7 key success factors that lie at the heart of a Cognitive Enterprise
Businesses now want to attain the next level and work on cognitive technologies such as Artificial Intelligence (AI), data analytics, blockchain, machine learning, IoT along with automation, to reshape their enterprise architecture.
They want to capture opportunities by visualising unique platforms and meet the expectations of customers by working on concrete insights from unstructured data. They want to navigate through complex structures and transform into a Cognitive Enterprise.
But, there’s more to a Cognitive Enterprise. Apart from working on the right technology, a Cognitive Enterprise has to meet several criteria to be successful and sustain growth in the future. It includes redesigning workflows, leveraging data, igniting talent and winning with trust.
There are seven key factors that determine the success of a Cognitive Enterprise. Let’s take a look at them:1. Identify core business platforms and scale:
Create the business platforms and work on 3S- Scale, Speed and Scale. Businesses have to bring a competitive edge to their core platforms and strive to be the best in the chosen areas by applying the right strategy. The business platform must be highly architected, have highly interoperable system components and infrastructure built using cloud, AI and other exponential technologies.2. Use data analytics to your advantage:
Find strength in numbers and leverage insights from data. Companies should exploit the data they gather from multiple channels and link all of it to understand their customer and fulfil expectations even before these are expressed. Few companies that have transformed into Cognitive Enterprises ‘listen’ to their customers and apply AI-based tools to make sense of the data and use it for personalised messaging to customers.3. Architect your business for change:
A Cognitive Enterprise conceptualises a blueprint to define its structure and operations. Thereby, organisations should focus on planned growth and align themselves with emerging technology architecture. Companies can no longer afford to take a wait-and-see approach to determine what works for others in their industry, or which technology or service is going to “win.” Enterprise architecture – like business strategy – must anticipate the future but also have its options open.4. Humanised workflows around AI:
Companies are re-orienting themselves and relying on AI and exponential technologies to direct their actions. At the same time, they are providing humanised experiences to customers. Leading organizations deploy AI to augment employee decisions, help them interpret what customers want and interact with customers in ways that builds trust. Decisions are supported at the edge, whether that’s a salesperson on a shop floor or a customer service rep in a call centre.5. Agility and adapting fast:
Cognitive Enterprises should discover and evolve new strategies. They should be able to think and deliver fast. Organisations must be open to learning and have the courage to change direction on the basis of what they learn. Business platforms, which support well-integrated and cognitively enabled workflows, are in turn becoming the ideal habitat for agile leaders and teams.6. Ignite talent:
It can be difficult for companies to find skilled workforce that can work on cognitive technologies as every company is looking for professionals from a limited talent pool. Under such circumstances, companies should apply AI tools to their HR workflows and gain insights into what skills they really need now and in the future, to determine how the demand can be met. Companies can also use AI tools to identify talent within the organisation and enable peer-to-peer learning.7. Trust and security:
These two parameters are critical to a Cognitive Enterprise. On business platforms, especially those that interoperate with other platforms, a concrete way to earn the trust of customers and continue to earn the right to access, store and utilize their personal data is to reduce the vulnerability of single players across the ecosystem, which can happen by leveraging AI and machine learning capabilities. To stay ahead of malicious actors equipped with advanced technologies, it is important to develop and leverage a robust security toolkit. This includes conducting rapid and continuous testing with working code implementations informed by up-to-date data on recent attacks and defences.Conclusion: