AI & ML
We all interact with AI products, for example, through an in-home voice-activated personal assistant or receiving a product recommendation on your favorite e-commerce website. It is abundantly clear that AI is omnipresent, driving innovation and influencing how businesses run and compete. AI has been known for nearly six decades. However, AI was thrust to the forefront of the new industrial revolution only recently. AI is the dominant driving force for modernizing a seemingly never-ending list of industries and functions such as communications, healthcare, media, education, audit, taxation, banking, insurance, and operations.
In short, the current and future use of AI in innovation can change how people live, work, play, and even think. Below are a few examples of what we can accomplish using AI today and could be of use to your use cases.
- Data is filtered to determine what should be included in the content produced at the end of the process. This stage includes identifying the main topics in the source document and the relationships between them.
- The data is interpreted, patterns are identified and it’s put into context. Machine learning is often used at this stage.
- A document plan is created and a narrative structure chosen based on the type of data being interpreted.
- Relevant sentences or parts of sentences are combined in ways that accurately summarize the topic.
- Grammatical rules are applied to generate natural-sounding text. The program deduces the syntactical structure of the sentence. It then uses this information to rewrite the sentence in a grammatically correct manner.
- The final output is generated based on a template or format the user or programmer has elected.
Natural Language Understanding and Generation.
We can process natural language and can read and comprehend what the intent is in documents, news articles, blogs, books, and emails. Furthermore, can generate summary information from processing the text and label the text with topics; convert Text-to-Speech (TTS) & Speech-to-Text (STT) and a conversationalist approach to complete NLG (Natural Language Generation) process highlighted:
Advanced Inference Engines.
Digital Twin – A virtual representation of a system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.
ECHO – virtual replicas of physical devices – assets, systems, or processes – that data scientists and IT personnel can use to run simulations.
Predictive Analytics
Leverage structured, semi-structured and unstructured data to predict future events – e.g. pricing & optimization, fraud risk, customer attrition, etc.
Forecasting
Drive superior operations and planning through demand forecasting, cost planning, granular sales, shipment forecasting etc.
Optimization
Achieve optimal throughput in each decision framework – e.g., maximize marketing reach, resource optimization, inventory management.
Miners
Web-scraping & targeted data mining from images, documents, or different formatted documents.
All the above technology driven outputs would be futile if the organization is not in-line with Vision of the Future and this is where we excel to help transform into an AI Powered Organization.
Today it is no longer a question of whether AI-fueled innovation can help drive business value; but how an organization can incorporate change into business operations to drive value, still is.
Common questions leaders frequently ask:
- Where do I start
- What do I do
- How much will it cost and
- How long will it take
The transformation journey can start with assessing and strengthening the internal capabilities described below:
– Strategy and Leadership.
Although cutting-edge technology and talent are needed to drive AI transformation, it is equally if not more important for leadership to align the organization’s culture and strategy to support AI. Alignment of corporate strategy with AI strategy and definition of measurable goals and objectives are necessary to prevent disjointed programs
– Talent.
It is essential to identify how the talent is recruited, developed, and retained and if a skill competency model exists. Enterprises should be capable of adapting and aligning to the new realm that is tying talent operating models to focus on reactive as well proactive recruiting and hiring.
– Ethics and Governance.
Ethics and governance capabilities are needed to define transparency, explicability, appropriate use of data sources, fairness appraisal, and compliance with regulatory and legal requirements. Technical processes for testing the behavior of algorithms through a quality assurance process play a critical role here.
– Technology Infrastructure.
A solid technical foundation is a critical component for AI transformation and contributes to developing a supporting ecosystem.
– Data
Data is the seed across all enterprises that allow for AI to scale, and hence, data must be accurate to the extent possible and unbiased to train systems continuously. The more data available, the more the system’s learning can advance. Although data availability and quality may seem like a straightforward concept across organizations, even organizations within the same industry still have different maturity levels.
– Organizational Structure
A one-size-fits-all organizational structure will not work because of social and technical variations. Instead, AI adoption and transformation can be driven and governed in several ways – from a centralized center of excellence where C-level executives lead a central group to a decentralized one where the structure is entirely independent of various business units.
– Science.
The maturity of the data drives better outcomes for an enterprise, but it is the methods and the science applied to the data that help draw meaningful insights and make intelligent decisions. This assessment identifies the use of mathematical modeling techniques and the maturity of the methods and models.
– Decisions, Feedback, and Learning.
AI-driven transformation can deliver astonishing results only if AI informs decisions. Suppose the relationships between insights and data are nonlinear, complex, and stochastic. In that case, executives may hesitate to make decisions, so it is essential to ensure appropriate decision-making mechanisms exist. Enterprises have only recently started establishing the necessary infrastructure to collect feedback and incorporate learning mechanisms. It is necessary to include multiple measurements to establish causality and identify the best strategies.