By Faryal Madad Naqvi
AI has gone from an idealistic world of dreams to a practical reality. Organizations across a variety of industries scramble to employ AI to save time and improve decision-making, customer experience, product and service development, and otherwise. Yet even with a race to get AI into daily practices, organizations that successfully implement AI remain few and far between. The problem often isn’t the technology itself but the organizational barriers to transformation. By addressing the following barriers effectively, executives can pave the way for a successful AI initiative that can lead to many more.
One of the biggest barriers for many organizations is a lack of purpose for getting AI. It’s the new thing; everyone is doing it. Therefore, we need it, too. However, when departments attempt to implement pilot projects without regard to organization-wide goals, such explorations become futile.
A clearly defined roadmap from leadership will demonstrate to employees why AI is being implemented and what specific needs it will serve going forward, how success will be evaluated, areas of importance, projected timelines for achievement, and whether/depth of cross-departmental collaboration is needed for success. The clearer and more people understand why its purpose, the easier adoption will be for everyone.
Data – the backbone of any successful AI initiative – also poses challenges to implementation via siloed or unreliable data systems, inconsistent formatting, and lack of data integrity protocols. Without a strong pipeline of reliable data resources, AI models are destined to fail.
A strong foundation must be built before data can improve business practices with artificial intelligence. Therefore, investment in data governance frameworks, third-party tools for integration and reliability and buy-in from IT/data governance teams is needed. If an organization can determine a reliable single source of truth before implementation, data challenges will fall by the wayside.
All organizations should strive to create talent from within and recruit talent outside. Training programs in relevant areas will help upskill existing employees for potential inclusion in AI implementation (and AI recommendations). Furthermore, partnerships with universities and other organizations and institutes specializing in talent development will help overcome this roadblock. Competing with external markets will require organizations to create a culture of learning for their existing employees.
Transparent communication is key. Employees must be treated as empowered stakeholders whose concerns must be valued and addressed before any trial-and-error period begins. Experiments that succeed in the pilot phase should be shared as case studies for less resistant adoption; if employees know the benefits early on instead of feeling blindsided midway through operations, culture shock will be reduced significantly.
AI requires a great deal of investment – from cloud infrastructure to specialized software tools to maintaining personnel willing to take on such projects. Small and medium enterprises may see this as a potential death sentence and are hesitant to adopt such costly initiatives.
AI presents a risk from the security and ethical standpoint – privacy violations, biased algorithms – and organizations risk legal issues without scrutiny.
AI is the future of business operations – but it cannot run itself without culturally developed readiness on the part of human engagement components to learn new strategies and implement them successfully. By addressing organizational barriers as noted above before challenges arise – including a lack of purpose and direction; failure to align data quality access; lack of talent; resistant cultures; financial implementation restrictions; and ethical concerns – organizations can position themselves more competitively in an ever-evolving technological future.
