What you’ll learn:
- Key definitions of disruption and disruptive processes
- Monetization examples of disruptive processes
- Data liquidity: the role of data in powering the AI disruption
We’ve reached the pinnacle of the Process Maturity Ladder: Disruptive Processes.
During our journey over the past few weeks, we delved into the business imperative of AI-ready transformations and converting manual processes (e.g., spreadsheets) into automated ones. This critical leap of automation creates a fertile ground for the infusion of human insight and machine intelligence, ultimately leading to the realm of optimization. Now, standing on the shoulders of AI-infused processes, we gaze into the realm of disruption.
As AI proliferates across myriad use cases, we can confidently stake our monetization strategies on the imminent wave of transformative change. This impending surge will not only redefine how businesses operate but also revolutionize industry definitions and expectations, heralding societal changes of an unprecedented scale.
As we navigate the stages of the Process Maturity Ladder (PML), AI acts as a powerful catalyst to spark a revolutionary paradigm shift.. This transformation is not linear or predictable. Instead, it mirrors the nature of the sigmoid function, a concept put forth by futurist Ray Kurzweil.
The sigmoid function, or S-curve, symbolizes how an existing technology matures, plateaus, and is then replaced by a new disruptive technology that begins its own S-curve. This series of S-curves, according to Kurzweil, fuels continuous societal advancement.
What makes AI so profound is that while it has served as a sustaining innovation of subtle refinements and incremental improvement along an existing S-curve, it’s now exploding into something else entirely. It has become, in a concept crystallized by Clayton M. Christensen, a disruptive innovation.
This is about creating a new S-curve altogether. Just think of how ride-sharing apps changed mobility, property-sharing apps transformed vacations, and movie-sharing apps dismantled entertainment.
Disruption challenges established norms, often starting as a seemingly inferior solution catering to an overlooked or new market segment. However, as it evolves, it reshapes the landscape and causes established businesses to falter if they fail to adapt. In a Harvard Business School article, Catherine Cote described the different types of disruptive innovation as low-end and new-market. Both disruptive innovations force incumbents to “retreat upmarket” rather than take on the disruptor.
But disruptive innovation isn’t only about creating market competition. Its tentacles can also disrupt the inside of an organization, upending the status quo and manifesting the kind of internal change that frees an enterprise from inertia. AI is one of those moments where everything can change – seemingly in the blink of an eye. AI-infused and AI-native computation can make one department stand out from the rest. In turn, it can enable one company to outshine all others within its industry. A few of these processes can lead to shifts of societal proportions.
No longer are we looking at rigid, linear processes. Instead, we’re stepping into an era of dynamic, adaptive, and disruptive processes – a brave new world of operational efficiency and strategic agility with AI at its core.
AI: The Key to Disruption
AI has a unique role in this process shift. It’s not just another step in the PML. It reimagines the very idea of process maturity. AI plays a similar role in disrupting the status quo of business processes. Christensen explains disruptive innovation as the process by which a smaller company (hard drive manufacturer) with fewer resources can successfully challenge established incumbent businesses by offering solutions that, although initially inferior, become increasingly appealing as they progress.
Where traditional models emphasize incremental improvements and increased efficiency, AI brings the potential for radical changes. It can redefine processes, create new business models, and shift the competitive landscape. Such disruptive innovations initially target a niche market before eventually reaching a mainstream audience, which is precisely the current trajectory of AI.
In this new AI-infused or AI-native PML, businesses must be ready to embrace disruptive change. This requires a paradigm shift, a willingness to let go of old models and practices, and the courage to venture into uncharted territory. The alternative is to disrupt or become disrupted.
Opportunities for AI-Driven Disruption
The disruptive potential of AI goes beyond mere automation and toward creating systems that can learn, adapt, and improve over time. The potential impact of AI is particularly potent at the higher levels of the PML. As processes become more mature and complex, the opportunities for AI-driven disruption become more profound.
At these levels, AI can not only improve existing processes but can also conceive entirely new ones that were previously unimaginable. This ability to create new, more efficient, and more effective processes is the hallmark of AI’s disruptive potential.
7 Examples of Disruptive Processes and How to Monetize Them
Monetization is a critical aspect of any innovation process. How a business generates revenue from its innovations can significantly influence the success of these initiatives. In this regard, the monetization strategy is as much a part of the PML as the process itself.
However, with AI’s potential for creating novel business models, traditional monetization strategies might not be sufficient. AI opens the door to previously unexplored avenues for revenue generation. This could include new services, product enhancements, or new products created by reimagining existing processes.
1) Customer-Centric AI Assistants
Take the conventional customer service process and blow it up with AI. Think beyond chatbots handling FAQs and imagine an AI-powered assistant that truly understands each customer’s unique needs, preferences, and journey. This disruptive process reimagines customer service from reactive problem-solving to proactive relationship-building.
How to monetize: With superior customer service, businesses can decrease churn and increase customer loyalty. These AI Assistants could upsell and cross-sell products and services based on a deep understanding of customer needs and behavior patterns.
2) Real-Time Supply Chain Management
Let’s stir up supply chains with a cocktail of AI, IoT, and real-time analytics. Instead of static forecasting models, we’ll have an adaptive, self-learning system that responds in real-time to changes in demand, supplier capacity, and global events. Disruption? You bet!
How to monetize: This process can reduce waste, optimize inventory, and avoid costly supply chain disruptions. It also enables dynamic pricing models, adjusting prices based on supply and demand in real-time.
3) Hyper-Personalized Marketing
Say adieu to the one-size-fits-all marketing strategy. Enter a disruptive process where AI analyzes massive datasets to tailor unique marketing messages to each customer. It’s not just personalization – it’s hyper-personalization.
How to monetize: Businesses could sell more effectively by finally reaching the right customers with the right messages at the right time, increasing conversion rates and average purchase value. Additionally, they could sell this highly targeted advertising service to other businesses.
4) Predictive Maintenance
What if we could prevent a machine breakdown before it happens? With AI and machine learning, we’re disrupting the traditional maintenance process. Instead of scheduled check-ups or reactionary repairs, we’ve got predictive maintenance. The machines tell us when they need fixing, optimizing downtime, and improving efficiency.
How to monetize: Companies can save substantially by avoiding unexpected machine downtime and prolonging equipment life. They could also sell predictive maintenance services to other businesses or use their capabilities to create an as-a-service business model, charging for usage rather than outright purchase of equipment.
5) AI-Driven Recruitment
The traditional recruitment process can step aside. It’s time to reimagine hiring with AI-driven talent acquisition. We’ve got AI algorithms sifting through resumes, identifying top talent, and even conducting preliminary interviews. It’s a disruptive process making hiring faster, smarter, and unbiased.
How to monetize: Reduced time-to-hire and improved quality of hires would result in substantial savings. Businesses could also expand this into a service by offering AI-driven recruitment services to other companies.
6) Intelligent Project Management
Imagine a world where projects almost manage themselves. We’re disrupting the conventional project management process with AI. It’s a world where AI assists in project planning, risk assessment, team coordination, and even stakeholder communication. Project management is no longer just about control but intelligent facilitation.
How to monetize: By increasing the success rate of projects, businesses can improve profitability. Moreover, this process could be packaged as a service and sold to other companies.
7) Autonomous Procurement
Procurement gets a new lease on life with AI. The traditional procurement process gives way to an AI-enabled model that can autonomously manage routine transactions, predict inventory needs, and even negotiate with suppliers. It’s procurement, but not as we know it.
How to monetize: This process could lead to significant cost savings due to improved efficiency and the ability to negotiate better prices with suppliers. Companies could also offer AI-driven procurement solutions as a service to other businesses.
Disruption: A Double-Edged Sword Found in a Data Moat
While AI presents vast opportunities for disruption, it also carries substantial risks. The most prominent risk is the potential for competitors to gain an advantage by more effectively utilizing AI to disrupt their processes.
In the AI-infused PML, falling behind in adopting and integrating AI will result in a competitive disadvantage. Businesses may find their processes outmoded and ineffective compared to their AI-empowered competitors – fast. This potential for rapid displacement highlights the importance of staying ahead in the AI race.
The future is here and replete with AI-infused processes. AI’s increasing ubiquity means that its integration into business processes isn’t just an option – it’s an imperative. We are now in the age of the AI-native PML, a model where AI isn’t just an add-on but forms the very core of the process.
Companies must adopt a more proactive stance toward AI integration in this landscape. They need to rethink their processes from the ground up with AI as the central component. This fundamental shift towards AI-native processes requires a drastic change in mindset that views AI not as a mere enabler but as an essential ingredient of success.
But What About the Data?
The pulse of every disruptive process beats to the rhythm of data. Like the unseen undercurrents of an ocean, data powers the velocity and direction of artificial intelligence. The digital age has conferred upon us an incredible bounty – a deluge of data that, when properly harnessed, can bring about extraordinary change. But understanding and utilizing this data goes beyond mere accumulation. It demands rigorous analysis, meticulous curation, and the cultivation of trust.
In this AI-infused world, data isn’t just the fuel but the raw material from which we fashion the future. Every byte of information and every nugget of knowledge is a building block in our AI-driven processes. But the quality of these building blocks matters profoundly, shaping the edifice we eventually create. As we have discussed throughout this series, an enterprise’s data is its reality and moat. Data provides the context that AI desperately needs to stay grounded in truth while it relentlessly disrupts.
Data can also be an invisible gatekeeper, standing as the threshold separating the disruptors from the disrupted. Access to the data can become an asset or a liability, as well. This is based on the quality of the data feeding the disruption process. Boomi Chief Technology Officer Matt McLarty often refers to this as “data liquidity.”
Just as their asset liquidity measures the financial vitality of organizations – how quickly assets can be converted to cash – the liquidity of their data will measure the viability of digital organizations. In other words, how quickly data can be contextualized for consumption in customer and other user experiences. The data that feeds the AI is enterprise intellectual property. Disrupt with eyes wide open and govern your assets well.
Companies that fail to recognize the imperative of data, both in its volume and veracity, risk falling behind in the AI race. In the grand scheme of AI disruption, not all data is created equal. The significance lies in making data actionable, in converting raw information into insights, and subsequently, into actions that carry the potential to disrupt. Thus, data serves as both a stepping stone and a stumbling block in the journey up the Process Maturity Ladder, reminding us that the path to disruption is as much about the information we have as it is about how we choose to wield it.
From sigmoids to disruption and disruptive processes to monetization, we’ve scaled a mountain of ideas within this article. Understanding that the journey isn’t over even as a company reaches the top is essential. With the rapid pace of technological advancement, there’s always a new peak to scale, a new process to disrupt, and a new opportunity for innovation.
Fortunately, AI-ready organizations are well-positioned to compete as they step up the PML. Embracing AI-infused and AI-native processes offers unprecedented opportunities for innovation, disruption, and competitive advantage. Remember, few enterprises and humans will change society. Some will change industries. But many will change their organizations from within. Disruptive processes, no matter how great or small, are extensions of disruptive innovation, and every human has a stake in this game.
Companies that navigate this new landscape with agility and foresight will be the ones that lead the charge into the future. They will disrupt and redefine industries, reshape markets, and set the pace for others to follow. As we forge into the AI era, one thing is certain: the only constant is disruption.
Up Next: In our last article of this series, we’ll summarize what we’ve covered with a detailed call to action. Then I’ll go out on a limb and share some of my predictions for AI in various contexts for the upcoming years.
Check out the earlier posts in this series:
- The End of Business as Usual: Preparing for the AI Revolution Requires a Thoughtful Strategy
- Manual Processes and the End of the Legacy Enterprise
- Automated Processes: Toward an AI-Native Enterprise
- Intelligent Processes: Human and Machine Intelligence for Tomorrow’s Enterprise
- Optimized Processes: Humans Make the Difference
- Chui, Michael et al. (2018). “Notes from the AI frontier: Applications and value of deep learning,” McKinsey Global Institute.
- Bughin, Jacques et al. (2017). “Artificial Intelligence: The Next Digital Frontier?” McKinsey Global Institute.
- Christensen, Clayton M. (1997). “The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail.” Harvard Business School Press.
- Cote, Catherine (2022). “Disruptive Strategy Entrepreneurship & Innovation Strategy,” Harvard Business Review. (Blog Post: Harvard Business School Online)
- Westerman, George et al. (2014). “Leading Digital: Turning Technology into Business Transformation,” Harvard Business Review Press.
- Kurzweil, Ray, “The Singularity is Near” (The S-Curve of a Technology as Expressed in its Lifecycle), Penguin Books, 2006.