis to determine the feasibility and potential impact of AI adoption in those areas before making any investments. Once the areas to be acted on are analysed and decided, create a roadmap for systematic execution.

Identify the right business metrics to evaluate performance and ROI:

Establish key performance indicators (KPIs) to measure the performance and impact of AI initiatives. Regularly assess the ROI of AI adoption, considering both quantitative and qualitative factors. Use the insights gained to refine strategies and make informed decisions.

Start Small and Iterate: 

Begin with small-scale AI projects or pilots to test and validate the effectiveness of AI solutions. Learn from the outcomes and make the necessary adjustments before scaling up. Continuous evaluation, monitoring, and refinement are essential for successful AI adoption.

Data Management and Data Engineering:

Establishing data warehouses and data marts that can store consumable data will go a long way in saving time to clean and shape data for final use. AI systems require high-quality and relevant data to learn and make accurate predictions or decisions. Establish processes to collect, clean, and prepare data for AI training. Ensure that data privacy and security measures are in place to protect sensitive information. This is the most critical aspect of AI, as the accuracy of all solutions depends on the quality of the data.

Skilled Workforce and Collaboration:

Build a team with expertise in the business domain, statistics, computational algorithms, and technology. Foster collaboration between IT professionals, data scientists, and business stakeholders to align AI initiatives with business goals. Provide training to upskill existing employees in AI-related skills, as AI is a continually evolving field.

Infrastructure and Resources:

Assess your organisation’s infrastructure and technology capabilities to support AI implementation. Determine if you have the necessary computing power, storage, and network infrastructure to handle AI workloads effectively. Consider cloud-based AI solutions if your current infrastructure is insufficient.

Ethical and Responsible AI:

Consider the ethical implications of AI adoption. Develop guidelines and policies to ensure AI systems are transparent, fair, and unbiased. Regularly monitor and audit AI systems to mitigate risks and ensure compliance with legal and ethical standards.

Change Management and Training:

Prepare your workforce for the changes that AI adoption brings. Educate employees about AI technology, its benefits, and its potential impact on their roles and responsibilities. Provide training programs to enable employees to work alongside AI systems effectively.

Continuous learning:

AI technology is rapidly evolving. Stay informed about the latest advancements, trends, and best practices in AI. Continuously assess the evolving needs of your organisation and adapt your AI strategy accordingly.

Shift180 Business Advisory and Services can assist enterprises in each of the aspect mentioned. We begin our engagements with clients by conducting AI adoption assessments over the course of a month. This would help us know the current state of AI adoption by enterprises.