Conversation-as-a-Service insights for new sense-making frameworks

As conversation-as-a-service gains momentum, it is providing a two-way marketing, sales and services interaction between customers and organisations from both the public and private sector. Being able to undertake chatbot dialogue at scale, consistently across all digital touchpoints, is setting a new benchmark for customer and indeed employee experience. An even bigger value can be found within the new form of Big Data known as dialogue data, which provides deeper and richer insights of user choices, pathways and outcomes, known as user decision journeys. Even more profound, conversation-as-a-service is an accelerator towards the co-existence of a hybrid workforce of humans and chatbots working together, whilst marginalising the constraints of organisational siloism, supervision and compliance.  

Driven by the convergence of technologies involving the Cloud, Big Data, Artificial Intelligence, Machine Learning, Omnichannel, Robotic Process Automation, Blockchain and IoT is enabling Chatbots to orchestrate this capability and lead the way towards the normalisation of conversation-as-a-service as the prime means for interaction.

Conversations at scale, text or voice, across any language, will have far-reaching changes to the very fabric of time-to-value, whilst blurring market boundaries, cultivating new niche markets, growing innovative competitiveness, increasing productivity and decreasing risks through greater transparency and traceability.

The challenge is no longer the technology, but the immaturity of new sense making frameworks, which challenge conventional business models in the way we work.

Here are some of the conversation-as-a-service sense-making shifts, which are already underway, but have yet to become part of the very core of public and private sector strategy:   

1. From lag indicators to lead indicators
2. From supply-driven to demand-driven
3. From tacit knowledge to working, measurable knowledge assets
4. From subjective decisions to scalable knowledge-driven decisions
5. From overwhelming complexity to masking complexity in practice  
6. From regulatory content to codified procedures
7. From managing micro risks to managing nano risks at the granularity of choice
8. From manual compliance to transparent compliance automation
9. From bounded decision-based data forms to collecting contextual data
10. From training to instant upskilling for learning in the moment
11. From learning management to enriching and extending knowledge at the edge  
12. From skills siloism to orchestration of capabilities as needed  

At the core of these sense-making shifts is innovation, which is the application of knowledge in a novel way, primarily for socioeconomic benefit. Conversation-as-a-service and knowledge are intertwined. The value of measuring knowledge-based choices, pathways and outcomes will accelerate the growth of real-time evidence, which in turn will deliver innovation faster and faster, creating more opportunities and more jobs.