<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=240394&amp;fmt=gif">
Artificial Intelligence


The AI megatrend is heating up across every mature industry. It has the potential to change the fundamental structure of businesses from agriculture to healthcare to cybersecurity to logistics - and everything in between. As businesses and academia continue to research towards developing truly and completely unsupervised ‘General AI’, great strides are being made in developing ‘Narrow AI’ solutions using Machine Learning (ML) and Deep Learning (DL) for specific use-cases. 

As organisations start to take their tentative first steps towards exploring how AI and related technologies can unlock benefits and help resolve multi-faceted problems facing their business and industry in general, it is vital for them to understand that adopting AI may not mean replacing traditional analytic techniques. AI should supplement their existing analytic methods rather than become a hammer looking for a nail. This requires a patient and detailed approach to developing a strategy and the supporting constructs. The document articulating the strategy itself may be less important than the experience of the process of its development, an honest and intense reflection on the key elements of the problems & opportunities that organisations hope to resolve through AI. 

Developing appropriate use-cases to solve is a vital to the AI journey. For one business it may well be the direct improvement of cost-base through the adoption of AI assisted RPA, while for another it may be more nuanced, such as using ML techniques for metadata discovery across its data organisation to achieve privacy and regulatory excellence around personally identifiable information of its customers. No matter what the use-case maybe, organisations must always appreciate the fact that AI is a synergistic exercise between man and machine. As machines continue to inch into the limelight of visible decision making, organisations must account for the inevitable conflict, whilst continuing efforts to build trust between these two equally critical components needed for true transformative benefits.



We at Coeus take an integrated approach to developing the AI strategy for our clients, one that orchestrates existing capabilities with new technologies and analytical techniques. We promote a vision that’s rooted in pragmatism and considers human sensitivities and the importance of people enablement which are vital ingredients for the long-term success of an AI journey. 

Our experience with clients has shown that the success of AI programmes depend heavily on: 

  • Leadership commitment to making the best out of AI investments
  • The rigour of the use-case selection process
  • The quality, availability and currency of data that’s input to AI models
  • The thoroughness with which governance structures are formalised
  • The handling of the ‘skills shortage’ problem

With our fiercely independent ethos, we have provided unbiased advice to our clients and have led their thinking on the key foundational elements of a long-term AI strategy. We combine our deep expertise in key related areas of strategy, operating model, enterprise architecture, automation and sourcing to collaborate with our clients in kickstarting their AI journeys, specifically focusing on: 

  • Assessment of Data maturity and Analytics capabilities
  • AI use-case assessments
  • Development of AI investment cases
  • Development of the AI strategy, operating model and implementation roadmap
  • Definition of theAIsourcing strategy 
  • Technologyplatform and vendor assessments 
  • Definition of governance structures aroundcross-functionalAI and automation programmes