Intelligent automation is best seen as a spectrum of business capabilities

IA is a spectrum or continuum of capabilities ranging from robotics at one end to artificial intelligence at the other, as shown below:

  • Robotic desktop automation, with manual intervention and is process driven.
  • Robotic process automation (RPA), with digital triggers and is process driven.
  • Machine learning (ML), with prescriptive analytics and decision engines and is
    data driven.
  • Artificial intelligence (AI), with deductive analytics and is data driven.

At its most fundamental level, RPA is associated with “doing” capabilities and following pre-programmed rules, whereas ML and AI is concerned with simulated “learning” and “thinking” capabilities respectively. And all fall under the IA umbrella.

At the heart of both robotics and AI is the same thing that drives today’s businesses: data, but in very different ways. Automated machines ‘collate’ data, whilst AI systems ‘understand’ it. Coupling software systems capable of automatically collecting incredible amounts of data with systems that can intelligently make sense of that information is the nirvana mentioned earlier. But why is this coupling proving so hard to get to for so many?

Only 10% of financial service companies have implemented IA technology to scale

The majority of FS firms are struggling with a range of business, technology, and people challenges to implementing IA successfully. The heat is growing on them too to get on with this, especially with many expecting BigTech players like Amazon and Alphabet to be their major competitor in only a few years’ time. Only around one in four organisations has the technological maturity to implement cognitive automation technologies comprising machine learning, computer vision and biometrics. Most organisations still have RPA, or – at best – Natural Language Processing (NLP), forming the backbone of their automation initiatives.

There are four recurrent reasons for this slow take-up for many:

  • A clear business case for automation needs to be established.
  • Persuading leadership to commit to a cohesive intelligent automation strategy.
  • Talent with a deep understanding of RPA and AI technologies is required for successful automation deployment and scaling-up – but many businesses are struggling to find the right resources to implement intelligent automation effectively.
  • Lack of an adequate data management strategy hampers progress as AI-based automation algorithms require the right data at sufficient volumes.

Making your organisation IA empowered

To break through these barriers and blockages to IA empowerment and prepare your organisation for IA, there are five key steps to impacting on the business areas which will yield the greatest and most immediate benefits:

  • Develop your vision. Appoint a C-suite executive as talent transformation sponsor, to define a top-down, company-wide IA talent vision and strategy. This vision will include a definition of the expected business benefits and how these align to corporate strategy. Consider whether you’ve the resource capability in-house for IA visioning and implementation and what capability is required to be outsourced through partners.
  • Define your Policy. Launch a cross-functional team, with a dedicated budget and executive-level stakeholders, to define policy with regard to IA. This team should be business-led but well supported by the IT function. It will serve as the gate-keeper for the demand for process automation, building an enterprise roadmap that is aligned to the IA strategy and vision.
  • Establish priorities. Identify the necessary governance changes, such as creating a comprehensive governance framework to support the IA implementation, and also to manage automation demand and supply, manage stakeholder expectations, and ensure proper adoption of the new processes by the business.
  • Acquire capabilities and partners. Develop and pilot a training program for IA teams and user experience & interface teams to cross-train on implementing IA. Three types of talent are needed to design, implement, manage and run an IA project: data scientists with a machine learning background, technologists with a computer vision or similar background, IA implementation experts and specific domain experts.
  • Manage the Change. Plan a comprehensive change management approach for rapid adoption of IA. This includes the impact and transition plans required to scale the automation project, continuous monitoring of the machines and their output, and the development of back-up plans to address the business implications of any machine failure.

There are many hurdles for financial services organisations to realising the benefits of a successful IA programme, but with the know-how and expertise developing so fast, once the biggest challenges of developing the business case and persuading the leadership team to adopt a programme are tackled, the doorway to IA nirvana can be crossed.

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