Artificial intelligence has become a common instrument in the success stories of many industries. Although the world has only seen the negative side of AI or technology in movies/series such as Eagle Eye, Terminator, and Black Mirror, the said domain is not focused on ending the human species.
We at Rapidops, strongly feel that AI (artificial intelligence) will not just make things easier for us but will also help us get into an advanced age that promises better growth and prosperity.
The state of AI (artificial intelligence)
Before expanding the topic and breaking down the benefits of inculcating the same into your organization, we would like to get some things straight first.
Let’s begin by gauging the growth and future promises AI (artificial intelligence) holds.
AI is a growing domain, and it’s doing so at a great pace. To keep track of this growing domain, you not only require paying close attention but look at it from various perspectives.
To lay a strong AI (artificial intelligence) foundation, you require certain command and discipline, that many companies do not yet understand. And they are not to be blamed. Such a discipline requires strategic collaboration, having key metrics in place and the necessary talent for working with AI.
Let’s see how you can implement AI in your organization.
- Identify your goals
- Find out about the underlying drivers
- Will AI make your organization more competitive?
- Is it going to deliver better business value?
We are mentioning a few steps that every organization who wants to include AI in their process must follow.
1. Restructure the data foundation
Big Data is said to be the founding father of AI. Yet the approach towards both is way opposite. If you are looking for answers in AI, you must have a strategy in place for the foundation of data itself.
Be sure that this foundation has the computing, storage and analytical capabilities. What’s more important is a shift in perspective.
If you have been using data to track and measure how your business functions perform, shun them. Your current data foundation must learn how to perform these functions with this data.
For instance, UBER!
What this ride-hailing company does with its collected data is fascinating.
Everything Uber does revolves around its data.
The data foundation processes around trillions of Apache Kafka messages, per day!
Hundreds of petabytes of data get stored across multiple data centres. The sole motive of doing so is to support millions of weekly analytical queries.
For deriving autonomous decisions, Uber uses their in-house system Michelangelo. This system discovers and manages metadata, and ontology. This process is necessary for deriving data-driven performance.
This entire process lets them know where the users want to go at a given point of time and matches them with the near-by drivers.
Apart from the new-born data-driven startups, established businesses have also realized the use of data for performing better in this competitive and advancing market.
Let’s take a look at farm-equipment maker John Deere.
Being a 182-year-old company, they created an open platform for small agricultural start-ups that allows small-sized businesses to leverage the power of data analysis.
Artificial intelligence has made its way into business architecture. Inculcating data strategy in your business process is a good idea for you. Creating an AI and ML (machine learning) backbone supported by a solid data-analytics foundation will help you scale your business.
2. Creating a collaboration that bridges business function with IT
Not every business is going this way, as they still think it is an expensive path they are not prepared for.
This is evident in enterprises that are still directing their data and analytical reporting to IT teams. IT drives data and analytics modernization within its own smaller spheres. Analytics teams, on the other hand, focus on the individual functions.
Having the two departments working individually and looking for answers outside the businesses’ architecture, enterprises are doomed to experience operational inefficiency.
We suggest that business owners must look forward to an integrated data foundation so that they can easily adopt enterprise-wide AI adoption.
3. Regular examination of data quality for measuring success
Is your data ready to support the organizational goals and desired business outcomes?
If the organizational goal revolves around generating an AI-driven recommendation for helping users decide when is the right time to invest a sum of their earnings in the stock market, then the data for training and testing the AI system must be of high quality. In addition it must also be highly correlated to the outcomes without any system errors.
4. Assign the correct talent pool to your AI projects
You must hire a talent pool that has better business knowledge. The design-thinking and outcome-driven approaches are necessary for successful data and AI implementation.
You want a successful implementation of your AI programs, then find out how they are going to impact the business. You can bring in the head of sales or marketing as the CIO (chief information officer)/CDO (chief development officer)/CAO (chief administrative officer).
And, you can always hire a CTO (chief technology officer) to work beside the CDO/CAO. They are needed for assisting and helping them make the right technology choices.
How you choose to run the AI programs and where the technological investments, and budgets must be assigned is depended on the CTO.
What does the future hold?
For laying a responsible AI foundation in their business stream, businesses need technology which will complement the entire architecture. Not to forget they will need to focus on the governance driven by ethics and trust.
Doing so will help their efforts of creating the architecture, looking for the talent pool and allocation of the resources. We mean to say that unless you embrace machine intelligence with an ethical and responsible dimension of AI, all the above efforts will go in vain.
“highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.” – brookings.edu
Many executives surveyed via a research said their companies do have policies and procedures for identifying and addressing all the ethical considerations once the system gets launched.
We strongly believe that modern organizations must focus the ethics upfront in your business strategy and decide whether a particular problem needs to be solved through an AI.
Modern business executives underestimate the challenging ethical questions when the emergence of AI becomes sophisticated in its use.
Businesses must act on several fronts if they want to gain benefits from application of AI.
We are sharing some of the items that businesses can use for applying AI in your business strategy.
1. Formulating an ethical for the AI strategies
As a business that is looking for a long-term growth make sure to focus your resources on opportunities that show measurable value like
- Reduced costs
- Increased revenue
- Improved customer service
- Enhanced employee experience
Your strategies must have a human-centric view of AI. This will help you create machine-learning work successfully alongside people and benefit your business.
2. Create a governance architecture
Business must act on enduring that AI decision-making is transparent. AI must stay free from any data bias and human error. You must personalize the AI so that it can provide tailored and relevant support to those who interact with it.
3. Create applications with responsible AI
As AI becomes a common phenomenon powered by advanced machine learning, the ethical concerns keep growing around it. Companies must develop AI applications by interweaving the ethical architecture.
Provide an oversight so that these AI systems operate ethically over time, learning and evolving with the help of machine-learning.
There are non-technical angles that play critical and complex roles.
- Human-centric approaches
These are crucial points to be considered for developing and running the technology itself and can be used for levering the same for achievement of the business objectives.
AI and ML have become part of the real business world now. If businesses cannot find a solution for these technologies to co-exist with their current business objectives, then their efforts of achieving success seem bleaker in the future.
How artificial intelligence is transforming the financial ecosystem?
Artificial intelligence is fundamentally changing the physics of financial services.
AI is rapidly becoming integral to FSI businesses and those that don’t update their infrastructure to support it risk being left behind.
The range of AI tools available to help financial businesses to update their operations is steadily growing. Take Intel® Saffron™ AI for example. It is an AI-based platform which is capable of simulating our (human beings) natural ability to learn, remember and reason. This capacity is based on associative memory reasoning technology.
With the Intel AI, finding hidden patterns in large datasets and transforming them into actionable and explainable information.
The benefits of AI in financial services are multiple and hard to ignore. As per a report from Forbes, 65% of senior financial management expects positive changes after implementation of AI in financial services.
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