With every new software release from RPA sector leaders, there is always much to be excited about as vendors continue to push the technological boundaries of workplace automation. Whether those new capabilities focus on cognition, or security, or scalability, the technology available to us continues to be a source of inspiration and innovative thinking in how those new capabilities can be applied.

But success in an RPA deployment is not entirely dependent just on the technology involved. In fact, the implementation design framework for RPA is often just as important – if not more so – in determining whether a deployment is successful. Install the most cutting-edge platform available into a subpar implementation design framework, and no amount of technological innovation can overcome that hindrance.

With this in mind, here are seven tasks that should be part of any RPA implementation plan before organizations put pen to paper to sign up with an RPA platform vendor.

Create a cohesive vision of what automation will achieve

Automation is the ultimate strict interpretation code: it does precisely as it’s told, at speed, and in volume. But it must be pointed at the right corporate challenges, with a long-term vision for what it is (and is not) expected to do in order to be successful in that mission. That process involves asking some broad-ranging questions up-front:

• What stakeholders are involved – internally and externally – in the automation initiative?
• What are our organization’s expectations of the initiative?
• How will we know if we succeeded or fail?
• What metrics will drive those assessments?
• Where will this initiative go next within our organization?
• Will we involve our supply chain partners or technology allies in this process?

Ensure a staff model that can scale at the speed of enterprise automation

We tend to spend so much time talking about FTE reduction in the automation sector that we overlook the very real issue of FTE sourcing (in volume!) in relation to the implementation of automation at enterprise scale. Automation needs designers, coders, project managers, and support personnel, all familiar with the platform and able to contribute new code and thoughtware assets at speed.

Some vendors are addressing this issue head-on with initiatives like Automation Anywhere University, UiPath Academy, and Blue Prism Learning and Accreditation, and others have similar initiatives in the works. It is also important that organizational HR professionals be briefed on the specific skillsets necessary for automation-related hires; this is a relatively new field, and partnering up-front on talent acquisition can yield meaningful benefits down the road.

Plan in detail for a labor outage

The RPA sector is rife with reassurances about digital workers: they never go on strike; they don’t sleep or require breaks; they don’t call in sick. But things do go wrong. And while the RPA vendors offer impressive SLAs with respect to getting clients back online quickly, sometimes it’s necessary to handle hours, or even days, of automated work manually. Having mature high-availability and disaster recovery capability built into the platform – as Automation Anywhere included in Enterprise Release 11 – mitigates these concerns to a specific degree, but planning for the worst means just that.

Connect with the press and the labor community

Don’t skip this section because it sounds like organized labor management only, although that’s a factor too. Automation stories get out, and local and national press alike are eager to cover RPA initiatives at large organizations. It’s a hot-button topic and an easily accessible story.

Unfortunately, it’s also all too easy to take an automation story and run with the sensationalist aspects of FTE displacement and cost reduction. By interacting with journalist and labor leaders in advance of launching an automation initiative, you’re owning the story before it can be owned elsewhere in the content chain.

Have a retraining and upskilling initiative parallel to your automation COE

Automation can quickly reduce the number of humans necessary in a work area by half or even more. What is your organization’s plan for redeployment of that human capital to other, higher-value tasks? Who occupies those task chairs now – and what will they be doing?

Once the task of automation deployment is complete, there is still process work to be done in finding value-added work for humans who have a reduced workload due to automation. Some organizations are finding and unlocking new sources of enterprise value in doing so – for example, front-line workers who have their workloads reduced through automation can often ‘see the forest’ better and can advise their superiors on ways to streamline and improve processes.

Similarly, automation can bring together working groups on tasks that have connected automations between departments, allowing for new conversations, strategies, and processes to take shape.

Have an articulation plan for RPA and other advanced technologies

RPA and cognitive automation do more than improve the quality and consistency of work – they also improve the quality and consistency of task-related data. That is an invaluable characteristic of RPA from the organizational data and analytics perspective, and one that is often overlooked in the planning process.

While it might take days for a service center to spot a trend in common product complaints, RPA platforms could see the same trend in hours, combine that data in an organizational data discovery environment with IoT data from the production line, and identify a product fault faster and more efficiently than a traditional workforce might. When designing an automation initiative, it is vital to take these opportunities into account and plan for them.

Create a roadmap to cognitive automation and beyond

RPA is no more a destination than business rules engines were, or CRM, or ERP. These were all enabling technologies that oriented and guided organizations towards greater levels of agility, awareness and capability. Similarly, deploying RPA provides organizations with insight into the complexity, structure and dependencies of specific tasks. Working towards task automation yields real clarity, on a workflow-by-workflow basis, of what level of cognition will be necessary to achieve meaningful automation levels.

While many tasks can be achieved by current levels of vendor RPA capability, others will require more evolved cognitive automation, and some will be reserved for the future, when new AI capabilities become available. By designating relevant work processes to their automation ‘containers’, an enterprise roadmap to cognitive automation and AI begins to take shape.