Intelligent Process Automation (IPA) – Humans Hold the Key to Unlock Value
Several recent developments are driving a new wave of artificial intelligence (AI) interest across most of the vertical sectors. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices from various sources is growing exponentially. Organizations that strike the right partnerships between people and machines can radically enhance their competitive advantage through new offerings, sharper value propositions, and more efficient processes.
We know that early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors. They deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership. Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment. That is why AI adoption by mainstream businesses is relatively low against the significant interest it has garnered lately.
AI will only be impactful if humans are willing to engage with it and that means understanding how this technology can best be leveraged to meet changing human preferences. Intelligent process automation (IPA) that combines AI with process automation is the next wave of AI-based solutions that is changing the way business is done in nearly every sector of the economy. IPA systems detect and produce vast amount of information and can automate entire processes or workflows, learning and adapting as they go. Robotic process automation (RPA) is software robots to replace computer activity traditionally performed by humans. These bots can open spreadsheets and databases, copy data between programs, compare entries, and perform other routine tasks. RPA is ideal for repetitive, rules-driven processes that span several IT systems—it’s like a macro on steroids. RPA is now able to combine AI capabilities – including natural language processing, speech parsing, knowledge-bases, machine learning, deep learning, autonomics, and machine vision – with automation.
Combining AI and RPA
Intelligent process automation is achieved by combining the rapid payback of RPA and advanced learning potential of AI. It is attractive option for service organization and companies with large legacy systems. Employees have to work together with AI and RPA to optimize their processes into IPA. This makes for a natural transition from automation to intelligence, which occurs when a human intervenes in a rules-based process. Over time, AI learns from these human interventions, resulting in increased autonomy of the process.
Benefits of implementing IPA
While typical automation is able to streamline repetitive, rules-based business processes, it is dependent on human intervention to manage exceptions. In comparison, AI can be used to approach tasks that require more complex decision-making and analysis – the main difference being that AI has self-learning capabilities, which means that it can tackle tasks which don’t involve repetition. AI can make sense of unstructured data-sets where there’s variation, and it also improves over time. Factors that make RPA attractive include fast implementation and as a result also a quick return on investment. As RPA covers repetitive, rule-based tasks, it allows employees (especially on the operational level) to focus on more challenging, higher value activities such as monitoring, quality control and problem solving – and thus amplifying the workforce potential of every individual. Some of the benefits of implementing IPA solutions using RPA include:
Positive impact on operational metrics with automation
Easily accessible from any device while on the go
Quickly reachable expertise on specific domain knowledge
Quicker and better decisions
Non-intrusive integration with enterprise systems
Automated knowledge-management and diagnostics
Guided support through structured collaboration
Reduced change management requirements
Significant reduction in operational costs (from 40% to 75%)
Significant reduction in turnaround time (from 20% to 70%)
Significant increase in productivity (up to 30%)
Improved quality control and governance abilities
IPA (using RPA) and business process outsourcing (BPO)
Within traditional BPO we’re seeing a lot more organizations frustrated with their relationships, as providers are lagging behind in delivering efficiencies and increased innovations. Organizations are looking to their BPO provider to make use of new technologies to lower costs and pass the savings on to them and with the promise of reduced errors and enhanced compliance, improved job satisfaction, deeper analytical insights, and 24 x 7 “always-on.” Robotics automation and augmentation tools along with AI, delivers just that. Early predictions put cost reductions via RPA in the range of 60% in comparison to 15-30% offered by traditional BPO approaches.
However, automation is just one part of the story – the real focus for the future is on the overall digitization of business with a focus on tools such as RPA but also deep analytics and digital orientated talent and a shift towards predictive and cognitive capabilities. Successful digitization including the successful automation of complex services is as dependent on the orchestration of diverse initiatives and proper service delivery as on the technology itself. RPA and BPO is therefore an ideal partnership, enabling providers to take an integrated approach, maximizing value.
Many companies find AI challenging. Machines learn inductively by processing ever-greater volumes of data, and this learning does not happen on its own. Humans need to train the algorithms. With limited in-house AI capabilities, companies often turn to vendors, and these vendors sometimes oversell their AI capabilities, leading to disappointing AI pilot projects.
Approach to implementing IPA
IPA does not require a significant infrastructure investment since it addresses the presentation layer of information systems. RPA software, for example, sits on top of existing systems, enabling it to be implemented to achieve rapid returns without changing the IT back end. In some cases, companies can get RPA systems up and running—and delivering value—in as little as two weeks.
Overarching IPA strategy
IPA initiative must be grounded in a clear understanding of the overall strategic imperatives of the business and the role of the next-generation operating model in helping to achieve it. It also needs to be aligned with the overall digital transformation roadmap for the enterprise. That requires a clear articulation of the target end state and the journey to reach it. Such clarity allows business leaders to evaluate and align on the approaches and capabilities to implement to drive the operating model.
Build a portfolio of IPA solutions
Automation projects implemented in silos are doomed to fail. Organizations need to envision and implement holistic optimization programs to maximize return on investment. By themselves, individual technology implementations are insufficient to capture value. End-to-end process redesign in innovative ways is required to transform the way a group works. A detailed roadmap for implementation should be created to identify all automation-enhancement opportunities and allow businesses to sequence IPA initiatives by balancing their impact with the feasibility of scaling solutions from initial use cases.
Co-create IPA solutions
Identify the opportunity along the overall value-chain. Get a clear understanding of the current-state process and identify where the IPA enhancements will enhance the process. Develop the future-state process solution. Next build a prototype MVP (minimum viable product) which is the most stripped-down version of the product that can still accomplish the task in the future-state solution. Test, train and finalize the prototype IPA solution. Successful solution with intelligent process automation is then scaled-up to the production environment. Ensure synergy across multiple projects being done in parallel. Go first for the opportunities that have bias to speed and impact. The rapid returns from early pilots help to secure support from stakeholders and executive sponsors for a much deeper program to harness the potential achievable from a full IPA transformation.
Data & analytics platform transformation
Simple RPA systems are fairly easy to layer atop legacy systems. But the degree of difficulty rises with the sophistication and scale of the automation and intelligence that companies are introducing. This might require a comprehensive overhaul of the data & analytics capability of the organization. Organizations must ensure that the new systems integrate seamlessly with call centers and other service centers, and they must establish a performance center to manage the overall automation and intelligence activities. And, as companies move into AI, they need access to large internal and external data sets. Vendors vary widely in their ability to support these integrations, however, so organizations need to critically assess vendor capabilities in this area.
Rethink & transform the operating model.
The overall digital transformation initiatives might have overlaps, dependencies, and conflicts that will need to be reconciled with the IPA transformation initiatives. Depending on the digital maturity of the company, overall rethink of the value-chain might have significant impact on the operating model already. This will call for close coordination with the digital transformation initiatives of the enterprise to ensure alignment.
One successful way to sustain value creation is by creating a center of excellence (CoE) to govern the transformation and support the rapid deployment of IPA solutions through capability building, certification and standards, vendor management, and the creation of a library of reusable solution patterns. Such a CoE should be centrally located and can be fairly small in size because it can call on existing lean or process-optimization CoEs, while business ownership and execution should sit in the lines of business or in digital factories. The most successful way to build lasting IPA capabilities is through a learn-by-doing approach that combines coaching, on-the-job training, and knowledge sharing. To capture value at enterprise scale, organizations need people with deep skills in IPA levers, process redesign, and lean principles as well as domain expertise. Technology skills alone will not be sufficient.
Organizational change management
As in any large transformation program, a robust organizational change management work-stream with communications plan will be required to help manage redeployment, generate excitement, and align the change story with corporate strategy. Success in establishing the new execution model will depend on how far it is aligned with the organization’s culture and how well people are able to adapt to agile practices. In addition, change champions will need to be developed internally to make the transformation a success. In addition to managing employee reaction to the changes, organizations must also change their capabilities so they can meet two needs. The first, and obvious, need is to find a way to hire employees with modern technology skills at a time when competition for these people is high. To address talent scarcity, companies should consider managing their AI resources centrally, at least initially. The other, more subtle need is to retrain displaced employees for new roles and responsibilities.
Businesses are using intelligent process automation in different ways. They invest in and develop new platforms, engage with customers, and win over advisors, all at a dramatically lower cost. However, these companies are only scratching the surface of the full potential of IPA. Leaders are those that embrace IPA capabilities as part of a next-generation operating model and move quickly to capture the value from them, pulling away from the laggards who choose to take on project by project, with gradual deployment in silos. Innovation and speed is critical to reap the full potential of intelligent process automation.