For many years, Balance Scorecard Institute’s Terry Sterling has worked across different industries helping a variety of organisations in the development of their strategies. He identifies three common challenges they face in the execution stage, namely the absence of real-time visibility into performance indicators and strategic goal-achieving progress, the ineffective use of resources causing numerous project delays and late deliveries, and the challenges in identifying patterns and trends in massive volumes of data.
It is crucial to be able to accurately establish objectives and associated Key Performance Indicators (KPIs) used to measure strategy to successfully overcome these difficulties. The possibility of success for an organisation is completely non-existent if these factors are not correctly established and integrated into their plan. Aligning objectives and strategic goals across all business units and divisions that may be present inside the organisation is the second component of this equation.
Adding AI to the Equation
Oftentimes, businesses miss the window of relevance when trying to interpret the sheer amount of data they have collected. They don’t know what to do with it, or they leave it on the backburner until the need is blindingly evident. AI is groundbreaking in gathering and analysing enormous volumes of data, much more than is humanly possible.
AI can automate the collection and analysis of KPI data with exacting accuracy and timeliness. This data can then be used by AI to generate predictive insights, helping organisations anticipate future trends and make proactive decisions. Thus, organisations can anticipate changes and adapt their strategy execution accordingly. When potential risks are identified and solutions are provided to mitigate them, businesses can achieve resilience in execution process.
Moreover, AI can provide a more in-depth understanding of the relationship between different KPIs. It can uncover correlations and patterns that may not be apparent to the human eye. This can reveal new opportunities for improvement and help organisations to fine-tune their objectives and strategy execution.
AI can also provide real-time visibility into resource allocation. It can monitor the budgeting of resources and track their usage, providing organisations with up-to-date information on whether resources are being managed effectively. AI can also consider the significance of various strategic initiatives, the accessibility of resources, and their prospective risks and benefits. This can ensure that their strategy execution is on track.
Siemen’s AI-Driven Strategic Execution
Like many other businesses, Siemens AG, a global powerhouse in energy, healthcare, and infrastructure solutions, encounters the three problems listed above. To deal with them, the organisation put in place an AI-driven strategy execution plan. In the first stage in the plan, they reviewed objectives and KPIs to make sure they were accurate and essential to promoting organisational success. They achieved this by combining internal and external data to determine which KPIs and targets were most pertinent to the organisation’s strategy.
The algorithms were employed to find patterns and trends in the data and give real-time insights on performance metrics and progress with objectives. The AI algorithms found places where the company wasn’t achieving its desired strategic goals and offered suggestions for better performance.
Hanna Henig, Siemen’s CIO, emphasised that digitalisation requires a cultural change in the organisation. How successfully, how swiftly, and how adaptably can a business shape this change? Carrying out surveys on AI literacy within the organisation and junction-making workshops to correct misconceptions and elevate skills was a crucial determinant of their implementation viability.
As it can be challenging to determine the exact financial impact of projects powered by AI, many organisations may be reluctant to adopt it. After all, imposing AI with unready employees, with no heed to its true value and place in the grander strategy scheme would be a waste of resources.
Pilot an AI-assisted project with just one division first. Assess internal talent to see if contacting external expertise is more worthwhile. Allow ample time for training and experimentation, such that you build it into the company’s culture.
Ensure that your company has a robust data governance framework in place, as AI relies heavily on data. Organisations need to ensure that their use of AI is transparent and accountable, and that it respects privacy and data protection laws. Don’t be swayed purely by its trendiness.
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