- Leaders from large- to small-scale organizations
- Bright minds with a clear AI strategy
- Business heads, who are mentally and financially prepared for practical AI
Do you have the answer to:
- Why AI is more relevant than ever before?
- Which aspects of AI are feasible for my business?
- How does my business get eligible for AI?
Then it is the right time to pay attention to collective intelligence (CI) for productivity improvement and keeping up your market. AI is not a traditional computer software where you download the executable file and then start using like a preconfigured software program, such as, say, Microsoft Office.
AI needs special attention before and after adaptation. AI is a programmed application without the configured instructions. An ideal comparison would be your new employee on their first day. To deliver the desired results, first, they need to learn, understand, and practice the process, technology, information, and people.
Let’s quickly understand what AI is before proceeding further. AI is nothing but a computer program which can simulate human actions without preconfigured steps/instructions.
Common AI subsets are:
- Natural Language Processing
- Speech Recognition
- Statistical Learning
- Computer Vision
- Augmented Reality
- Virtual Reality
- Object Recognition
- Image Processing
- Machine Learning
- Pattern Recognition
- Deep Learning
- Neural Nets
There are many advisory firms, consultants, and online portals which can make you understand and preselect one or more AI methodologies for your business problems such as products, application programming interfaces (APIs), platforms, as a hybrid, or as subsets like robotics, machine learning, deep learning, and natural language processing. The game is not over at this point, the challenge arises when people start using and adapting current-gen technology such as augmented reality, virtual reality, and speech recognition.
The reality is, we do not have any standard practices to teach us how to adapt/modify business IT functions for AI, machine learning, and statistical learning, because every organization operates under its own structure and ethics. The diagram below shows how the biggest tech companies are organized and operate, how their decision controls are different, but they are still successful.
Ethics changes with technology — The ethics are equally important and mostly structured for people. For example, say an organization allocates two hours for people to read and process a 100-page document. Logically, this SLA is ideal for humans but not for AI. There are also places where humans can do better than AI like differentiating between a cat and a dog in a picture.
Organizations will fail fast in their AI transformation if they implement aspects such as augmented reality, statistical learning, and robotics; to name just a few; with their traditional ethics, process, and data movements. In general, there are several touchpoints, like degree of control, target values, fairness, and accuracy, among others, to take care of. However, we need to see how collective intelligence, that combines all subsets of AI such as machine learning, augmented reality, deep learning, robotics, natural language processing, speech recognition, and virtual reality, can be a lifesaver.
According to MIT, “Collective Intelligence is a group of individuals acting collectively in ways that seem intelligent”. Their favorite references are how Google and Wikipedia collectively interconnect several websites/data sources to deliver accurate results for user queries in a fraction of a second.
As the technology evolved from “1-Tier to 2-Tier”, then “2-Tier to 3-Tier”, and is now running Serverless Architecture, every organization has CI to some extent through mutually interconnecting,
- “Humans to Humans”
- “Computers to Computers”
- “Humans to Computers”
AI is not a human, but it can simulate human actions. Due to its nature, AI needs more test data to train models with appropriate training algorithms. The traditional level of CI will not be enough to fulfill business needs.
To enable the right degree of CI and “AI-ready” data, the ethics, operational/system changes, and acceptance of AI should apply in all levels, starting from the leadership to team members, and then extending to end customers and clients. The example below explains how people, process, and technology are interconnected intelligently to make AI successful.
- This organization has 10 Processes (P1 to P10)
- People and software are used in process executions
- People alone run some processes without the software
- AI is deployed in P4 and P5
- Even though AI is used by P4 and P5, the entire organization’s people and software are connected logically and intelligently for successful AI implementation
As every organization is built with their own people, process, technology, and data, there is no right or wrong way to enable CI for AI, but to get the CI straight for your business, your business needs to:
- Hire or Identify the right minds
- Qualify the practical AI product/API/platform for your business (or) bring a good AI product consultant/team
- Ensure collaboration between your engineering and AI teams
- Identify and provide them the necessary hardware and software/tools
- Make sure enough data training is achieved
The above activities are not like traditional software development activities and phases, because usually the people, processes, and technology are not mature for AI, so it will consume extra time. In this case:
- Keep your long-term vision alive
- Pay attention to your AI project planning and release schedules
- Collect continuous feedback from business strategy advisors and enterprise architects to foresee your business with AI
- Do not hesitate to refactor your ethics as and when necessary
Their primary goals can be:
- Identify the ideal process for AI
- Bring the right AI product(s) or AI models and algorithms
- Find the logical touchpoints to enable CI (in both processes as well as technology)
- Confirm all the necessary data and make it “AI-ready” for training and testing.
Apart from the above responsibilities, there is a smart way to achieve successful AI implementation. You can look for some ready to deploy, AI-enabled products for intelligent automation and enterprise-wide smart agents. These can eliminate the manual efforts related to building AI models, creating algorithms, and data training. Learn more about how we have leveraged AI-based products to drive automation in global enterprises across industries here.