Intelligent Automation (IA) offers challenges and opportunities in equal measure, which makes this an exciting emerging area for businesses across all industry sectors to consider. In this article we look to share some insights based on a number of pioneering engagements we have completed in this space.
Q. What is Intelligent Automation (IA)?
A. Intelligent Automation brings together Artificial Intelligence (including natural language processing, machine learning, and machine vision) with Robotics Process Automation software to automate digital tasks in your business.”
Intelligent automation capabilities have existed for decades but have only gathered momentum – and hype – more recently. The conversation has shifted from ‘what if?’ to ‘what now?’. While the economic case for robotics is generally well understood, turning this enthusiasm into real implementation success at scale is, perhaps unsurprisingly, proving less straightforward.
Often, these initiatives start off on the wrong foot. Rather than starting with an overall strategy or game plan they jump straight into immediate ‘quick wins’ to realize tactical value as swiftly as possible. They may achieve that aim, but unless companies define how Intelligent Automation fits within their wider strategy and architecture, it will only ever be a quick fix and the potential for wider value will be missed.
Many leaders are unsure where to start, what these technologies may mean for the organization in the longer-term and how to navigate the impacts on their people and processes. The UK government has announced its vision to make the UK a global AI superpower and in its “Future of Jobs Report 2020,” the World Economic Forum estimated that 85 million jobs will be displaced while 97 million new jobs will be created across 26 countries by 2025.
Organizations are consequently keen to move quickly and identify what this could mean for them. The key considerations outlined below are a good starting point for anyone who is considering investing in IA, while for those with existing programs, they can also serve as a useful checklist to assess whether projects and initiatives are set up for success.
1. Experiment
In many organizations, Intelligent Automation is still considered innovative and experimental. Running proof of concepts or pilots to learn more about its application in your organization continues to be the most effective way to start the journey. The key is to recognize these are experiments that inform a wider strategy, business case and implementation journey (see step 2 below). This is in contrast to a series of siloed pilots that are left ‘stranded’ in production as tactical quick fixes that are hard to support in the long run and never develop any further.
A second key consideration is to look at the business case in effort (FTE) reallocation, not just in headcount reduction terms. What time, effort and capacity could be freed up by robotics, what could that be re-directed to, and what would be the business value of that? In addition, reflect on the business value generated by new business opportunities and models enabled through the use of Intelligent Automation – it’s not just about doing what you always did more efficiently, IA can open up new ways of doing business.
The next challenge is to determine which tool(s) to use. The vendor market remains fragmented and confusing, whilst technologies are evolving quickly creating new entrants and opportunities for acquisitions. Each vendor presents a different angle and claimed Unique Selling Point, whether that’s scalability, flexibility, AI or integration. One feature many tools have in common is a desire to steer you towards whichever System Integrator (SI) they conveniently have a commercial arrangement with. Obtaining independent advice can therefore be very valuable.
Some top tips for success in this experiment phase include:
Structure the scope to gather business case evidence around customer experience (the positive or negative effects), cost reduction, and new business opportunities.
Build a working group from core areas across the organization including business technology and human resources to ensure the potential impacts on the organization are well understood. Don’t allow it to become too technology led or dominated. This phase can also be used to inform how you want to work with IA in future, in terms of the people, skills, capabilities, and divisional roles and responsibilities.
Communicate (at appropriate intervals) to every level in your organization to build awareness and a shared understanding of where you are, where you want to go, and generate cross-organizational excitement about future possibilities.
2. Set the strategy
Intelligent Automation can’t just be about saving money and headcount reduction. It can reduce daily drudgery, empowering employees to do something more interesting and value adding, and so deliver a better, richer service as well as create new offerings for your customers.
This means adopting a “co-biotics” mindset: IA complementing humans, but also humans complementing IA. The introduction of Intelligent Automation into your organization at scale, requires a cross-organizational approach given potential impacts on business models, people strategy and, in many cases, a change to divisional ways of working or responsibilities.
Some top tips for setting an IA strategy include:
Establish how your Intelligent Automation strategy delivers the organizational vision and mission. It will be critical to securing buy-in from around the organization.
Decide which area(s) to prioritize: customer experience; cost reduction; or creating new opportunities or business models. Demonstrate early success and build on this iteratively.
Examine your organizational values and align the approach. The introduction of Intelligent Automation involves ethical considerations such as “if mistakes are made which cause harm, who should bear the risk and responsibility?,” and “what is our appetite for job cuts as a result of increased Intelligent Automation when balanced against responsibility or capability to retrain?”
3. Define the operating model
Consider how any Intelligent Automation works with the other capabilities and ways of working in your operating model. Some key operating model considerations may include:
The best approach to ensuring sustainability is to develop in-house talent, and our experience has shown that existing staff can learn and adapt to IA well, so don’t just assume that you have to hire new. However, there are likely to be gaps in the skills and capabilities of your people that require a revised people strategy and change in approach for how you attract, retain and develop in-house talent.
Many organizations find it challenging to reach consensus about where and how Intelligent Automation solutions fit within the enterprise and business architecture.
Consider sustainability, for example, building centers of excellence to foster capability and develop talent as well as ensuring Intelligent Automation skills are embedded within the business.
The brave new world is taking shape. It’s not the miracle cure that the hype may have you believe, but it is certainly an important development that offers the opportunity for positive transformation. Significant people and ethical factors need to be considered but, done well, Intelligent Automation can improve your operational efficiency, customer experience and employees’ working experience.”
Jon Bradbury, partner
The very first steps in the journey are undoubtedly the most important.
Case study
Berkeley successfully worked with a client to deliver IA solutions based on Artificial Intelligence and natural language processing, providing enhanced customer service and enabling thematic trend analysis on large volumes of speech and text.
The challenge / opportunity
The organization had struggled to provide service and support at the scale required by their customers and as a result was suffering reputational damage. Additionally, this organization had a manual and time-consuming mechanism for aggregating data and identifying trends impacting their customers. As a result, the organization was slow to respond to market needs.
As part of a wider portfolio of change, Berkeley ran a series of IA pilots and proof of concepts designed to address the organization’s key business challenges, and then shaped successful business cases and the subsequent projects for full IA implementation.
The solution
A pipeline of innovative solutions was created, ranging from chatbots and intelligent automated advisors, able to learn and analyze sentiment to provide the most appropriate interventions, through to online content managed and updated by artificial intelligence.
The solutions were shaped, developed and landed in the business by the business functions themselves (with our support). However, technology teams, other business areas and support functions were all part of the cross-organizational approach, encompassing vision, strategy, idea creation and experimentation, ensuring a real impact to the organization and its customers.
Results
Customer satisfaction has increased, productivity in key areas has improved, the volume of transactional support required has reduced, and employees are being diverted to higher-value, less clerical and ultimately more rewarding work.
