Industry Expert Talk at Alliance University
Office of Career Advancement and Networking at Alliance University had organized an Industry Expert Talk with Mr. Sameer Dhanrajani to discuss ‘Decoding Artificial Intelligence: The Sought After and Future Proof Career Opportunity’ on June 11, 2021 at 6:00 PM.
Data Driven Enterprise – Part II: Building an operative data ecosystems strategy
Ecosystems—interconnected sets of services in a single integrated experience—have emerged across a range of industries, from financial services to retail to healthcare. Ecosystems are not limited to a single sector; indeed, many transcend multiple sectors. For traditional incumbents, ecosystems can provide a golden opportunity to increase their influence and fend off potential disruption by faster-moving digital attackers. For example, banks are at risk of losing half of their margins to fintechs, but they have the opportunity to increase margins by a similar amount by orchestrating an ecosystem.
In my experience, many ecosystems focus on the provision of data: exchange, availability, and analysis. Incumbents seeking to excel in these areas must develop the proper data strategy, business model, and architecture.
What is a data ecosystem?
Simply put, a data ecosystem is a platform that combines data from numerous providers and builds value through the usage of processed data. A successful ecosystem balances two priorities:
Building economies of scale by attracting participants through lower barriers to entry. In addition, the ecosystem must generate clear customer benefits and dependencies beyond the core product to establish high exit barriers over the long term.Cultivating a collaboration network that motivates a large number of parties with similar interests (such as app developers) to join forces and pursue similar objectives. One of the key benefits of the ecosystem comes from the participation of multiple categories of players (such as app developers and app users).
What are the data-ecosystem archetypes?
As data ecosystems have evolved, five archetypes have emerged. They vary based on the model for data aggregation, the types of services offered, and the engagement methods of other participants in the ecosystem.
- Data utilities. By aggregating data sets, data utilities provide value-adding tools and services to other businesses. The category includes credit bureaus, consumer-insights firms, and insurance-claim platforms.
- Operations optimization and efficiency centers of excellence. This archetype vertically integrates data within the business and the wider value chain to achieve operational efficiencies. An example is an ecosystem that integrates data from entities across a supply chain to offer greater transparency and management capabilities.
- End-to-end cross-sectorial platforms. By integrating multiple partner activities and data, this archetype provides an end-to-end service to the customers or business through a single platform. Car reselling, testing platforms, and partnership networks with a shared loyalty program exemplify this archetype.
- Marketplace platforms. These platforms offer products and services as a conduit between suppliers and consumers or businesses. Amazon and Alibaba are leading examples.
- B2B infrastructure (platform as a business). This archetype builds a core infrastructure and tech platform on which other companies establish their ecosystem business. Examples of such businesses are data-management platforms and payment-infrastructure providers.
The ingredients for a successful data ecosystem : Data ecosystems have the potential to generate significant value. However, the entry barriers to establishing an ecosystem are typically high, so companies must understand the landscape and potential obstacles. Typically, the hardest pieces to figure out are finding the best business model to generate revenues for the orchestrator and ensuring participation.
If the market already has a large, established player, companies may find it difficult to stake out a position. To choose the right partners, executives need to pinpoint the value they can offer and then select collaborators who complement and support their strategic ambitions. Similarly, companies should look to create a unique value proposition and excellent customer experience to attract both end customers and other collaborators. Working with third parties often requires additional resources, such as negotiating teams supported by legal specialists to negotiate and structure the collaboration with potential partners. Ideally, partnerships should be mutually beneficial arrangements between the ecosystem leader and other participants.
As companies look to enable data pooling and the benefits it can generate, they must be aware of laws regarding competition. Companies that agree to share access to data, technology, and collection methods restrict access for other companies, which could raise anti-competition concerns. Executives must also ensure that they address privacy concerns, which can differ by geography.
Other capabilities and resources are needed to create and build an ecosystem. For example, to find and recruit specialists and tech talent, organizations must create career opportunities and a welcoming environment. Significant investments will also be needed to cover the costs of data-migration projects and ecosystem maintenance.
Ensuring ecosystem participants have access to data
Once a company selects its data-ecosystem archetype, executives should then focus on setting up the right infrastructure to supports its operation. An ecosystem can’t deliver on its promise to participants without ensuring access to data, and that critical element relies on the design of the data architecture. We have identified five questions that incumbents must resolve when setting up their data ecosystem.
How do we exchange data among partners in the ecosystem?
Industry experience shows that standard data-exchange mechanisms among partners, such as cookie handshakes, for example, can be effective. The data exchange typically follows three steps: establishing a secure connection, exchanging data through browsers and clients, and storing results centrally when necessary.
How do we manage identity and access?
Companies can pursue two strategies to select and implement an identity-management system. The more common approach is to centralize identity management through solutions such as Okta, OpenID, or Ping. An emerging approach is to decentralize and federate identity management—for example, by using blockchain ledger mechanisms.
How can we define data domains and storage?
Traditionally, an ecosystem orchestrator would centralize data within each domain. More recent trends in data-asset management favor an open data-mesh architecture . Data mesh challenges conventional centralization of data ownership within one party by using existing definitions and domain assets within each party based on each use case or product. Certain use cases may still require centralized domain definitions with central storage. In addition, global data-governance standards must be defined to ensure interoperability of data assets.
How do we manage access to non-local data assets, and how can we possibly consolidate?
Most use cases can be implemented with periodic data loads through application programming interfaces (APIs). This approach results in a majority of use cases having decentralized data storage. Pursuing this environment requires two enablers: a central API catalog that defines all APIs available to ensure consistency of approach, and strong group governance for data sharing.
How do we scale the ecosystem, given its heterogeneous and loosely coupled nature?
Enabling rapid and decentralized access to data or data outputs is the key to scaling the ecosystem. This objective can be achieved by having robust governance to ensure that all participants of the ecosystem do the following:
- Make their data assets discoverable, addressable, versioned, and trustworthy in terms of accuracy
- Use self-describing semantics and open standards for data exchange
- Support secure exchanges while allowing access at a granular level
The success of a data-ecosystem strategy depends on data availability and digitization, API readiness to enable integration, data privacy and compliance—for example, General Data Protection Regulation (GDPR)—and user access in a distributed setup. This range of attributes requires companies to design their data architecture to check all these boxes.
As incumbents consider establishing data ecosystems, we recommend they develop a road map that specifically addresses the common challenges. They should then look to define their architecture to ensure that the benefits to participants and themselves come to fruition. The good news is that the data-architecture requirements for ecosystems are not complex. The priority components are identity and access management, a minimum set of tools to manage data and analytics, and central data storage.Truly mentioning , Developing an operative data ecosystem strategy is far more difficult than getting the tech requirements right.
Data Driven Enterprise – Part I: Building an effective Data Strategy for competitive edge
Few Enterprises take full advantage of data generated outside their walls. A well-structured data strategy for using external data can provide a competitive edge. Many enterprises have made great strides in collecting and utilizing data from their own activities. So far, though, comparatively few have realized the full potential of linking internal data with data provided by third parties, vendors, or public data sources. Overlooking such external data is a missed opportunity. Organizations that stay abreast of the expanding external-data ecosystem and successfully integrate a broad spectrum of external data into their operations can outperform other companies by unlocking improvements in growth, productivity, and risk management.
The COVID-19 crisis provides an example of just how relevant external data can be. In a few short months, consumer purchasing habits, activities, and digital behavior changed dramatically, making preexisting consumer research, forecasts, and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little of use in their internal data. Meanwhile, a wealth of external data could—and still can—help organizations plan and respond at a granular level. Although external-data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what’s available requires considerable effort, given that the external-data environment is fragmented and expanding quickly. Thousands of data products can be obtained through a multitude of channels—including data brokers, data aggregators, and analytics platforms—and the number grows every day. Analyzing the quality and economic value of data products also can be difficult. Moreover, efficient usage and operationalization of external data may require updates to the organization’s existing data environment, including changes to systems and infrastructure. Companies also need to remain cognizant of privacy concerns and consumer scrutiny when they use some types of external data.
These challenges are considerable but surmountable. This blog series discusses the benefits of tapping external-data sources, illustrated through a variety of examples, and lays out best practices for getting started. These include establishing an external-data strategy team and developing relationships with data brokers and marketplace partners. Company leaders, such as the executive sponsor of a data effort and a chief data and analytics officer, and their data-focused teams should also learn how to rigorously evaluate and test external data before using and operationalizing the data at scale.
External-data success stories: Companies across industries have begun successfully using external data from a variety of sources . The investment community is a pioneer in this space. To predict outcomes and generate investment returns, analysts and data scientists in investment firms have gathered “alternative data” from a variety of licensed and public data sources, many of which draw from the “digital exhaust” of a growing number of technology companies and the public web. Investment firms have established teams that assess hundreds of these data sources and providers and then test their effectiveness in investment decisions.
A broad range of data sources are used, and these inform investment decisions in a variety of ways:
- Investors actively gather job postings, company reviews posted by employees, employee-turnover data from professional networking and career websites, and patent filings to understand company strategy and predict financial performance and organizational growth.
- Analysts use aggregated transaction data from card processors and digital-receipt data to understand the volume of purchases by consumers, both online and offline, and to identify which products are increasing in share. This gives them a better understanding of whether traffic is declining or growing, as well as insights into cross-shopping behaviors.
- Investors study app downloads and digital activity to understand how consumer preferences are changing and how effective an organization’s digital strategy is relative to that of its peers. For instance, app downloads, activity, and rating data can provide a window into the success rates of the myriad of live-streaming exercise offerings that have become available over the last year.
Corporations have also started to explore how they can derive more value from external data . For example, a large insurer transformed its core processes, including underwriting, by expanding its use of external-data sources from a handful to more than 40 in the span of two years. The effort involved was considerable; it required prioritization from senior leadership, dedicated resources, and a systematic approach to testing and applying new data sources. The hard work paid off, increasing the predictive power of core models by more than 20 percent and dramatically reducing application complexity by allowing the insurer to eliminate many of the questions it typically included on customer applications.
Three steps to creating value with external data:
Use of external data has the potential to be game changing across a variety of business functions and sectors. The journey toward successfully using external data has three key steps.
1. Establish a dedicated team for external-data sourcing
To get started, organizations should establish a dedicated data-sourcing team. Per our understanding at AIQRATE , a key role on this team is a dedicated data scout or strategist who partners with the data-analytics team and business functions to identify operational, cost, and growth improvements that could be powered by external data. This person also would be responsible for building excitement around what can be made possible through the use of external data, planning the use cases to focus on, identifying and prioritizing data sources for investigation, and measuring the value generated through use of external data. Ideal candidates for this role are individuals who have served as analytics translators and who have experience in deploying analytics use cases and in working with technology, business, and analytics profiles.
The other team members, who should be drawn from across functions, would include purchasing experts, data engineers, data scientists and analysts, technology experts, and data-review-board members . These team members typically spend only part of their time supporting the data-sourcing effort. For example, the data analysts and data scientists may already be supporting data cleaning and modeling for a specific use case and help the sourcing work stream by applying the external data to assess its value. The purchasing expert, already well versed in managing contracts, will build specialization on data-specific licensing approaches to support those efforts.
Throughout the process of finding and using external data, companies must keep in mind privacy concerns and consumer scrutiny, making data-review roles essential peripheral team members. Data reviewers, who typically include legal, risk, and business leaders, should thoroughly vet new consumer data sets—for example, financial transactions, employment data, and cell-phone data indicating when and where people have entered retail locations. The vetting process should ensure that all data were collected with appropriate permissions and will be used in a way that abides by relevant data-privacy laws and passes muster with consumer.This team will need a budget to procure small exploratory data sets, establish relationships with data marketplaces (such as by purchasing trial licenses), and pay for technology requirements (such as expanded data storage).
2. Develop relationships with data marketplaces and aggregators
While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, that approach is not necessarily the most effective. It generally leads to a series of time-consuming vendor-by-vendor discussions and negotiations. The process of developing relationships with a vendor, procuring sample data, and negotiating trial agreements often takes months. A more effective strategy involves using data-marketplace and -aggregation platforms that specialize in building relationships with hundreds of data sources, often in specific data domains—for example, consumer, real-estate, government, or company data. These relationships can give organizations ready access to the broader data ecosystem through an intuitive search-oriented platform, allowing organizations to rapidly test dozens or even hundreds of data sets under the auspices of a single contract and negotiation. Since these external-data distributors have already profiled many data sources, they can be valuable thought partners and can often save an external-data team significant time. When needed, these data distributors can also help identify valuable data products and act as the broker to procure the data.
Once the team has identified a potential data set, the team’s data engineers should work directly with business stakeholders and data scientists to evaluate the data and determine the degree to which the data will improve business outcomes. To do so, data teams establish evaluation criteria, assessing data across a variety of factors to determine whether the data set has the necessary characteristics for delivering valuable insights . Data assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case. For example, a transaction data provider may claim to have hundreds of millions of transactions that help illuminate consumer trends. However, if the data include only transactions made by millennial consumers, the data set will not be useful to a company seeking to understand broader, generation-agnostic consumer trends.
3. Prepare the data architecture for new external-data streams
Generating a positive return on investment from external data calls for up-front planning, a flexible data architecture, and ongoing quality-assurance testing.Up-front planning starts with an assessment of the existing data environment to determine how it can support ingestion, storage, integration, governance, and use of the data. The assessment covers issues such as how frequently the data come in, the amount of data, how data must be secured, and how external data will be integrated with internal data. This will provide insights about any necessary modifications to the data architecture.
Modifications should be designed to ensure that the data architecture is flexible enough to support the integration of a continuous “conveyor belt” of incoming data from a variety of data sources—for example, by enabling application-programming-interface (API) calls from external sources along with entity-resolution capabilities to intelligently link the external data to internal data. In other cases, it may require tooling to support large-scale data ingestion, querying, and analysis. Data architecture and underlying systems can be updated over time as needs mature and evolve.The final process in this step is ensuring an appropriate and consistent level of quality by constantly monitoring the data used. This involves examining data regularly against the established quality framework to identify whether the source data have changed and to understand the drivers of any changes (for example, schema updates, expansion of data products, change in underlying data sources). If the changes are significant, algorithmic models leveraging the data may need to be retrained or even rebuilt.
Minimizing risk and creating value with external data will require a unique mix of creative problem solving, organizational capability building, and laser-focused execution. That said, business leaders who demonstrate the achievements possible with external data can capture the imagination of the broader leadership team and build excitement for scaling beyond early pilots and tests. An effective route is to begin with a small team that is focused on using external data to solve a well-defined problem and then use that success to generate momentum for expanding external-data efforts across the organization.
TiE Kerala – IET India : Webinar on ‘Reimagine Small & Medium Businesses with Artificial Intelligence
TiE Kerala – IET India : Webinar on ‘Reimagine Small & Medium Businesses with Artificial Intelligence
TiE Kerala in partnership with IET India is organising a Webinar on ‘Reimagine SMB’s with AI for Scalability & Competitive advantage’ on Friday, 26th February 2021 between 5:00 to 6:30PM.
Sameer Dhanrajani, CEO of AIQRATE – a bespoke global AI advisory & consulting firm will lead the session.
About the topic:
AI adoption is not just restricted to large enterprises , it is also an excellent lever to achieve tremendous growth for SMBs as well. There is a belief that AI requires considerable investment. Fortunately, for SMBs on a budget, one doesn’t need to break the bank to start incorporating AI into their operations. By picking the low hanging fruits of the business and leveraging quick to deploy AI interventions, one can efficiently harness the capabilities of AI and improve performance metrics across different business functions. However, To be AI ready, SMBs need to strategize and align their AI initiatives with their strategic and operational business goals : revenue enhancement, cost/bottom-line optimization , operational efficiencies , customer acquisition.
SMB’s focused on manufacturing sector are reshaping itself with AI to achieve new models: addictive manufacturing , smart factory , federated production , personalization, connected value networks . This is a critical point for a sector where the possibilities for innovation, transformation through AI comes with new learnings and human capital challenges . The inexorable shift from simple digitization (third industrial revolution ) to innovation based on AI of industry 4.0 has brought about exciting opportunities that policy makers , business leaders and senior executives must pay attention to . Whilst , economic impairment and stunted growth have been deterrents , adoption and adaption of AI is no longer an option . In order to stay relevant in this highly competitive and fast changing business environment , it is imperative for SMB’s to respond quickly to changing customer demands and maximize new market opportunities.
The session anchored by Sameer will showcase on the AI led application areas , AI adoption framework and will provide use cases from different industry segments on leveraging AI to drive growth , scalability and above all, competitive advantage.
Indian Staffing Federation (ISF) Industry series on Reviving India: How is Technology Leading Growth in New Normal?
Indian Staffing Federation (ISF) Industry series on Reviving India: How is Technology Leading Growth in New Normal?
Indian Staffing Federation (ISF) brings its flagship Industry series “Reviving India: How is Technology Leading Growth in New Normal?” on Feb 12, 2021 03:00 PM IST.
Businesses witnessed major disruption in the last eleven months as we witnessed a wave of change towards digitisation, smart transformation – bringing many industries forward with their technological investments. The discussions will cover the way technology has started changing our environment with impact of Digitisation, Internet of Things, Gig Workforce, Artificial Intelligence, Machine Learning among others.
Subject Matter Experts
– Augustus Azariah, HR Leader, IBM India Pvt Ltd
– Uday Sanghani, Partner, BDO India LLP
– Sameer Dhanrajani, CEO, AIQRATE Advisory & Consulting
– Sameer Penakalapati, CEO & Founder, Ceipal
Vijay Sivaram, CEO, Quess IT Staffing
2nd Global AI Conclave 2021
On 10-02-2021 04:00 PM to 10-02-2021 08:30 PM IST
BML Munjal University would like to carry forward the discussion and intellectual interest generated during the inaugural Global AI Conclave last year to further understand AI’s possibilities in specific areas such as Healthcare, Manufacturing, Banking, Fintech etc. Things have been rather dynamic in these pandemic times and this conclave would like to capture our understanding within this context as well. The conclave is expected to be attended by a wide spectrum of people including business leaders, industry practitioners, policy makers/implementers and researchers/students/enthusiasts. An important element of the conclave would be the launch of a research report on the state of Artificial Intelligence within the healthcare sector which has been a major focus of attention in these testing times.
HEALTHCARE IN THE AI ERA
AI and related technologies provide tremendous promise to the healthcare sector across the entire value chain. There may also be certain downsides such as patient safety or data security. Artificial Intelligence is being used to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. While each AI technology can contribute significant value alone, the greater potential lies in the synergies generated by using them together across the entire patient journey, from diagnoses, to treatment, to ongoing health maintenance etc. In a country where the healthcare systems are evolving and have been tested to the hilt during the last one year, the 2 panel discussions in this segment are expected to pick up two important areas – (1) Diagnosis and (2) Ethics.
PANEL 1 Diagnosis Made Easier
It is expected that AI will access multiple sources of data to reveal patterns in disease and aid treatment and care. Diagnostics is focused on using AI and machine-learning to improve diagnostic accuracy and to cut costs. AI diagnostics have the potential to improve the delivery and effectiveness of health care. This panel would discuss this element from a multidimensional perspective. The panel is expected to consist of the following esteemed experts:
- Sanjay Dhawan – Group Director, ClearMedi Healthcare.
- Prof David Snead – Consultant pathologist at the University Hospitals Coventry & Warwickshire Prof. of Pathology Practice, University of Warwick
- Prashant Warier – Founder, Qure.AI
- MODERATOR – Nirupam Srivastava – VP Strategy, M&A and AI/Digital Transformation, Hero Corporate Services
PANEL 2 | What is ethical about it?
The AI code of ethics talks about the role of artificial intelligence as it relates to the continued development of humans. According to some, the purpose of AI should be to produce beneficial intelligence rather than undirected intelligence. Privacy and surveillance, prejudice and discrimination and most importantly, the role of human judgement do come into play when we speak about AI within the healthcare ecosystem. This very relevant and timely discussion will have the following esteemed experts:
- Yonah Welkar – AI innovator, Explorer, Mentor and Board Member in Education, Health, AI and Ethics
- Chhavi Chauhan – Director of Scientific Outreach, American Society for Investigative Pathology. Ethics Advisor at the Alliance for Artificial Intelligence in Healthcare and an AI Policy Expert at the AI Policy Exchange
- Balaji Vishwanathan – Founder, Invento Robotics
- Dipyaman Sanyal – Data Scientist. Educator. Real Estate Researcher. Behavioral Economist. Quant, Dono Consulting
- MODERATOR – Sameer Dhanrajani – CEO, AIQRATE Advisory & Consulting
PANEL 3 |AI in Business: The Evangelist View
Artificial Intelligence has moved into the mainstream of business, driven by advances in cloud computing, big data, open-source software, and improved algorithms. As AI technologies impact how we work, live, and manage businesses, organizational leaders, innovators, and investors are looking to harness the power of AI to create customer value and competitive advantage. This panel discussion would provide a window into how industries across the spectrum – banking, manufacturing, technology companies etc. are capitalizing on the AI opportunity. This panel would consist of the following esteemed experts:
- Sandeep Alur – Director, Microsoft Technology Center, Microsoft
- Ajit Jaokar – Course Director, Artificial Intelligence, Cloud and Edge Implementations, University of Oxford
- Utpal Chakraborty – Head of Artificial Intelligence at YES BANK, Chief Data Scientist, AI Researcher, a TEDx Speaker and Agile Lean Practitioner.
- Pinak Dattaray – Associate Partner, McKinsey
- Dinis Guarda – CEO, board member and digital and crypto economics driver and evangelist. Openbusinesscouncil.org, Ztudium, techabc, fashion abc.
- MODERATOR – David Siegel – Founder, Cutting through the Noise
Sameer Dhanrajani in IIM Kashipur Tejas Series
Sameer Dhanrajani was the guest speaker at Indian Institute of Management Kashipur for ‘Tejas’ – a Leadership Talk series through webinars. Sameer addressed the topic- “AI Strategy: The New Next in Transformation, Innovation & Disruption”.
AIQRATE in 2020 ….A walk to remember
“Enabling clients reimagine their decision making & accentuate the business performance with AI strategy in a transformation, innovation and disruption driven world”
In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture. Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems. To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it. AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.
In the age of the bionic enterprises, AI continues to dominate the technology & business landscape. Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres. Traditionally, human decisions are to a large extent based on intuition, gut and historical data. In the age of AI, several of our decisions will be taken by algorithms. Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business. All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage. If you have not started yet, you are already behind the competition, however large or pedigreed you might be.
So, where are you placed on your AI journey? At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.
At AIQRATE, we are here to help you with the art of the possible with AI. Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses. Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.
We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator. AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.
Wish you a great 2021!
Chief Executive Officer
Bangalore , India
Sameer Dhanrajani in Top 10 of 100 Digital Influencers of 2020
YourStory’s 100 Digital Influencers of 2020 features thought leaders, who, through their writings, inspired hope and strength in a pandemic-hit year, and who, we believe, will influence and inspire the next generation of changemakers in 2021 and beyond.
Sameer Dhanrajani is featured in top 10 of 100 Digital Influencers of 2020.
Decoding AI and analytics for businesses is Sameer Dhanrajani’s daily duty. The former CSO of Fractal Analytics launched AIQRATE in 2019 and provides strategic AI business knowledge.
Sameer Dhanrajani has been known in the ecosystem for his knowledge and passion for understanding artificial intelligence and analytics. The AI advisor and former Fractal Anaytics CSO launched his startup AIQRATE in 2019 to provide strategic AI advisory services and consulting across multiple business segments to help businesses with their AI powered transformation and innovation journey. Sameer’s take on AI can be best understood through his book titled “AI and Analytics: Accelerating Business Decisions” and his TEDx talks. So if you are a business owner looking to understand AI, you know who to go to.
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
In an exclusive interaction with SCIKEY, Sameer Dhanrajani, CEO at AIQRATE Advisory & Consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
“AI led algorithms can decide on how we need to emote, behave, react, transact or interact with an individual,” Sameer Dhanranjani
Sameer is a globally recognized AI advisor, business builder, evangelist and thought leader known for his deep knowledge, strategic consulting approaches in AI space. Sameer has consulted with several Fortune 500 global enterprises, Indian corporations , GCCs, startups , SMBs, VC/PE firms, Academic Institutions in driving AI led strategic transformation and innovation strategies. Sameer is a renowned author, columnist, blogger and four times Tedx speaker. He is an author of bestselling book – AI and Analytics: accelerating business decisions.
In an exclusive interaction with SCIKEY, Sameer Dhanranjani, CEO at AIQRATE advisory consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
Mr Dhanranjani, you have consulted with several Fortune 500 enterprises, GCCs also start-ups in driving AI-led strategic transformation strategies. What according to you, are the topmost strategic considerations to weigh for managing accelerating business in Post COVID world for a start-up?
The unprecedented times of COVID-19 have brought the aspect of decision making under consideration. This includes tactical, strategic, and operational decision making that is crucial to make the venture more sustainable. Today the use of artificial intelligence is quite high amongst organizations. It can be used by start-up ventures and other outfits to make decisions irrespective of the area that needs decision making.
Most decisions that need to be made strategically are being passed on to artificial intelligence-enabled interventions. The algorithm makes similar decisions based on the previous decisions taken. Algorithms can decide how we need to emote, behave, react, transact or interact with the opposite individual This advancement in AI brings the challenge for organizations to create products and services specific to each customer through hyper-personalization and micro-segmenting. However, it can also be considered as an opportunity for organizations to emerge from the pandemic with newer business models and experiences for customers. Start-ups, especially, can make use of such advancements to reinvent and rejuvenate the organizational ecosystem.
You are known for your passion for Artificial Intelligence and are an author to the bestselling book – AI and Analytics: Accelerating Business Decisions. Tell us where how can AI be strategically significant while building productive teams.
My experience has led me to deal with engagements in the entire value chain of HR, ranging from hiring to engagement to incentivization that has leveraged using AI. It is phenomenal to see how AI can help build, engage, and sustain productive teams. AI can help in hiring through the detection emotions, facial expressions, tone modulations of the interviewee through computer vision and image classification techniques.
In the creation of productive teams, AI can gauge the engagement levels of an employee. It tries to look at the various interventions made by an employee regarding their attendance, participation in virtual meetings, and propensity to ask and engage themselves in conversations. It also keeps in check the number of pauses, intervals, and breaks taken by an employee. Every aspect of the employee is being marked to see how productive, inclusive, as an individual and in teams.
What are the top 5 AI trends to watch out for in Post COVID the scenario of the next one year?
When it comes to AI, the first trend emerging is that AI is not a tool or a technology, but it is now being touted as a strategic imperative for any organization. This means that AI strategies will become an intrinsic part and feature of every organisation.
The second trend is the democratization of AI. There is a possibility of the emergence of an AI marketplace where virtual exchanges related to business problems, demo runs etc. can be conducted. One would actually be able to figure out which algorithm is best for them in customer experience, supply chain etc.
The third trend being the cloud will act as a catalyst for AI proliferation. The propensity for cloud providers to enable AI companies with possible aspects of microservice API’s, Product Solutions will be created on the go. This means that the cloud enablers will have options to see various possibilities specific to their organisation when it comes to AI-specific use cases.
The fourth trend is linked to skilling. AI today is a part of a lot of course curriculums. But what is missing is the whole aspect of how does it get applied? The new courseware will be focused on how is AI implemented, adopted in the organization.
The last fifth trend is decision-making enabled by AI, which means humans will have no option but to upskill and reskill themselves to take a more rational, pragmatic and sanguine approach. So new models, new emerging realities of decision making will emerge.
How is AI powering the Future of Work, what are critical considerations for business and tech leaders considering the rapidly changing business dynamics due to COVID?
The future of work will be about AI and what we call AI plus a set of exponential technologies. This means that every aspect of our performance interaction and our responses will be gauged very manually through these technologies. This indicates that the level of performances in terms of how we go up-to-date needs to be worked upon. The future of work is an ecosystem where one particular employer cannot do it all.
This means that if learning must occur through an external player, it must come through the ecosystem of co-employees and the employer. In the future, we will not be caged as mere professionals doing our job but will be encouraged to push our boundaries to explore more at work. At the same time, transformation, innovation, and disruption will be a part of the future’s performance metrics. They will become a major parameter for the organization to create a mediocre versus proficient employee or a professional. This is where the onus will fall on the employees to ensure that they are not just doing what is being called out, but are going beyond to create what we call a value creation for the organisation.
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