Leaders today must continually re-envision the way their teams work, and AI presents an enormous opportunity to pull your team out of manual, mundane tasks to focus on the work that matters. Generative AI is expected to raise the global GDP and transform our work, but it is up to individual leaders to implement AI in a way that strengthens (rather than distracts from) their day-to-day operations.
Three problems often stand in the way of successful adoption. These include:
Rigid tooling
Some AI offerings make it difficult for teams to tailor applications to meet their specific business needs or workflows, hindering AI adoption. AI technology should be flexible enough to suit your organization’s unique way of working.
Messy workflows
Teams already struggling with disconnected workflows are at risk of exacerbating organizational silos by adopting AI in an ad hoc fashion. For example, AI can accelerate a range of individual tasks (creating briefs, analyzing data, structuring information) but if your organisation is already struggling to keep these workflows connected, poor implementation of AI might deepen the fractures between teams. Map and connect your existing workflows, before incorporating AI to accelerate work.
Disparate data
Prompting AI with high-quality information yields quality results. But, if your most critical information (performance data, customer feedback, campaign briefs, audience information, localisation tactics, channel performance) lives in different places, the quality and relevancy of any AI output will deteriorate. To implement AI effectively, first, connect your critical data and make it visible and accessible.
Leaders today must continually re-envision the way their teams work, and AI presents an enormous opportunity to pull your team out of manual, mundane tasks to focus on the work that matters. Generative AI is expected to raise the global GDP and transform our work, but it is up to individual leaders to implement AI in a way that strengthens (rather than distracts from) their day-to-day operations.
Three problems often stand in the way of successful adoption. These include:
Rigid tooling
Some AI offerings make it difficult for teams to tailor applications to meet their specific business needs or workflows, hindering AI adoption. AI technology should be flexible enough to suit your organization’s unique way of working.
Messy workflows
Teams already struggling with disconnected workflows are at risk of exacerbating organizational silos by adopting AI in an ad hoc fashion. For example, AI can accelerate a range of individual tasks (creating briefs, analyzing data, structuring information) but if your organisation is already struggling to keep these workflows connected, poor implementation of AI might deepen the fractures between teams. Map and connect your existing workflows, before incorporating AI to accelerate work.
Disparate data
Prompting AI with high-quality information yields quality results. But, if your most critical information (performance data, customer feedback, campaign briefs, audience information, localisation tactics, channel performance) lives in different places, the quality and relevancy of any AI output will deteriorate. To implement AI effectively, first, connect your critical data and make it visible and accessible.
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