AdobeStock_276700411, Sergey Nivens


Combining AI and BPA for Sustainable Competitive Advantage

FEBRUARY 20, 2024

Summary: Sustainable competitive advantage in today’s business landscape requires a strategic foundation that goes beyond traditional levers of business growth. As the market evolves, it is imperative for enterprises to embrace technological disruption driven by AI and allied technologies. In this context, BPA lays an important foundation for business model innovation and acts as a catalyst for change.


  1. BPA establishes a strategic foundation for long-term success by enhancing operational reliability, thereby minimizing governance issues. This empowers the enterprise to become AI & ML-ready, propelling further innovation.

  2. In a rapidly evolving business landscape, the levers that once provided sustainable competitive advantage in the past are becoming less relevant today. Looking ahead, enterprises with smarter and more efficient processes supporting their products and services will be better positioned to swiftly adopt AI & ML, making them much more relevant in the market.

The need for innovation

Sustainable competitive advantage is achieved when an enterprise possesses strategic levers that give it an edge over competitors and concurrently it is sustainable over a longer period of time, meaning the competition will take time to develop similar capabilities.

Traditionally, it has been possible to generate such an advantage by investing heavily in marketing and communications, supply chain, or additional sales channels in addition to the investments in the core product or service. However, with time, it becomes important to complement these core capabilities with technological disruption to retain the competitive edge as other capabilities become commoditized.

With customers becoming more tech-savvy, they are demanding improved customer experience and empathetic attention to their needs. This has led to a situation where customer churn for established enterprises has increased as they are being challenged by new entrants, which understand the importance of an intelligent mix of product offerings and thoughtful use of technology to provide users with better user experience and quicker turnaround times. For instance, leveraging technologies such as BPA, CRM and Machine Learning for smarter operations has helped multiple fintech enterprises to snatch away market share from bigger banks, in large part due to their agility and efficiency in providing services, such as new account opening, credit approvals, and brokerage solutions, and these are now setting new benchmarks for the whole industry.

AI – The next game-changing technology

It would not be wrong to say that advancements in AI and ML seem to be pointing towards the next megatrend, as it has all the ingredients of the past mega innovations such as industrialization, printing press, broadcasting, cellular technology, internet, cloud computing, social media etc.

In 2023, ChatGPT rose to fame, enabling an unprecedented pace of innovation in the field of AI in just about a year. The hype around ChatGPT made 45% of enterprises increase their budget for AI. AI is by no means a new technology. Gartner has been talking about AI as a trend already back in 2017. However, it was not until the past year that AI grew so fast that it cannot be ignored by enterprises anymore. In fact, AI is listed as one of the main technological trends for the upcoming years by all big analysts, such as Gartner and Forrester. But the rise of AI has also brought up discussions around safety, security management, and more. This leads to the question: how should enterprises approach AI and how can it provide a sustainable competitive advantage?

Integrating AI

Many enterprises are in the dilemma of creating a roadmap and strategy for incorporating AI in the medium to long term. Especially weighing the pros and the cons of AI and considering discussions around data security, privacy, and deep fake. How can enterprises explore and leverage AI within their business models? We have the following three recommendations for you in this regard:

  1. Data quality: make your critical processes reliable with automation wherever necessary to improve the quality and reliability of data.

  2. Build your unique AI story and roadmap (however broad this might be right now) but have a vision in place.

  3. Conquer by breaking down: bifurcate the efforts in stages:

    1. Stage 1: explore and identify in-house talent or hire to mandate the team on impactful experimentation.

    2. Stage 2: empower the team with budgets, management bandwidth, and forum.

    3. Stage 3: select impactful proof of concepts and/or proof of value.

    4. Stage 4: start small in scaling up with a smaller team or business unit. Use the feedback loop to reiterate, improve, and scale.

Quality data lays the foundation for successfully integrating AI – and quality data has a direct correlation with automated, strongly governed, touch-free processes. These processes can then feed the data pipeline that would be needed to create your own decision trees and decision models. This is why Business Process Automation should be at the very start of your AI journey. Business Process Automation sets the ground by streamlining operations across all aspects of a business. It provides enterprises with the necessary agility to respond quickly to market changes and to incorporate new emerging technologies such as AI for ultimately incremental and sustainable innovation.

BPA and AI for innovation

Let us look at the three most important pillars of any business and understand how process automation and machine learning can help enterprises create sustainable competitive advantage. These three important pillars are:

  1. Customer experience

  2. People

  3. Processes

1. Customer experience

A bank, competing in the highly competitive landscape, discovers issues related to governance and speed in its mortgage application process. Upon analysis, it is revealed that the application process involves more human touchpoints than planned, and subjective decisions are applied in multiple cases. This has resulted in a decrease in the Net Promoter Score over the last 12 months, significantly impacting customer experience. To address these challenges, a combination of Business Process Automation (BPA) and Artificial Intelligence (AI) can play a pivotal role.

BPA: streamlining processes

  • Business Process Automation (BPA) can streamline the mortgage application process by identifying and eliminating unnecessary human touchpoints.

  • Automation tools can be employed to handle cases, tasks, and routing, thereby reducing the likelihood of errors and increasing overall efficiency.

  • This not only accelerates the application process but also ensures a standardized and compliant workflow.

AI: incremental innovation

  • Artificial Intelligence can bring about incremental innovation by optimizing decision-making in the mortgage application process. ML algorithms can analyze historical data to identify patterns, enabling more accurate and consistent decision-making.

  • By reducing the reliance on subjective judgments, AI helps minimize errors and ensures a more objective evaluation of applications. Additionally, machine learning models can continuously learn and adapt, improving decision accuracy over time.

Furthermore, AI-driven chatbots and virtual assistants, combined with BPA, can enhance customer interactions, providing real-time assistance and guidance throughout the application process. This not only improves customer satisfaction but also contributes to a smoother and more efficient experience. In the current market scenario, recognizing and addressing unmet needs is crucial for business success. Previously, fulfilling such needs might not have required extensive technological investment. However, in today's landscape, with technology disrupting various industries, incumbents must fortify their business models by strategically incorporating advanced technologies.

2. People

A consumer company realizes that the speed to market for their new product launches has lagged behind competitors, and newer brands are innovating much faster. Layers of bureaucracy built up over time reduce speed, resulting from multiple layers of pending approvals, opaque processes, and other obstacles. This leads to internal discouragement to innovate, and in the long term, such an enterprise may lose market relevance and fade into history.

BPA: augmenting innovation pipelining process

Business Process Automation (BPA) can play a crucial role in augmenting the innovation pipeline process by introducing transparency, speed, and efficiency.

  • Manage all tasks from one place: BPA tools enable the consolidation of tasks into a single platform, clearly defining responsibilities and streamlining workflows.

  • Reduce paperwork: BPA facilitates the reduction of paperwork by leveraging document management services offered within BPA tools, such as the Flowable connection with SharePoint.

  • Reduce approval backlogs: BPA helps in reducing approval backlogs by sending timely process notifications via pre-defined Service Level Agreements (SLAs).

AI: incremental innovation

Investing incrementally in AI can significantly enhance the understanding of patterns in innovation. For example:

  • Analyzing past innovations: AI can analyze past innovations, distinguishing successful and unsuccessful ones. This process helps in creating decision trees and data tables to predict the future success of innovations, guiding the investment in the best possible ideas.

  • Pattern recognition: AI and machine learning capabilities allow the identification of patterns and trends in consumer preferences, market dynamics, and competitive landscapes. This assists in making informed decisions and tailoring innovation strategies to meet evolving market demands.

  • Optimizing decision-making: by leveraging AI incrementally, the consumer company can optimize decision-making processes, ensuring that resources are strategically allocated to initiatives with the highest potential for success.

3. Process

A manufacturer realizes that their procurement and purchase department is experiencing delays in new vendor onboarding, leading to frustrating delays in crucial projects and increased project costs.

BPA: orchestrating the end-to-end process

  • Streamlining procurement process : BPA can play a pivotal role in orchestrating the end-to-end procurement process for the organization by connecting to major systems such as Salesforce, SAP, and SharePoint.

  • Use Low-code drag-and-drop forms: BPA tools can employ low-code drag-and-drop forms to create an intuitive interface for requestors. This capability empowers end-users to fill out requisition forms with ease, reducing the need for future back-and-forth communication.

  • Enhance visibility: provide end-users with the capability to view as many details as required while filling out the requisition form. This enhances transparency and reduces potential misunderstandings or errors during the procurement process.

  • Make BPA the single source of truth: execute the entire procurement process on the BPA platform, leveraging the connector ecosystem available with BPA tools. This ensures that all relevant information is centralized within the BPA system, reducing the chances of data discrepancies, and providing a comprehensive overview of the procurement status at any given point.

AI: incremental innovation

  • Intelligent decision-making : by incrementally integrating AI and ML capabilities, the manufacturer can enhance decision-making within the procurement process. AI algorithms can analyze historical data to identify patterns, helping in predicting potential delays or bottlenecks in vendor onboarding. This enables proactive measures to be taken, reducing project timelines and costs.

  • Optimizing vendor selection : AI can assist in evaluating and selecting vendors based on performance data, ensuring that new vendors align with the organization's requirements and standards. This optimization contributes to a more efficient and reliable procurement ecosystem.

  • Continuous improvement : Machine learning models can continuously learn from procurement data, enabling the identification of areas for improvement. This iterative learning process allows the organization to adapt and refine its procurement strategies over time, ultimately enhancing overall efficiency.


As more enterprises embrace the new wave of technological disruption, it is imperative for enterprises to not get overwhelmed with the kind of innovations happening. Even in the past disruptions, it has been seen that multiple enterprises fail in their endeavors because of the approach to either invest huge amounts without a long-term story and roadmap in place or mis-timed acquisitions, which lead to value loss for all the stakeholders.

We at Flowable believe that a practical approach is to first look internally i.e. critical business processes that are at the core of your business and make them as touch-free, efficient, and intelligent as possible. This will lay a sound foundation for your AI exploration by providing you with a reliable stream of data, on which you could build your own decision models.

Tushar Srivastava

BPM enthusiast and former Gartner Analyst with a decade of experience in business process and requirement gathering, process mapping and management.

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