The Era of Artificial Intelligence and Machine Learning
Artificial Intelligence(AI) can be interpreted as intelligence performed by artificial beings like man-made machines. AI is a procedure for making an artificial machine to think how smart humans think. So, AI is ultimately an imitation of human intelligence. By simulating human intelligence Artificial Intelligence enables computer applications to learn from experience through repetitive processing and algorithmic training.
AI networks become more and more efficient with each successful round of data processing since each interaction allows the network to test and measure solutions and develop expertise in the task it’s been set to accomplish. Artificial Intelligence networks and systems can become experts far faster than humans, making them incredibly beneficial options for any process expecting intelligent decision-making.
When it comes to Machine Learning (ML), it can be described as a type of Artificial Intelligence that permits software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
How do Artificial Intelligence and Machine Learning affect entrepreneurship?
Artificial intelligence in business:
The existence of Artificial Intelligence and its impact on our life and surrounding is inevitable in the present scenario. Everyone is knowingly or not making benefit of its presence. You have likely used it on your daily commute, searching the web or checking your latest social media feed.
Whether you're aware of it or not, AI has a massive influence on your life, as well as your business. Here are some of the major examples of Artificial intelligence in business management.
Applications of AI in business management include:
Business enterprises are majorly utilizing the power of AI to
● enhance customer service
● maximize sales
● sharpen cybersecurity
● optimize supply chains
● free up workers from mundane tasks
● improve existing products
● and point the way to new products.
During the pandemic outbreak, AI has played an important role in the global effort to contain the spread, detect hotspots, improve patient care, identify therapies and develop vaccines. As businesses have emerged from the pandemic, investment in AI-enabled hardware and software robotics is expected to surge as companies strive to build resilience against other disasters.
The realm of artificial intelligence is altering rapidly because of the enormous amount of AI research being done. The world's biggest companies, research institutions and governments around the globe are supporting major research initiatives on AI.
Building a profitable AI method
Discovery is the first and foremost step in building an AI strategy that delivers value. Gathering information about your organization that will guide strategic decisions is the essence of this step. It can be the most exciting phase, it will also set a strong foundation for everything to follow. Discovery efforts fall into two categories: organizational discovery and use-case discovery.
Organizational discovery uncovers the details that make your organization unique. What are the most important business priorities for the upcoming year and beyond? Knowing business priorities will help select use cases. How large is your IT department and how deep are your technical capabilities? Selecting technologies will depend on this information. And how will organizations develop and support AI solutions? It is essential to understand whether solutions will be owned by central IT or within business units.
Use-case discovery is where the discovery process gets exciting because you’ll be laying down ideas for potential AI solutions. Much of the information will be gathered through interviews with business units. Through this process, you should identify the most important problems that can be solved through AI. Data scientists can then identify solutions that can be solved with AI.
Create a reference architecture
A reference architecture consists of multiple diagrams and documents that provide different views of your system. Having multiple views helps audiences from various backgrounds understand the architecture – sort of like zooming in and out on a map.
Having a clear reference architecture will assist in selecting technologies in the next phase. It will also make sure you don’t miss any steps in the MLOps cycle that would prevent you from creating complete solutions.
Identify partners and vendors
The AI and ML space is increasingly packed and highly segmented. There are fascinating and wonderful products coming on the market every day, but not every one of these is right for your organization. You’ll want to make sure to identify vendors that truly compliment your organization’s strengths and weaknesses. The reference architecture will be a guiding light in this phase. For each element in your reference architecture, you’ll want to create a list of potential tools and vendors.
Evaluate personnel and industrial changes.
An organization can't be completely prepared to pursue its first AI initiatives. Every organization will likely have gaps in certain domains and skills. For instance, some organizations have great software and IT departments but are weak in terms of statistical modeling and machine learning. Other organizations are great from a scientific and R&D perspective but lack the engineering skills or operational expertise to fully deploy solutions. Hiring and training can take a long time. Developing new competencies can also be a distraction from the things you do best. If you are going to develop new competencies, is your leadership fully on board with the level of investment? Alternatively, you may wish to outsource certain steps in the process. Perhaps it makes sense to tackle R&D within your organization to own your intellectual property but outsource the deployment and operations to an external firm. Organizations with strong engineering and IT teams could of course seek to do exactly the opposite!
Build a roadmap
The next step is to build a roadmap that prioritizes quick wins to demonstrate business value and justify investments, both current and future. Build your roadmap by making the following prioritization decisions.
● Select an initial project from use-case discovery, make sure that this project is small- medium sized to create a quick win, and aligned with business initiatives.
● Select which components of your reference architecture are necessary for this first project. Prioritize tools and vendors based on how quickly your project will depend on them.
Present the strategy
Establishing an AI strategy is a very rewarding process, but of course, it’s just the beginning. Presenting your plan to leadership will make sure that everyone is aligned with your strategy, and also help prevent you from taking wrong turns and wasting investment.
Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. AI can be used to help the decision-making process. AI is highly valuable throughout many industries – whether it’s simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs. So, it is very evident that the future of the corporate world is inseparable from AI.