5 Ways to Build a Robust AI and Automation Strategy

Today’s enterprises are drowning in data, and from an IT operations perspective, this is a major challenge. Increasingly, the only way to make sense of this data — while operating at machine speed and scale — is with AI and automation. Such technologies promise to empower ITOps teams to resolve issues faster, build more reliable services and eliminate fatigue and burnout. That’s why 71% of business and IT leaders say they’re expanding AI and machine learning investments, while a further 75% are doing the same for automation, according to research from PagerDuty.

However, there are several barriers to overcome.

It’s not enough simply to deploy these tools. Enterprises first need to implement a clear AI and automation strategy. This allows them to make the business case for technology adoption, demonstrate clear ROI, set expectations, establish goals and ensure flexibility throughout the implementation.

Where Are the Main Barriers?

More than 35% of enterprises are estimated to be using AI across at least one business function today, while 70% are beginning to automate their business operations. These figures are expected to grow to 70% and 90% by 2030, but there are several roadblocks that stand in the way, including:

• Data quality: AI and automation projects that rely on poor-quality data might as well have been built on quicksand. Inconsistencies, inaccuracies, missing information and other issues can lead to data biases, hallucinations and, ultimately, poor decision-making. Technical and business stakeholders must collaborate to create a robust data architecture, alongside data cleansing and data validation processes. Clearly defined data governance policies are also important to outline ownership, responsibility and access control for AI and automation projects.

• Technical challenges: Legacy systems, technical debt and inefficient manual workflows all create barriers to AI and automation adoption. Organizations must address any gaps in their infrastructure, processes and data before embarking on projects.

• Cultural change: Given the potential of AI and automation to fundamentally change the way we work , there are understandable concerns over job displacement. To alleviate these concerns, senior leaders must reach out proactively to emphasize the benefits of the technologies. These include the potential for employees to upskill and augment skills, and to free themselves from the burden of manual toil.

• Security, privacy and compliance: As AI and automation adoption increases, so too do concerns over possible business risks such as exposure of sensitive information, algorithmic biases and hallucinations that provide unreliable information. Organizations need to keep a close eye on the data they’re feeding into AI systems and be prepared to adapt to a fast-evolving regulatory landscape. They should consider putting robust safeguards in place to protect data from unauthorized access and developing responsible deployment practices.

Five Ways to Build a Better AI Strategy

Understanding the main barriers to AI and automation projects is only half the battle. Organizations must define a clear corporate strategy, taking into account business requirements for AI-driven applications and risks to compliance, trust and security.

Once that strategy is defined, consider the following five steps:

1. Set expectations

Workers might express concern about potential job losses resulting from AI and automation. One solution is to proactively tackle these worries head-on with an ongoing change management strategy. This can help communicate the benefits of the technologies to improve the employee experience and provide a timeline for initiatives.

2. Offer educational opportunities

Online or virtual training and other educational initiatives can help prepare employees for a future of AI-supported work. Gamification techniques like “hack weeks” can encourage an AI and automation-first mindset among staff. Consider also identifying champions of the technologies who can help foster excitement and share knowledge.

3. Focus on data management and governance best practices

Successful AI and automation projects fundamentally depend on the quality and integrity of the data they’re built on. To increase corporate confidence in data quality, tech leaders will need to collaborate with their peers across the business. Internal data cleansing and validation processes will help fix inconsistencies and improve accuracy. It’s also worth considering working with a third-party expert on data management and governance.

4. Tackle infrastructure and scalability challenges

Legacy infrastructure is the enemy of AI and automation, often proving a significant barrier to integration efforts. Organizations should look to cloud and distributed computing to build a foundation for new projects that are both robust and scalable enough to handle the demands of emerging technologies. AIOps can also help by automating manual workflows, reducing alert fatigue and delivering intelligence to help address service disruption proactively.

5. Define real-world use cases from the start

Technical leaders must collaborate with business teams to develop real-world use cases that tie AI initiatives to desired business outcomes and key performance indicators (KPIs). These outcomes must be monitored and managed before, during and after deployment to ensure participants fully understand their impact.

Stay Flexible and Carry On

No one knows exactly how AI and automation will evolve over the coming years. That’s why it pays to be adaptable throughout — keeping an open mind to adopting technology without falling for marketing hype. It pays to keep a clear head and evaluate any use cases, technology stacks and relevant KPIs.

It might be useful here to define a standardized metric to measure project impact during testing. It will ensure the technology is producing the desired results and enable tech teams to jump in quickly to change things up if it isn’t. Plan carefully, be flexible and understand the risks and benefits before embarking on an AI or automation program. This isn’t a journey that happens overnight.