This is the second in our series of blog posts discussing the art of AI adoption. Our first blog highlighted<\/a> the importance of organizational change management and enterprise portfolio and program management. In this blog, we focus on operational excellence and the importance of business architecture, business process improvement and performance measurement to successfully implement AI technologies.<\/p>\n Operational excellence refers to the relentless pursuit of conducting business in an efficient and effective manner, aligning processes with strategic goals to exceed customer expectations and outperform competitors.<\/p>\n Below, we\u2019ll explore the role these aspects of operational excellence play in the successful adoption of AI with a few real-world examples.<\/strong> We\u2019ll also share common AI technologies, and how organizations are getting started on their AI Journey.<\/p>\n Business architecture lays the groundwork for successful AI implementation. A well-defined business architecture ensures that AI initiatives align seamlessly with your organization’s objectives, strategies and existing systems. This strategic alignment is crucial because even the most sophisticated AI solutions can fall flat without a solid business framework supporting them.<\/p>\n Moreover, business architecture offers a clear roadmap for AI adoption because it identifies the processes and systems you will enhance. By targeting the business touchpoints and components that AI can bolster, business architecture guides your placement of AI technologies for optimal impact.<\/p>\n For example, Salesforce adjusted its business architecture<\/a> to ensure the seamless integration of AI with its customer relationship management (CRM) systems. They found that 63 percent of service professionals rely on AI<\/a> to help them serve their customers quickly and more efficiently.<\/strong> Integrating AI within their CRMs allows both Salesforce and their customers to significantly improve customer service and sales performance.<\/p>\n With a robust business architecture in place, the focus shifts to business process improvement. The key is to refine existing processes, eliminate inefficiencies and prepare the ground for AI interventions. This stage is integral because it ensures your implemented AI solutions operate within a streamlined, efficient environment.<\/p>\n Without process improvement, you risk layering AI risks over redundant or sub-optimal processes, significantly diluting its effectiveness. By refining the processes first, businesses ensure that AI technologies enhance an already efficient system, thereby unlocking its true potential.<\/p>\n Recently, Banco Bilbao Vizcaya Argentaria (BBVA) streamlined its processes to provide a seamless customer experience<\/a>, creating the right environment for AI-driven personalized financial advice and seeing substantial improvements in customer satisfaction and engagement.<\/p>\n They did this by recognizing 90 percent of all customer interactions were human-to-machine, enabling the application of machine learning tools<\/a> to scan large customer data sets.<\/strong> This allowed BBVA to predict their customer\u2019s future income and expenses for the next month and offer customized services.<\/p>\n Performance measurement is the mechanism that continuously validates and enhances your AI solution. By establishing clear objectives for AI implementation and regularly tracking progress against these objectives, organizations can ensure the effectiveness of AI technologies, identify areas for improvement, and continually enhance your AI initiatives.<\/p>\n General Electric (GE) and Wells Fargo highlight the importance of performance measurement in different areas. GE employed AI in predictive maintenance<\/a> and tracked key metrics to maximize equipment availability and lifespan.<\/p>\n Wells Fargo, on the other hand, implemented AI chatbots to improve customer service, with customer satisfaction scores and resolution times serving as important performance indicators<\/a>. Only you can determine which AI performance measures are most important for your business.<\/p>\n Business architecture, business process improvement, and performance measurement are interconnected pillars essential to AI adoption. Business architecture sets a strategic blueprint, aligning AI initiatives with the organization’s vision. Process improvement refines this stage, enabling AI to operate optimally by removing inefficiencies.<\/strong><\/p>\n Performance measurement, meanwhile, acts as a continuous feedback mechanism, ensuring AI initiatives deliver the expected value and informing necessary adjustments.<\/p>\n However, these pillars become even more important for certain AI technologies because of their unique demands.<\/p>\n Certain AI technologies require the operational excellence elements described above for successful adoption. Here are 5 examples:<\/p>\n ML algorithms learn from data to make predictions or decisions without explicit programming. However, their efficacy heavily relies on well-structured data inputs, which require streamlined business processes. Moreover, the alignment of ML projects with business goals<\/a> (ensured by business architecture) and ongoing efficacy tracking (through performance measurement) are critical to realizing ML’s full potential.<\/p>\n NLP enables machines to understand and respond to human language. The successful integration of NLP, such as chatbots<\/a>, requires well-defined processes for customer interactions and a robust business architecture that accommodates this technology. Performance measurement, meanwhile, enables ongoing improvements based on user feedback.<\/strong><\/p>\n This technology interprets and makes sense of visual data. Whether it’s used in quality control in manufacturing or image recognition in healthcare, computer vision relies on well-defined processes for image data collection and analysis, strategic alignment with business objectives, and regular performance evaluation to ensure accuracy.<\/p>\n RPA uses software robots to automate repetitive tasks. For successful RPA adoption<\/a>, you must thoroughly optimize and standardize processes to avoid automating inefficiencies. In addition, business architecture is key to determining which tasks to automate, and performance measurement validates the effectiveness of automation.<\/p>\n Predictive analytics uses historical data, machine learning and statistical algorithms to predict future outcomes. You need business architecture<\/a> to align predictive analytics projects with business goals, while process improvement ensures clean, high-quality data input.<\/strong> Performance measurement delivers ongoing model tuning and validation.<\/p>\n Independent of any specific AI technology, organizations must first determine their AI vision, AI strategy and AI governance models.<\/strong><\/p>\n AI vision determines what level of AI adoption is possible and outlines the end-point destination for performance improvement and impact. AI strategy puts flesh on the bones of the vision with more detailed goals and timelines related to people, processes, technologies and analytics. And the AI governance<\/a> model establishes the policy and structure for AI tool use, including ethical considerations, security, data privacy and compliance.<\/p>\n Another common hurdle is developing a workforce and change plan to address the many people-related challenges AI adoption creates. Some organizations are establishing AI centers of excellence and conducting AI readiness assessments to address these issues.<\/p>\nBusiness Architecture<\/h2>\n
Business Process Improvement<\/h2>\n
Performance Measurement<\/h2>\n
Common AI Technologies<\/h2>\n
1. Machine Learning (ML)<\/h3>\n
2. Natural Language Processing (NLP)<\/h3>\n
3. Computer Vision<\/h3>\n
4. Robotic Process Automation (RPA)<\/h3>\n
5. Predictive Analytics<\/h3>\n
How Organizations Are Getting Started With AI Adoption<\/h2>\n
Conclusion<\/h2>\n