1. Artificial Intelligence
Competitive Edge for Business
Solutions & Applications

2. The growth of Artificial Intelligence in recent years brought forth a major
challenge for brands in deploying such AI solutions. Many brands lack the
clarity regarding where to start the AI integration process and profitably
deploy these solutions in the most effective manner.

3. A PwC report estimates that AI is capable of contributing almost $15.7
trillion to the world economy by 2030. Today, every business is aware of
the AI’s operational benefits in the modern technological territory ruled by
data mining. However, such a position also poses a major challenge in
front of human operators: which tasks are to be assigned to software and
which are to be kept under their watch.

4. Implementing AI
Solutions
Systematically to
Solve Real
Problems

5. The first step to develop intelligent platforms is the implementation of AI in
the application layer. Database operations can assist in enabling
AI-driven APIs to process the NLP, image patterns and speech/text. As AI
applications become diverse, a systematic way to solve real-time
challenges becomes significant to make way for a centralized paradigm
shift:

6. Utilizing Existing
Platform or
Building Own

7. It is highly probable that AI will change the technology game by
enhancing the efficiencies of business processes. However, businesses
are still unsure about AI’s potential and are in constant confusion how it
can bring efficiency in their processes. Hence, the biggest question is –
whether to implement an existing application or build an own. Utilizing an
existing platform/application built by an AI development company is
definitely going to save costs, time and maintenance. Moreover, they
enable an easy entry to the market with lower barriers. Many app
development companies provide AI and Machine Learning services for
developing real-time solutions. It also makes an organization free from all
the risks associated with data security and validation. Some of the most
popular AI applications are TensorFlow by Google, Fast Text by
Facebook, Keras which is also written entirely in Python.

8. On the other hand, businesses can also build an AI application of their
own if they are dealing with a very niche product. Since niche products
require complex processes, many standard AI platform vendors don’t
usually include them. Plus, such organizations might already have
invested in data intelligence resources. If they want to build their own
application, many cloud platforms also offer ways to build an AI
application with all the required tools and modules.

9. Beginning Small
with Lots of Smart
Data

10. Implementing AI in the database and operations, should be a planned
process instead of just jumping onto the bandwagon. Whether it’s a small,
medium or large scale business, massive and immediate investment can
prove to be detrimental. They can begin by first-party app integration to
increase employee productivity. Afterwards, they can move towards AI
systems that are open-source, and provide more flexibility in the
workflow. This way, they can keep concentrating on a smaller goal and
yet gain a positive Return on Investment.

11. Smart data form the backbone consisting of the right quality and type of
clean and structured first-party and third-party data leading to smart
decisions. Businesses can gain precision and a comprehensive view of
the data, along with constant and real-time upgrades. Smart data is a
great source of obtaining dynamic signals and classification for
forecasting demand and supply operations. Cleaning the input data is
another important process that involves entry-point protection,
verification, maintaining and refreshing data, flagging irregularities, and
then connecting the cleaned data across various systems.

12. Injecting
Transparency and
Implementing
Analysis

13. Gartner’s 2018 survey highlights that out of the companies surveyed, only
4% have invested and implemented AI solutions. Implementing AI
includes everything from applications, production, investments, an
AI-driven culture, work environment and management. It would help in
creating an organized two-way network between Machine Learning with
human supervision. It would eventually assist in gaining better insights
regarding producing the right output from the machine. The supervision
can ensure that humans are capable of enhancing or restricting the
output algorithms.

14. AI analysis is another vital process that includes predictive analysis for
broadening the scope of a business. It’s excellent for the businesses not
looking to invest heavily on ML. Many analytics software offer business
intelligence solutions like H20, Microsoft Onboard, and Amazon Machine
Learning. AI cloud is a great option for any business to invest. They help
in saving costs, maintaining infrastructure, and are easily scalable.
Businesses can avail direct sources of AI and ML services without
worrying about the right selection of algorithms and models. Plus, they
provide exposure to voice and text bot services, enabling businesses to
develop digital assistants with IoT.

15. Strategizing,
Experimenting and
Integrating

16. The success of AI implementation in business processes depends highly
on how well the applications are aligned with the business goals. Hence,
strategies should also be created by defining the purpose and prioritizing
them. Investment from all sectors almost tripled two years back in 2016
showing a jump to $39 billion from $26 billion. Since the current AI
technologies like neural network ML and NLP are proving their mettle, it’s
a good time for brands to experiment. Using AI in scaling businesses and
core operations results in increasing revenue, gaining market share and
revolutionizing their products.

17. Another crucial parameter to consider is assigning teams for handling AI
initiatives. Both technical and business teams must be assigned to
supervise such processes for launching compelling new technologies. A
systematic approach to utilize the tools from a spectrum that can either
solve business problems or have higher potential; can help in building a
growth path that is robust and proactive.

18. Final Thoughts

19. AI deployment in production processes has its own risks for both small
and large scale businesses. It needs proper planning and research to
build an AI application from scratch. Plus, there are further risks to
maintain and support it, if the algorithms break. New codes are also full of
many bugs and errors when they are implemented and deployed.
However, current scenario has many AI platforms and cloud applications
that offer access to third-party tools and user-friendly applications to
businesses. Companies offering next-generation solutions enable a
smoother workflow apart from making businesses capable of meeting an
increasing number of data intelligence challenges.

20. 9series
Leading Website & App Design Company
www.9series.com
Sales: +1 (425) 504 6109 | Email: sales@9series.com