Intelligent Automation: Your Roadmap to Successful Transformation

By Pascal Bornet
Senior IRPA AI Contributor

Despite this widespread adoption of IA, there remain significant challenges to transforming initial efforts into large-scale, enterprise-wide IA implementations. My takeaway from years of helping companies enables me to provide a transformation roadmap — broken down into four main components.

Project launch

For an IA initiative to succeed, it must be championed at the highest levels of management. According to a McKinsey survey, an IA transformation is twice as likely to succeed if company leadership teams are in alignment on the vision and strategy.

The transformation process begins with upper management. There are three elements C-levels need to envision and communicate:

• The strategic objectives of the IA transformation.
• A business case — including a high-level cost–benefit analysis.
• An overview of the roadmap and key milestones for the next one to three years.

The project should be designed from the top down but implemented from the bottom up (e.g., begin by deploying a pilot).

Project preparation

The preparation phase comes after the high-level roadmap has been issued by upper management. It involves identifying specific IA opportunities and prioritizing them, filling in the implementation details in the roadmap, selecting vendors and partners, building the necessary internal IT infrastructure, and launching a pilot program.

It is critical to identify a broad and compelling set of IA opportunities, so the most strategic ones can be included in the roadmap and prioritized appropriately. Once these opportunities have been identified, they can be prioritized by weighing costs — in terms of technical feasibility and the complexity and effort involved in automating a task — and benefits — in terms of the expected quantitative and qualitative benefits of automation. Based on my experience, I recommend the cost-benefit calculation give high weight to the expected impact of the proposed IA transformations, so the results of the company’s first IA projects can showcase the value of IA to management and employees — and create buy-in for subsequent phases of the transformation.

Project scaling

The project scaling phase consists of successive waves of deployment of the IA opportunities identified earlier. Each wave consists of three elements:

• Redesigning existing processes (e.g., leveraging lean or zero-based redesign approaches).
• Undertaking agile deployment sprints.
• Realizing the actual benefits of the IA program by migrating it to the production environment.

Implementing IA within a limited scope is relatively easy, so proofs-of-concept and pilot programs are likely to succeed, but it’s important to anticipate the increased complexity of scaling the IA transformation to the whole organization.

Leveraging support from third-party consulting and delivery partners is an effective way to scale rapidly and gain momentum with minimal investment. There is no need to spend more than 1–2 weeks selecting these partners. Any differences in cost-effectiveness among them will quickly be outweighed by the competitive advantage you gain from moving forward with your automation journey promptly and accumulating its returns.

Efficient scaling also depends on anticipating and meeting IT requirements. The IT factor is easy to overlook. Over half the IA transformations I’ve supported needed to overcome IT-related roadblocks. Important IT requirements to anticipate include:

• Building the IT infrastructure
• Purchasing enough software licenses
• Preparing and cleaning the data in advance.

Data — the “new oil” — is the fuel for IA decision-making. Otherwise well-designed IA implementations can be let down by poor-quality data. In addition to incorporating a data-cleaning step into your IA deployment, I recommend implementing a company-wide policy of treating data as a valuable asset going forward — to build a solid foundation of data on which later phases of IA transformation can be built and scaled.

Change and talent management

An IA transformation is not primarily a technology project, in my opinion, but a business initiative with people at its center. The two key human elements of the transformation are talent management and change management.

Finding the right talent can be challenging but should be prioritized as early as possible in the IA transformation process. Getting people on board early gives them insight into the background of the project and enables them to take ownership of its outcome. Do not dismiss internal recruitment and retraining as a source of necessary talent. In my experience, recruiting existing, long-standing employees and training them in IA can be more successful than recruiting an IA expert externally and trying to teach them the specifics of the company — priceless knowledge that takes years to build and is difficult to explicitly teach. A McKinsey survey showed the most digitally mature organizations typically use this internal recruiting strategy.

Change management affects the whole organization. It is easy to overlook because it’s downstream from the technological transformation, but it’s vital to the overall success of the project. A survey by Deloitte showed companies that successfully implement and scale IA are those that build buy-in among employees by communicating appropriately and engaging people across the organization.

Companies that have undergone a successful IA transformation have done so by making substantial investments — both human and financial — to generate significant benefits. My four-step IA transformation roadmap allows an iterative approach. It starts with management framing the structural, high-level components of the transformation before they are refined and implemented in the following phases of the project. This ensures consistency of the final outcome with the initial vision while ensuring the deployments are optimally prioritized and monitored. Finally, let us not forget that IA is built by people to be used by people, so employee involvement in the program — and understanding of its opportunities — is crucial to drive IA initiatives to large-scale, enterprise-wide implementations.