As per a recent study by Digitate, 90 % of IT leaders are planning to incorporate more technology and AI in the upcoming year, signaling a rising trend in adoption among businesses. However, companies must find a balance between the necessity to manage these advancements and concerns regarding their impact on the workforce.
The report “AI and Automation: Laying the Foundation for the Autonomous Enterprise” surveyed 601 IT decision-makers about their technology strategies. It is related to ITPro Tomorrow’s 2023 IT Priorities Survey Report.
Key findings from the research include:
- Increased Focus on Automation: 67 % of respondents intend to enhance IT automation in the next 12 months, with 26 % aiming for fully autonomous operations within five years.
- Automation Driven by IT Complexity: 44 % of participants cited multicloud as their primary challenge. Additionally, 90 % of businesses prioritize automation in IT operations.
- Exploration of Generative AI: Beyond IT, 74 % are exploring generative AI, while 89 % plan to automate customer service and financial processes.
- Workforce Concerns: 26 % of respondents view job redundancy as a significant threat. However, 60 % believe that technology has boosted worker satisfaction and productivity.
Avi Bhagtani, Digitate’s Chief Marketing Officer, highlighted the growing desire to control output, stating that 26 % of IT leaders aim to introduce machine-operated tasks or transition to fully autonomous systems within five years.
The narrative emphasizes that technology aims to assist humans rather than replace them. While automation and AI may raise concerns about job displacement, the study suggests otherwise.
Reskilling employees, implementing automation gradually, and creating new job opportunities are proposed strategies to enhance productivity, save costs, and address job displacement concerns. Bhagtani envisions a future where routine tasks are automated, but human involvement remains essential.
The study also delves into various technologies being considered by companies. It reveals that 74 % have experimented with relationalAI, while workflow automation (68 %) and AIOps (65 %) are also prevalent. Bhagtani underscores the distinction between experimentation and widespread deployment of these technologies.
In addressing automation-related challenges, Bhagtani suggests prioritizing small, impactful tasks to streamline operations. Mapping existing processes, identifying repetitive tasks, and selecting suitable automation technologies are recommended steps. Establishing a robust data foundation, addressing ethical considerations, and planning workforce training are crucial for successful implementation and adoption.
Bhagtani advocates for continuous monitoring of automation progress, adjusting strategies as needed to maximize benefits and overcome obstacles along the automation journey.
