Nurturing Intelligent Organizations


Today, AI is taking advantage of increased computational abilities and reducing the cost of data storage – as a result, it is being utilized by organizations to harness large amounts of data and generate state-of-the-art insights, leading to improved decision-making based.

Nonetheless, a number of people believe that advances in technology will lead to a risk of mass unemployment in organizations. So, should we fear artificial intelligence? My belief is no – everyday huge amounts of data is created and large numbers of computational errors are made as part of manual business activities. All of which could have detrimental effects on business decision-making. This data can be analyzed quicker and more efficiently, and with the right level of data quality and integrity arrive at a quality business decision which has been correlated across all data sources.

As an example, from a business perspective, three main areas of organizational activities appear to be prime candidates for AI adoption. Firstly, AI can assist in tailoring customer engagements based on transactional data and product preferences, providing a baseline for sales teams to explore alternative, cost-effective solutions prior to customer engagement. The ability of AI to look for non-linear patterns could be incredibly useful in identifying product usage, timing, and efficiency.

Secondly, international regulators are taking an active interest in AI and the potential benefits it may bring to risk management – although they are also mindful of the unintended consequences these activities could have on data privacy laws and market misconduct. Nonetheless, the ability of AI to look for patterns across transactional data and match results to money laundering compliance listings would have a huge impact on risk activities for the organization.

Thirdly, strategic business transformation programs can benefit from AI. The technology can identify any deviations from the agreed performance indicators outlined during strategic planning exercises for the organization. The results of the deviations are then shown on dashboards to the relevant program and project stakeholders. In the image below I have depicted a representation of a project with a budget of £6million designed to deliver eight milestones. As can be seen by the data, the budget spend at milestone three has deviated considerably from the expected norm. This easy to read graphical representation provides the C-suite with live information regarding the viability of the project, rather than waiting until the end of the project and incurring huge budget overruns. AI can be utilized to generate this overview by interrogating finance and project applications to map the deviation against key performance indicators and extrapolate potential budget overruns should the trend continue.

C-suite owners typically generate projects to deliver on organizational capability shortfalls. The budget is allocated under strategic guidelines to meet specific goals and objectives and is measured by pre-determined performance indicators. On the premise that “what gets measured gets managed”, deviation reporting will ensure that management of errant spend is identified and addressed by the relevant C-suite executive at a very early stage in the project, and can be managed back to compliance or closed down to avoid unexpected costs.

Artificial intelligence may have had its ups and downs over the years but with increasing compliance and regulatory requirements and focus on customer experience, coupled with decreasing time frames for the delivery of strategic investment delivery, AI is destined to become a lot less artificial and a whole lot more intelligent in the not too distant future.