As businesses seek to leverage these advancements to drive commercial growth and efficiencies, they recognize a journey lies ahead. Possibly the best example of this journey is the evolution of AI in vehicles. Features like blind spot detection, lane departure warning, and front collision warning were necessary prerequisites for self-driving capabilities. However, as recent news reports demonstrate, there is much to be done before self-driven vehicles become the norm.
With this backdrop, we examine eight elements that will help businesses assess their ML/AI maturity, as well as manage their adoption journey.
1. STAKEHOLDER ENGAGEMENT
Needless to say, ML/AI investments should begin with securing the buy-in from the impacted business process stakeholders. Who better understands the decision-making process and the opportunities to ‘automate’, ‘assist’ or ‘augment’ through ML/AI than the process stakeholders? It is not uncommon for us to see organizations dive into ML/AI, while the impacted stakeholders sit disengaged on the sidelines. They are either not actively participating in defining opportunities or worse still, are debating the longevity of their jobs within the organization. The latter should be a non-issue if the strategic objective driving the ML/AI investments is profitable business growth.
2. CAPABILITY CYCLES
Academic origins for ML/AI date back at least four decades prior to 1997, when IBM’s Deep Blue machine defeated then the reigning chess champion, Gary Kasparov. While it has taken about half that time for it to penetrate our everyday lives, it helps illustrate that advancements come in waves. Two of the most common reasons for the oft-repeated “ML/AI hype” are: [a] an inability to see the limitations of the current ML/AI wave, and [b] a short-sighted vision for the longer-term transformation that future ML/AI waves could drive. In contrast, businesses that are successful in disrupting the norm, see discrete capability cycles within their ML/AI journey. An appreciation for what can and should be targeted in each cycle is critical to continued success.
3. DATA AND MODEL GOVERNANCE
Data is central to the success of ML/AI adoption. With that comes an understanding of the role of data strategy, data architecture, data management, data security, data privacy and permissible use. These facets have been brought front and center in the recent weeks with the potential non-permissible use of social media data. As new data sources emerge over time and, existing data sources and ML/AI solutions multiply in volume, a strong stewardship and governance practice becomes an essential ingredient to balancing the risks of ML/AI with its rewards. Further, all analytic and ML/AI models developed and deployed to support business processes also require appropriate governance controls to ensure compliance with operational or regulatory requirements.
4. DATA ARCHITECTURE
As an extension of the Data Governance point, the need for a mature Enterprise Data Architecture is a natural foundational step. Emerging data sources, various data types/formats, sensor and IoT data streams together with a push towards near real-time decision-making capabilities, are all calling for a rethinking of enterprise data architecture. We recommend following an agile strategy in regard to designing your data architecture. However, sooner than later, a sufficiently robust ‘baseline’ enterprise architecture needs to emerge from the agile work streams, that will catapult the organization forward in their ML/AI journey.
