AI enterprise future

In 2023, enterprises invested $19 billion into their GenAI efforts. That number is expected to double this year and reach $151 billion by 2027.

This surge in investment reflects the broader business trend of accelerating GenAI initiatives designed to help boost business transformation and increase revenue and business efficiencies. With such rapid adoption, enterprises had better prepare for the resulting significant organizational change and even angst among staff. Perhaps one strategy is to use GenAI itself to help improve the management of GenAI deployments.

As Katy Crighton, senior director of human resources at Harte Hanks, explains, success often comes down to the staff’s enthusiasm to adopt the technology and the degree to which technology is integrated into their workflow. “As with any outcome reliant on people, there are complex reasons why an initiative may or may not be successful,” explains Crighton.

GenAI Can Spot Trends Humans May Miss

Crighton adds that GenAI’s knack for scouring and analyzing large amounts of data enables organizations to spot non-obvious patterns of why GenAI may or may not be successfully adopted.

When an organization deploys any new technology, Crighton says GenAI can be used to analyze multiple operational performance indicators, such as how fast tasks are completed, how accurately they are finished, and customer and user satisfaction scores. These scores can then be compared to existing change management tactics, such as how and when a change was communicated to users, how, when, and to whom the training for the new technology took place, and the level of leadership support for the change.” It [GenAI] can pinpoint trends and patterns a person may not have been looking for. For instance, employees who had the change communicated two weeks before receiving in-person training are seeing the highest increase in performance efficiency and customer satisfaction,” Crighton explains.

Daniel Fallmann, CEO of insight engine firm Mindbreeze, agrees that GenAI can enhance the analysis of change initiatives and capture lessons learned through data analytics, natural language processing (NLP), and predictive modeling.” Data analytics processes vast amounts of project data to identify patterns and trends. At the same time, NLP extracts insights from qualitative sources like feedback and reviews—predictive analytics forecasts outcomes based on historical data, aiding proactive decision-making,” Fallmann said.

Fallmann adds that real-time monitoring tracks progress and prompts adjustments, while recommendation systems suggest that best practices and knowledge graphs can map relationships between project elements and reveal insights.” Automated reporting streamlines information dissemination, and collaborative filtering fosters knowledge sharing among teams. By leveraging AI in these ways, organizations can gain valuable insights, improve future initiatives, and facilitate continuous learning and adaptation,” Fallmann says.

Can AI Help Better Predict the Potential Outcomes of New Initiatives

As Fallmann continues, AI can also play an essential role in predicting the outcomes of new business initiatives through data analysis, predictive modeling, risk assessment, market analysis and scenario planning. “Through extensive data analysis, AI algorithms can sift through vast amounts of historical and real-time data to uncover patterns and trends relevant to the new initiative, providing valuable insights into market conditions and customer behavior,” he says.

“By harnessing AI for predicting outcomes, organizations can reduce uncertainty, optimize resource allocation and increase the likelihood of success in their new business endeavors,” Fallmann says.

Finally, other areas where AI can play a role are traditional change management practices and tools such as change readiness assessments and stakeholder analyses to assess an entire organization’s or a department’s preparedness for change. “AI can significantly improve the ability to predict potential outcomes of new initiatives through AI-driven assessments for the soft skills associated with change acceptance, such as adaptability, openness to change, flexibility, resilience and more,” Crighton says.

“Given the large amount of data to process and the complexity of predictive behavioral analysis, AI can be effectively deployed to highlight areas of opportunity and challenges that may require mitigation plans. Lastly, predictive behavioral analysis can provide a real-time perspective into change acceptance for an organization, monitoring for key adoption indicators,” Crighton concludes.

Of course, when managing change effectively, whether involving GenAI or another technology, communication with the staff and other stakeholders is essential, and everyone in the organization understands the changes being implemented. Of course, a thorough analysis of the changes being implemented and identifying and resolving reasons for staff resistance will help identify initiatives that aren’t working out as planned and remedy issues for staff where needed.