Five Key Steps to Developing a Strong AI and Automation Strategy

Today’s enterprises are overwhelmed with data, presenting a significant challenge for IT operations. The only viable solution to manage this data at machine speed and scale is through AI and automation. These technologies promise to empower IT teams to resolve issues faster, build more reliable services, and reduce fatigue and burnout. This potential is why 71% of business and IT leaders are increasing investments in AI and machine learning, and 75% are doing the same for automation, according to PagerDuty research.

However, there are several barriers to overcome.

Deploying these tools is not enough. Enterprises must first implement a clear AI and automation strategy. This strategy is crucial for making a business case for technology adoption, demonstrating clear ROI, setting expectations, establishing goals, and ensuring flexibility throughout the implementation process.

Main Barriers to AI and Automation

Currently, over 35% of enterprises use AI in at least one business function, and 70% are beginning to automate their operations. These figures are expected to grow to 70% and 90% by 2030. However, several roadblocks remain, including:

1. Data Quality: AI and automation projects built on poor-quality data are doomed to fail. Inconsistencies, inaccuracies, and missing information can lead to data biases, hallucinations, and poor decision-making. Technical and business stakeholders must collaborate to create robust data architecture, along with data cleansing and validation processes. Clear data governance policies are also essential to outline ownership, responsibility, and access control for AI and automation projects.
2. Technical Challenges: Legacy systems, technical debt, and inefficient manual workflows hinder AI and automation adoption. Organizations must address gaps in their infrastructure, processes, and data before embarking on these projects.
3. Cultural Change: AI and automation can fundamentally change work processes, leading to concerns about job displacement. Senior leaders must proactively highlight the benefits of these technologies, including opportunities for employees to upskill and alleviate the burden of manual tasks.
4. Security, Privacy, and Compliance: As AI and automation adoption increases, so do concerns about business risks such as exposure of sensitive information, algorithmic biases, and hallucinations. Organizations must monitor the data fed into AI systems and adapt to evolving regulatory landscapes, implementing robust safeguards to protect data from unauthorized access.

Five Steps to Building a Better AI Strategy

Understanding the main barriers is only half the battle. Organizations must define a clear corporate strategy, considering business requirements for AI-driven applications and risks to compliance, trust, and security. Here are five steps to consider:

1. Set Expectations: Address potential concerns about job losses from AI and automation through a comprehensive change management strategy. Communicate the benefits of these technologies to improve employee experience and provide a timeline for initiatives.
2. Offer Educational Opportunities: Provide training and educational initiatives to prepare employees for AI-supported work. Gamification techniques, like “hack weeks,” can encourage an AI and automation-first mindset. Identify technology champions to foster excitement and share knowledge.
3. Focus on Data Management and Governance: Successful AI and automation projects depend on data quality and integrity. Tech leaders should collaborate across the business to ensure data cleansing and validation processes are in place. Consider working with third-party experts on data management and governance.
4. Tackle Infrastructure and Scalability Challenges: Legacy infrastructure can be a significant barrier. Organizations should adopt cloud and distributed computing to build a robust and scalable foundation for new projects. AIOps can automate manual workflows, reduce alert fatigue, and provide intelligence to proactively address service disruptions.
5. Define Real-World Use Cases: Collaborate with business teams to develop real-world use cases that tie AI initiatives to desired business outcomes and KPIs. Monitor and manage these outcomes to ensure participants understand their impact before, during, and after deployment.

Stay Flexible and Adaptable

The future of AI and automation is unpredictable. It is essential to stay adaptable, maintaining an open mind to new technologies while avoiding marketing hype. Define standardized metrics to measure project impact during testing, ensuring the technology produces desired results and allowing quick adjustments if necessary. Carefully plan, remain flexible, and understand the risks and benefits before embarking on an AI or automation program. This journey requires time and thoughtful implementation.