Hour of need for Artificial Intelligence and Data Analytics
How far can AI and Data Analytics expand the horizon of intelligence and automation? In our opinion, as much data, we can collect. It is no secret that AI is the thing that is disrupting any sectors we know. AI is refining processes, recommending new services, redefining markets. And with all these, AI is getting smarter and faster.
But within some years, we will not be able to differentiate interactions with a human and AI. Chatbots will be incredibly smart, with supervised learning using superior natural language processing and generations. They will understand the context of our interactions and engage us in precisely how a human would. AI will recognize us and our behavior through computer vision and can recommend actions by analyzing our intent.
Suddenly, AI-powered robots have surpassed the silver screen space and entered our lives literally. At first, robots involved themselves in repetitive tasks, but by 2025, there will be trained machines taking over requisite tasks. Software robots are already filling our forms, placing our orders, and taking care of our work schedules in the background.
The ubiquitousness of Data
Since data is everywhere, corporate powers are doing everything to get their hand on multiple sources of information. To develop an infallible AI, all it needs is accurate and reliable information. The interconnectedness of devices will work better to create a perfect ecosystem of user journeys in every sphere of life. But to achieve ubiquity, we need ubiquitous data.
With more devices and apps, will come continuous cyber intrusions. Advanced predictive algorithms will play a smart shield against incessant attacks made against these digital products. It can detect nefarious acts identifying patterns and signatures within ongoing transactions.
AI complementing humans
AI will not supersede humans. First, it's a new opportunity to reskill the workforce and utilize its capabilities to strategize and streamline operations using data insights. It will be human intervention to understand the context and quality of AI analysis, and will take a final call to deploy decisions based on AI recommendations.
AI-powered devices are rising
AI-powered tools and hardware will embed themselves in our vehicles, household appliances, portable devices, and workplace infrastructure. We will see augmented reality displays at most of our living spheres and will advance towards making them interactive by 2030.
Computational AI-powered Processors
Before quantum computing will be at scale, custom processors are under design to carry out real-time analytics. These processors will be AI-powered, increasing the load and computational capacity of large scale cloud computing infrastructures and providing the power of edge-computing.
Automated Deep Learning
Learning which is not supervised or structured, through unstructured or unpolished data, will be the trend in building perfect AI algorithms. Furthermore, using these algorithms, automation of developing and managing machine learning micromodels will help in automated operation optimization, recommendations, and real-time personalization of various functions.
Augmented analytics and data management
Automated Deep learning techniques will enhance business optimization through real-time decision making. Augmented data analytics will produce insights in real-time through real-time data. Data management will be boosted by deep automated learning, which will scrub data through multiple data sources, organize them in a structured manner for use in augmented data analytics platforms.
The semantic graph at rescue
Semantics graph will empower data-driven enterprises to curb the complexity of data structures and governance of usage. This tool will organize and connect data to AI engines in a fashion that will be optimized and structured, despite the data variations in structure and levels.
Embedded Analytics for real-time insights
To move operations at a faster pace for any functions, facilitating tools will have embedded analytics at every point so that workers won't have to switch applications to look for insights on a particular operation or process. While executing the required function, embedded analytics will simultaneously display insights and recommended actions.
Multiple Data sources to perfect AI
A data-driven enterprise is genuinely one when they create a data backbone, that incorporates and siphons in all data from all the bases in an enterprise and create a shared data fabric for AI to perfect its skills, and utilize the data fabric to shape data for usage in various AI models within an organization
Future of work is data-laden and driven. It's time to invest and upskill workforces in various sectors to understand and work using data to get accustomed to data-driven decision making.
Growth of Mobile Device Intelligence
Voice assistance, augmented reality, and various other technologies are making an increasing foray into wearables and mobile devices. Most of the applications that function using these technologies will need intelligence through real-time data analysis. Increasingly, thus wearable and portable device intelligence systems are seeing investment to perfection.
Digital Experience Design to be intuitive
An user journey within a device or in a physical space, proliferated by device ecosystem, will adapt and change according to the user's behavior. User experience will be mostly intuitive by learning through real-time AI about user intent and context, and changing their experience journey based on the AI computed outcomes.
Commercialization of Machine Learning
Unprecedented development of algorithms using numerous open data sources has been possible due to open-sourced platforms. Various application vendors will start making API integrate with these open platforms to use parts of algorithms and data, or integrate these platforms to scale.
Governance of AI through transparency
We have seen numerous regulatory policies implemented to control the use of data and AI. To make it easy for regulatory boards to understand the intent of AI, companies have started making AI transparent and explaining their structure, model, input sources, and function.
Neural Networks and Data Accuracy
Automated AI is achievable through advances in generative adversarial networks (GAN). The automation happens as these advanced neural networks can synthesize their training data. Automated structuring of data is the key. But for that preliminary primary data is needed from various sources, and much more accurate the information is, the better is the quality. To cross-reference the data quality, some firms are developing shazaming like tools that could identify sources of data during their pre-synthesis processing. It can also identify and weed out deep fakes from multimedia platforms.
AI and Data Science Ecosystem Consolidation
In June '19, Salesforce acquired Tableau, and Google brought in Looker. Smaller firms like DataRobot acquired three companies (ParallelM, Cursor, and Paxata), and Appen acquired Figure Eight. Whereas simulation software giant Altair acquired Datawatch Corp and SymphonyAI acquired Ayasdi. Most of these acquisitions have happened to keep in mind the trends of AI capabilities and data analytics advancement. The disruption in business services and offerings is where these organizations want to be among the first movers.
If AI is the engine, data is the fuel to propel it forward
The organization's AI strategy is successful when the foundation of AI's structural components is strong. One of those components is insights from data analytics. If organizations are interested in building an AI-driven organization, they should start with identifying all possible data sources within and outside the organization. The leadership should create a blueprint around data architecture, integrate multiple data sources, and create a data backbone that can facilitate proper data management. They should initiate their AI journey by propelling data analytics as a first step.
AI is the accelerator of modern digital transformation
Artificial intelligence is now the most reliable form of digital transformation. Its transformation power spans from business operations to the customer experience. The latest AI tools are available in their best stage for firms to leverage for various purposes and at various processes in your business operations, enabling successful digital transformation. AI can impact how you interact with your customers. It can reframe your business model into a more innovative and adaptive best. It directly affects the customer base and revenue to grow. Using data-driven AI tools, organizations can deliver business value throughout their customer cycle.
Future of AI
We are expecting to see the trends discussed, taking shape very soon. There will be innovation in methodologies and techniques to achieve the perfection and accuracy in AI tools. Trends like Automated Deep Learning to generate its data, Augmented Analytics embedded everywhere, Graph methodology to refine data analytics are some of the new development areas, most of the firms will be interested in building their capabilities around.
Think through the steps of implementing AI
Define and standardize goals that AI can achieve being a tool
Look into the AI capability portfolio along the line of appropriate use cases.
Create a data infrastructure integrated with multiple data sources, clean and structure complete data for use.
Strategize proper business rules and models to get contextual information.
Create analytics capability using the rules
Embed analytics in every part of your business operations and offerings/customer lifecycle
Establish AI capabilities at the top of everything, which is fed data analytics based outcomes to provide information that can directly imply into business outcomes
Two Cents by SA
Digital transformation accelerates through business focussed and data-intensive AI platforms. It should be a strategic priority for all organizations that want to challenge and lead ahead in any given market situation. If an enterprise wants to achieve AI-driven business goals sooner, they should consider investing in AI specializing firms and partner with them to start commercialized AI solutions and vertically driven consulting.
SA expects the continuation of leading tech mid-market firms to enhance their business process or increase service lines by tapping into the AI-as-a-service market. SA is adept in playing a much larger role to assist incumbents in acquiring the capabilities they need to leverage AI, and facilitate AI innovating firms in growing their innovation and product goals.
SA has seasoned experience and has been first movers in the space of Data Analytics & AI ecosystem partnerships, and strategic investment during the course of years for multiple large firms and boutique AI firms. SA has helped through accelerated inorganic growth of companies through AI and Data Analytics capabilities, by evaluating their business process and product growth map, and planning both collaborative and investment transactions at the right stage and time.
To know more about how we can help you to grow your business by utilizing AI and Data Analytics, write to us.