Gen-AI has the potential to substantially improve business efficiency and effectiveness through end-to-end business rules fostering operations rigor, high productivity, and smart interfaces for real-time interactions and greater intelligence. Many enterprise boards and executive teams are pushing their business to engage and take the ownership as a means to achieving operations rigor and growth.
Business stakeholders are confused and nervous about Generative AI, due to concerns about the quality of data and their current complex, siloed business and technology environment. They don’t have trust or confidence that Gen-AI can provide accurate results to generate operations rigor and business growth. For many enterprises the business didn’t take the ownership of the data or adopt a unified approach to operations rigor fearing Gen-AI can mislead and cause risks to business operations. Furthermore, they cannot relate their intuition to Gen-AI language models due to the current data environment itself not instilling confidence in business decisions.
Due to hype surrounding Gen-AI and its perceived magical capabilities that it can do wonders automatically, and with an abundance of tools and services, it further adds confusion for businesses to engage and take ownership.
Transitioning to Digital Business with Gen-AI
Gen-AI is a powerful concept that can effectively help the business in their operations environment with simplified, end-to-end workflows for collaboration. By enabling businesses to aggregate data from various sources and analyze it in an agile way at the sprint level, it helps them to converge data (quality) that supports their intuition with market intelligence. This combination becomes the single source of truth (SSOT) for enterprise data that becomes a data asset. In addition, applying end-to-end business rules at each stage further solidifies understanding of language models that can enhance further productivity for operations rigor and growth. This approach should be done at the incremental level rather than taking a big bang approach.
Many enterprises invested heavily in digital transformation thinking it is all about technology and tried to execute in their current complex environment resulting in minimal returns.
Digital business (business facing) in a simplified environment in the cloud away from the current complex environment generates data, innovation assets. Further, specific Gen-AI language models and other AI algorithms significantly boost productivity through smart interfaces and interactions in the real time.
Digital business requires a different ecosystem that is purely business facing with simplified technology and business with end-to-end workflows. It requires technology talent with an agile and engineering approach giving business experience for engagement. Practitioners, not consultants, who can simplify business cases, create end-to-end workflows to help the customer’s business to establish a particular use case with business intuition for engineering teams. Engineering teams bring together data from various sources in real time with business intuition for them to converge and collaborate with an incremental approach in the cloud. Leveraging new data technology like Snowflake, Databricks they create data and innovation (market facing) assets.
Also read: Digital Business vs. Digital Transformation: Understanding the Key Differences
The Role of Engineering Approach for Digital Business Growth
This engineering approach helps to ensure quality, security, and compliance (auditable, traceable). Further, data scientists and engineering teams work together to identify language models and algorithms related to their business intuition. Converging them through a SSOT and applying them through these models, businesses can trust and take ownership for further effectiveness and productivity of operational rigor and business outcomes. In order to achieve speed, consistency, scale with business outcomes across the enterprise requires a Digital Business Methodology (DBM) and is enforced by a fully automated, end-to-end, cloud based Digital Business Platform (DBP) in the client environment.
This simplified business technology environment with an agile engineering approach, DBM, and DBP can create unlimited opportunities for growth. Digital business is not transformation, it is business by itself that creates reusable data/innovation assets, security, Gen-AI, AI as part of a highly intelligent business differentiation in the market. Currently most complex technology has become a debt, whereas digital business create assets.
In order to bring Digital Business by itself requires a culture of agile engineering, an incremental approach, business and technology integration with more business acumen, and simplified technology with latest technologies for data, Gen-AI/AI. This cannot be outsourced, it needs a different culture and talent with business and technology acumen without change management.
Initially enterprises can leverage a strategic partner and catalyst with proven experience and digital business tools like a DBM and DBP to realize outcomes. At the same time partners can instill a culture and identify talent internally and externally to create scale through the digital business academy (DBA). Partners also have experience in taking a holistic approach to creating a digital business ecosystem.
In general the new paradigm, digital business, can create new ways of business in a simplified, agile culture that is strictly business facing to reduce operations cost substantially and create unlimited opportunities for growth.
