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Bizagi CEO Gustavo Gomez says that ‘AI alone cannot transform how the enterprise works. It needs to work seamlessly alongside people with both governance and business context.’ To make a convincing case, focus less on the technology itself and more on the outcomes it can deliver. Whether it’s reducing manual work, speeding up decision-making, or improving customer experience, AI should be tied directly to business needs.
When introducing AI, begin by framing the business problems it can help solve. Identify specific pain points and demonstrate how incorporating AI can address the issues that are arising in current processes. For example, if response times from your customer service team aren’t meeting customer expectations, demonstrate how AI can create recommended responses that speed up the process.
Leaders want results. Make your case using simple metrics: cost savings, time saved, revenue gained. For example, if your procurement team is struggling to keep up with the purchase requisitions submitted by the business due to high levels of manual data entry, demonstrate how implementing AI can ease some of the strain and help them reach decisions faster.
Adopting AI often raises valid concerns around security, cost, complexity, bias, or workforce impact. Acknowledge these early and offer balanced responses.
• Security: Answer any data security questions early and make sure to leverage AI technologies designed for enterprise that have data governance and security measures built-in, so projects don’t get stuck at ideation.
• Cost and ROI: Address concerns around cost by proposing a low-risk pilot before rolling out across wider operations. Define clear success metrics like time saved, errors reduced or customer satisfaction scores to measure ROI.
• Bias and transparency: Demonstrate your commitment to ethical AI by outlining measures to ensure transparency, address potential bias, and build trust through responsible governance.
• Workforce impact: Tackle fears around job loss by positioning AI as a way to enhance human roles, not eliminate them. Emphasize how it can reduce repetitive tasks and support better decision-making.
Start with a clear, focused business problem. Choose something that you can demonstrate value with but is still manageable in scope. This is your opportunity to prove the value of AI without overcommitting resources. Use real data to measure impact and gather feedback from the teams involved. If the results show clear benefits, use these early wins to build credibility and make the case for further investment.
Identify individuals within the business that are open to innovation and work with them to use AI to create solutions that meet their department’s specific needs. Once you see success in one area of the business share the results to create an appetite for the technology among other teams.
By tying AI initiatives to clear business needs, addressing concerns, and proving value early, you build a strong foundation for adoption. Focus on the key metrics AI can impact and build support by showing what’s possible, not just what’s new.
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