Many organisations turn to Artificial Intelligence to solve their business problems and respond swiftly to changing market conditions and customer demands. However, the journey towards AI adoption is often marked by uncertainties.
The major barriers stem from a lack of understanding of the benefits of AI, challenges in measuring its business value and ad hoc approaches to AI adoption. These factors lead to increased technical debts as organisations struggle to navigate the complexities of AI integration without a clear strategic direction or means of evaluating its impact on the bottom line.
Confronting these challenges head-on demands more than just a haphazard approach- it requires a systematic and structured strategy. Through the utilisation of foundational frameworks such as Business Capability Models (BCMs) and Business Outcome Statements, organisations can effectively appraise and prioritise AI use cases that are most likely to be delivering the expected outcomes for the businesses.
In this edition of the newsletter, we delved deeper into Gartner’s research report, ‘4 Top Practices That Help EA/TI Leaders Add Value to Artificial Intelligence Initiatives*, ‘ to pick the best AI adoption practices and blended it with the technical adeptness of Zuci’s AI experts, to help technology leaders to plan their AI initiatives more strategically.
Hello readers,
I am Ameena Siddiqa, Marketing Strategist at Zuci Systems. To gain deeper insights into the aforementioned report, I tapped into the expertise of our experts, Prasanna Venkatesh, Vice President-Delivery, Digital Engineering,Clarence Fernando, Delivery Manager, Digital Engineering and Sridevi Ramasamy (Shri), Delivery Manager, Digital Engineering.
Let’s dive into the topic!
Best Practice – 1: Identifying the Business Capabilities to Target Your Business Outcomes
Ameena: What role do Business Capability Models play in driving business outcomes, and how are they helpful in identifying Artificial Intelligence (AI) initiatives?
Shri: A Business Capability Model (BCM) provides a structured framework for understanding an organisation’s capabilities across various business functions. It helps organisations assess their existing capabilities in areas such as operations, marketing, customer service, and finance.
By understanding their strengths and weaknesses, Business Capability Models can become powerful tools in spotting opportunities for AI projects. Essentially, they help organisations take stock of what they’re good at and where to improve. This understanding can then guide them in pinpointing areas where AI technologies could make a real difference. Furthermore, BCM helps ensure that AI initiatives are aligned with the organisation’s overall business strategy and objectives. By mapping AI capabilities to strategic priorities, organisations can prioritise initiatives with the most significant potential to create value and competitive advantage and drive sustainable business growth in the digital era.
Ameena: How can organisations leverage Business Continuity Models to safeguard AI initiatives and ensure long term success?
Shri: Business Continuity Models ensure that AI systems can operate effectively even in the face of disruptions or unexpected events. They help identify potential risks to AI systems, such as data breaches, system failures, or changes in regulatory requirements. By assessing these risks, organisations can implement measures to mitigate them and ensure uninterrupted AI operations.
AI projects are heavily reliant on data and tech infrastructure. Through BCMs, these systems are built with redundancies and fail over capabilities to withstand failure or catastrophic events. This robustness helps to maintain continuous AI performance, even in challenging environments. This includes strategies for data backup, system restoration, and alternative operational modes to minimise downtime.
Consistency in AI operations builds trust among clients and stakeholders, underscoring the organisation’s reliability and commitment to delivering dependable AI-driven solutions. This commitment is crucial for nurturing and upholding a favourable reputation within the industry.
Best Practice – 2: Assessing the Business Capability Readiness for AI
Ameena: How can SMEs and decision-makers collaborate to create a roadmap for AI readiness that aligns with the organisation’s strategic goals and objectives?
Clarence: Success with AI hinges on collaboration between Subject Matter Experts (SMEs) and decision-makers. SMEs bring deep domain knowledge, while decision-makers provide strategic direction. This collaboration sparks impactful AI use cases that help organisations to build a roadmap for AI adoption and navigate the complexities of AI. Here are some key steps to consider when crafting a roadmap for AI adoption;
> Evaluate existing status: SMEs should evaluate their current capabilities, including technical infrastructure, data assets, and workforce skills. Decision-makers can start off the assessment process by gathering input from relevant stakeholders and conducting thorough analyses to identify strengths, weaknesses, opportunities, and threats related to AI adoption.
> Define strategic goals: Together, SMEs and decision-makers should define clear strategic goals and objectives that AI adoption aims to support. These goals include improving operational efficiency, enhancing customer experience, increasing revenue, or entering new markets. Alignment with broader business objectives is essential to ensure that AI initiatives drive tangible value for the organisation.
> Identify use cases: Collaboratively, SMEs and decision-makers should identify specific AI use cases that align with the organisation’s strategic goals. This involves brainstorming sessions across various departments, such as marketing, sales, operations, and customer service, to explore how AI can lend a hand. Decision-makers can prioritise these ideas based on their potential impact and how achievable they are in practice.
> Develop a roadmap: Based on the assessment of current capabilities, strategic goals, and identified use cases, SMEs and decision-makers can develop a roadmap for AI readiness. This roadmap should outline critical milestones, timelines, resource requirements, and dependencies for implementing AI initiatives.
Prioritising futuristic goals and objectives over current ones is crucial to steering an organisation in line with its strategic direction.
For instance, when crafting an AI roadmap for a SaaS product, it’s imperative to anticipate forthcoming trends in offerings rather than solely addressing present ones. By emphasising forward-thinking strategies, SMEs and decision-makers can ensure that the AI readiness roadmap is in sync with the organisation’s long-term goals and remains flexible in adapting to evolving market dynamics.
Ameena: How can businesses measure the potential ROI of AI initiatives during the readiness assessment phase?
Prasanna V: Measuring AI initiatives’ potential Return On Investment (ROI) during the readiness assessment phase is critical for businesses to justify investments and make informed decisions. Let’s explore some key steps organisations can take to gauge the ROI potential:
> Calculating operational cost savings: Organisations can estimate the financial savings generated by AI initiatives compared to existing processes. By projecting these annual operational cost savings, they can quantify the direct financial benefits of AI initiatives.
> Reinvesting for business growth: It’s essential to look beyond cost savings and consider reinvesting these funds to foster business expansions and capitalise on growth opportunities. Reinvesting in research and development, marketing, talent acquisition, or infrastructure upgrades can enhance the impact of AI initiatives and bolster long-term ROI.
> Going beyond traditional ROI metrics: It’s vital to broaden their perspective beyond mere financial gains and consider both monetary and non-monetary benefits to gain a comprehensive understanding of AI’s potential. This holistic approach empowers businesses to make intelligent decisions about where to allocate their resources and what to prioritise.
Best Practice – 3: Building an AI Capability to Business Capability Model
Ameena: What steps are involved in developing a robust AI capability to Business Capability Model?
Prasanna V: Organisations should consider incorporating AI at various phases when developing a robust AI capability within a Business Capability Model. For instance, when defining a BCM, AI plays a vital role in offering insights and suggestions for defining parameters. Subsequently, in the execution phase, AI becomes instrumental in sourcing and analysing data from recommended sources, providing essential insights into market competition and demand dynamics. AI takes centre stage as the model progresses to the analysis stage and helps with in-depth data analysis and score weighting. This process necessitates a meticulous design approach to systematically define and manage decision parameters throughout the model’s development and application.
Furthermore, leveraging machine learning algorithms and capabilities at each stage of the process augments the integration of AI into the business capability model, enhancing its effectiveness and impact.
Ameena: How often should the AI capability to Business Capability Model be reviewed and updated to reflect changes in technology, business priorities, and market conditions?
Prasanna V: AI models, even when trained, tend to degrade over time. Therefore, it’s vital that the AI capability integrated into the Business Capability Model is designed with dynamic data sources and adaptable models that stay current with technological advancements, shifting priorities, and evolving market conditions. Machine learning techniques such as back-propagation and feedback loops play a vital role in ensuring their relevance over time. These techniques are not only key to training the AI models but also utilise user feedback during their utilisation to keep them relevant. Usage patterns and user sentiments can be analysed to trigger internal events where appropriate actions are required to realign the model with current business needs. The review and update process should be continuous and ongoing, with consistency measured continuously rather than at fixed intervals.
Ameena: Can you provide examples of businesses that have successfully implemented an AI capability to business capability model, and what lessons can be learned from their experiences?
Prasanna V: Various organisations across industries have effectively integrated AI capabilities into their business capability models, yielding tangible results such as improved operational efficiency, enhanced customer interactions, and informed decision-making. For example, in the e-commerce sector, AI helps forecast demand, understand seasonal preferences, identify market trends, optimise inventory management, and create personalised customer experiences.
Similarly, AI enhances supply chain capabilities in services and manufacturing through data analysis, enabling better forecasting and cost reduction. These successes underscore the importance of industry-specific AI solutions tailored to operational needs, robust data analytics infrastructure, and cross-functional collaboration.
It’s important to note that strategically investing in AI is not a one-and-done deal, and organisations should keep an eye on its performance and be ready to adapt to the evolving business terrain.
Originally featured in our LinkedIn Newsletter, ZtoA Pulse.
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