Artificial intelligence (AI) technologies have the potential to help businesses improve upon their processes. But a lack of consensus on what AI is destined to achieve and a lack of understanding of where or how to start can leave some stalled at the start.
Our on-demand webinar features Vizuri founder Joe Dickman and former Harvard professor/author Mark Esposito. They help build a better understanding of AI and its foundations so you can set realistic goals, envision how AI fits into digital modernization, and create a strategy that makes modernization work for you.
Watch the webinar to see for yourself and read on for some of the highlights.
Highlight 1: AI is not a consistently understood term
Fears surrounding the role of AI in the workplace have put many under the impression that AI will one day replace people at their jobs. As a result, many misunderstand the risks associated with the implementation of AI technologies. It is important to first understand two subsets of AI: Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI).
AGI is self-aware and has free will. In fictional contexts, this usually causes lots of problems for humanity. AGI is generally seen as a substitute for humans and is even able to work and manage itself better than a real human employee. While this sounds like an interesting dream, this is not the reality.
ANI is the current reality of AI. It does not have free will and can only serve a pre-existing, well-defined purpose. As a result, its function is to support humans in their work and make various improvements to human work, not entirely replace it.
AI is not a substitute for human intelligence. It cannot provide for the other human elements that define and make valuable human contributions.
Overall, the risks of using AI technologies lie in mismanagement—just like using any other technology.
Learn more about ANI and how it augments human work in the full webinar.
Highlight 2: Understanding the types of AI in play today
AI is the first of three successive technologies: AI, machine learning, and deep learning. Furthermore, AI can be broken down into symbolic and non-symbolic types.
- Symbolic AI is inclusive of rules engines and systems that manage entrenched knowledge.
- Non-symbolic AI uses programmed knowledge to recognize patterns and make predictions and is what we know as machine learning.
- Deep learning takes this concept a step further with complex neural networks that learn from immense amounts of data.
Symbolic AI provides many advantages for certain industries such as healthcare, insurance, and finance. It uses a human-readable language and can be audited—when it makes a decision, that decision can be clearly tracked back to a business rule or some other knowledge that the system was given.
Non-symbolic AI can be powerful but is difficult to audit because such systems can be too mathematically abstract to understand. As a result, these systems can be difficult to predict and are less ideal when it comes to managing business processes.
Highlight 3: Knowing if and when to start implementing AI
Many companies struggle to find out what they need in terms of digital transformation and falter when it comes to implementing AI to augment their business. But for AI, it comes down to the availability of data. Data is needed to connect technologies with each other (Internet of Things) and for AI to make accurate predictions and models. Data feeds into an organization’s ability to quickly modernize and transform.
Beyond data, companies must identify narrowly defined, predictive questions they are able to answer using artificial intelligence. This allows them to transform and adopt new technologies toward business goals, rather than doing so as a step toward simply not being outdated.
Highlight 4: Foundations for transformation
To harness the data and knowledge needed by AI systems to improve business processes, modern software architectures must be established that enable data flow and effective knowledge management.
Five main technologies form the core of modern software architectures:
- API Gateway: Enables intersystem communications and data transfer
- Business Process Management: Defines immutable workflows for intersystem processes
- Business Rules Management: Isolates knowledge into manageable domain models that separate decision logic from underlying technologies
- Microservices and Containers: Building blocks for scalable infrastructure that follows industry best practice for modern architectures
- Container Orchestration: Automates deployment of multi-container-based applications at scale
Note: Businesses should avoid adapting existing systems since this just increases technical debt. If businesses allow legacy systems and old ways of doing business heavily influence future processes, they will lose market share to new entrants that have the luxury of coming in with totally new processes that make them more agile than incumbents that are weighed down by aging systems.
Check out the full webinar for more details on integrating modern software architectures.
Effective modernization not only staves off stagnation, but also helps businesses face several associated challenges:
- Evolving consumer expectations and preferences
- Emergence of increasingly complex business decisions
- Need for faster time-to-market
- Burden caused by manual, time-consuming tasks
- Need for greater consistency (internal business processes and external consumer experiences)
- Need for more accurate risk mitigation techniques that balance knowledge and experience
- Unpredictable workflow management and lack of performance metrics
Highlight 5: Knowledge management as the foundation for AI
Another common key challenge is the presence of a communication gap between the business and IT sides of the organization. They generally lack a shared vocabulary and shared objectives. As a result, overarching goals don’t always get reflected in technology solutions and the business may not get what they expect. This fosters distrust between the business and IT sides of an organization, which can further exacerbate the issue.
Knowledge management must be employed to help resolve this. Knowledge management works to centralize all the entrenched knowledge at an organization. A knowledge engineer thus serves as the bridge between IT and business, making knowledge a consumable asset for developers.
Not only does a solid knowledge management discipline help solve some common business challenges, it also serves as the foundation for artificial intelligence. As we discussed earlier, symbolic AI is based off business rules and knowledge that informs decisions. Knowledge management is the first step to gathering this knowledge and building a “single source of truth” that fuels AI systems.
Watch the webinar for more insights on AI as part of a modernization strategy
These were some of the highlights from our on-demand webinar, “The Future of AI, Microservices, and Decision Management.” For all of the valuable takeaways, check out the video and let us know any questions.