By 2022, 55% of EA programs will be supported by artificial intelligence (AI)-enabled software, freeing enterprise architects for more internal management consultancy work.
Maximize the AI opportunity and craft your artificial intelligence strategy
Enterprise Architects and Technology innovation leaders are uniquely positioned to identify opportunities for creating new data-driven business models that leverage advanced analytics for market differentiation.
AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs). AI comes in two forms — quantitative techniques that can predict behavior from data; and neural network techniques that can classify complex objects, such as images, video, speech and sound.
Organizations using AI technologies can harness data to both extract new insights from data and automate processes that are uneconomical applications for the human labor that is otherwise needed to perform the process. Any industry with very large amounts of data — so much that humans can’t possibly analyze or understand it on their own — can utilize AI. Some industries, such as healthcare, are ripe for disruption. AI applications will bring new levels of customer service, decision quality, scale and operational efficiency to processes formerly operated by human labor.
If an enterprise adopts a distributed architecture style, like microservices, this problem is further exacerbated. Yet, the utopia for any enterprise is to have all teams align to the same goals to resolve bottlenecks at every step. This is where the benefits of a standards framework, like The Open Group Architecture Framework (TOGAF), come into play.
How To Get Artificial Intelligence “Right”
Build AI right
To “build AI right,” it is key to first establish the basic vocabulary of AI — a technical dialect of how people “speak data.” At the very least, you should determine the primary terms used when describing an AI system or solution, including the purpose or reason that the AI solution is being developed, as well as other key terms, such as the types of data used and gathered from the solution.
Use AI right
The information language barrier can exist locally or systemically, regardless of program scope or organizational maturity. Addressing it requires a mindset shift as well as deliberate acknowledgment and intervention to course correct. To make data literacy more explicit, you should develop a data literacy program.
- Identify fluent and native speakers who speak data naturally and effortlessly. Fluent speakers should be adept at describing contextualized use cases and outcomes, the analytical techniques applied to them, and the underlying data sources, entities and key attributes involved.
- Identify skilled translators. Classic translators are often enterprise data or information architects, data scientists, information stewards or related program managers.
- Identify areas where communication barriers are inhibiting the effectiveness of data and analytics initiatives. Pay particular attention to business-IT gaps, data-analytics gaps and veteran-rookie gaps.
- Actively listen for business outcomes not clearly articulated in terms of explicit action. What business moments are being enabled with enhanced data and analytics capabilities? What operational decisions are being improved?
- Identify key stakeholders requiring specialized translations. To assess data literacy levels, ask key stakeholders to articulate the value of data as a strategic asset in terms of business outcomes, including enhanced business moments, monetization and risk mitigation.
- Identify and maintain a list of words and phrases. Engage the data and analytics team in crafting ways to better articulate these phrases.
Keep AI right
Not even the most successful companies can afford to think they are immune to ethical mishaps. Extensive and explicit discussion is needed to distinguish between the types of ethical questions and dilemmas one can face versus the actual ethical position one can take.
- Take a step back and absorb digital ethics and digital connectivism as a philosophy for the improvement of digital business — and digital society more generally.
- Actively look for ethical case studies relating to the use of data in AI, as the ethical questions that confront you are often not new.
- Opportunities include competitive differentiation and a superior value proposition; dangers include reputational risk, regulatory issues and financial losses.
- Use AI algorithms and data exchange as an enabler of digital interactions, and a way to enable stakeholders to participate in an ecosystem rather than as specific process controls. Encourage everyone contributing their data within the AI environment to be active participants in a mutually beneficial ecosystem.