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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.
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Reimagining analytics in the cloud enables enterprises to achieve greater agility, increase scalability and optimize costs. But organizations take different paths to achieving their goals. The best way to proceed will depend on data environment and business objectives. There are two best practices to maximize analytics adoption in the cloud:
• Cloud Data Warehouse, Data Lake, and Lakehouse Transformation: Strategically moving data warehouse and data lake to the cloud over time and adopting a modern, end-to-end data infrastructure for AI, and machine learning projects.
• New Cloud Data Warehouse and Data Lake: Start small and fast and grow as needed by spinning up a new cloud data warehouse or cloud data lake. The same guidance applies whether implementing new data warehouses and data lakes in the cloud for the first time, or doing so for an individual department or line of business.
As cloud adoption grows, most organizations will eventually want to modernize their enterprise analytics infrastructure entirely in the cloud. With the transformation pathway, rebuild everything to take advantage of the most modern cloud-based enterprise data warehouse, data lake, and lake house technology to end up in the strongest position long term. But migrate data and workloads from existing on-premises enterprise data warehouse and data lake to the cloud incrementally, over time. This approach allows enterprises to be strategic while minimizing disruption. Enterprises can take the time to carefully evaluate data and bring over only what is needed, which makes this a less risky approach. It also enables more complex analysis of data, using artificial intelligence, machine learning. The combination of a cloud data warehouse and data lake allows to manage the data necessary for analytics by providing economical scalability across compute and storage that is not possible with an on-premises infrastructure. And it enables to incorporate new types of data, from IoT sensors, social media, text, and more, into your analysis to gain new insights.
For this pathway ,enterprises need an intelligent, automated data platform that delivers a number of critical capabilities. It should handle new data sources, accommodate AI and machine learning projects, support new processing engines, deliver performance at a massive scale, and offer serverless scale up/scale down capabilities. As with a brand-new cloud data warehouse or data lake, enterprises need cloud-native, best-of-breed data integration, data quality, and metadata management to ensure maximizing the value of cloud analytics. Once the data is in the cloud, organization can provide users with self-service access to this data so they can more easily and seamlessly create reports or take swift decision. Subsequently , this transformation pathway gives organizations an end-to-end modern infrastructure for next-generation cloud analytics
Lines of business increasingly rely on analytics to improve processes and business impact. For example, sales and marketing no longer ask, “How many leads did we generate?” They want to know how many sales-ready leads we gathered from Global 500 accounts as evidenced by user time spent consuming content on the web. But individual lines of business may not have the time or resources to create and maintain an on-premises data warehouse to answer these questions. With a new cloud data warehouse and data lake, departments can get analytics projects off the ground quickly and cost effectively. Departments simply spin up their own cloud data warehouses, populate them with data, and make sure they’re connected to analytics and BI tools. For data science projects, a team may want to quickly add a cloud data lake. In some cases, this approach enables the team to respond to requests for sophisticated analysis faster than centralized teams can normally handle. Whatever the purpose of new cloud data warehouse and data lake, enterprises need intelligent, automated cloud data management with best of-breed, cloud-native data integration, data quality, and metadata management all built on a cloud-native platform in order to deliver value and drive ROI. And note that while this approach allows enterprises to start small and scale as needed, the downside is that data warehouse and data lake may only benefit a particular department inside the enterprise.
Some organizations with significant investments in on-premises enterprise data warehouses and data lakes are looking to simply replicate their existing systems to the cloud. By lifting and shifting their data warehouse or data lake “as is” to the cloud, they seek to improve flexibility, increase scalability, and lower data center costs while migrating quickly to minimize disruption. Lifting and shifting an on-premises system to the cloud may seem fast and safe. But in reality, it’s an inefficient approach, one that’s like throwing everything you own into a moving van instead of packing strategically for a plane trip. In the long run, reducing baggage and traveling by air delivers greater agility and faster results because you are not weighed down by unnecessary clutter. Some organizations may need to do a lift and shift, but most will find it’s not the best course of action because it simply persists outdated or inefficient legacy systems and offers little in the way of innovation.
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
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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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Though a rhetorical theme , We can safely defer the discussion about whether artificial intelligence will eventually take over board functions. We cannot, however, defer the discussion about how boards will oversee AI — a discussion that’s relevant whether organizations are developing AI systems or buying AI-powered software. With AI adoption in increasingly widespread use, it’s time for every board to develop a proactive approach for overseeing how AI operates within the context of an organization’s overall mission and risk management.
According to a recent global AI survey, although AI adoption is increasing rapidly, overseeing and mitigating its risks remain unresolved and urgent tasks: Just 41% of respondents said that their organizations “comprehensively identify and prioritize” the risks associated with AI deployment. Board members recognize that this task is on their agendas: According to the 2019 National Association of Corporate Directors (NACD) Blue Ribbon Commission report, “Fit for the Future: An Urgent Imperative for Board Leadership,” 86% of board members “fully expect to deepen their engagement with management on new drivers of growth and risk in the next five years.”
Why’s this an imperative ? Because AI’s potential to deliver significant benefits comes with new and complex risks. For example, the frequency with which AI-driven facial recognition technologies misidentify nonwhite or female faces is among the issues that have driven a pullback by major vendors — which are also concerned about the use of the technology for mass surveillance and consequent civil rights violations. Recently, IBM stopped selling the facial technology altogether. Further, Microsoft said it would not sell its facial recognition technology to police departments until Congress passes a federal law regulating its use by law enforcement. Similarly, Amazon said it would not allow police use of its technology for a year, to allow time for legislators to act.
The use of AI-driven facial recognition technology in policing is just one notorious example, however. Virtually all AI systems & platforms in use today may be vulnerable to problems that result from the nature of the data used to train and operate them, the assumptions made in the algorithms themselves, the lack of system controls, and the lack of diversity in the human teams that build, instruct, and deploy them.Many of the decisions that will determine how these technologies work, and what their impact will be, take place largely outside of the board’s view — despite the strategic, operational, and legal risks they present. Nonetheless, boards are charged with overseeing and supporting management in better managing AI risks.
Increasing the board’s fluency with and visibility into these issues is just good governance. A board, its committees, and individual directors can approach this as a matter of strict compliance, strategic planning, or traditional legal and business risk oversight. They might also approach AI governance through the lens of environment, social, and governance (ESG) considerations: As the board considers enterprise activity that will affect society, AI looms large. The ESG community is increasingly making the case that AI needs to be added to the board’s portfolio.
How Boards can assess the quality & impact of AI
Directors’ duties of care and loyalty are familiar and well established. They include the obligations to act in good faith, be sufficiently informed, and exercise due care in oversight over strategy, risk, and compliance.
Boards assessing the quality and impact of AI and oversight is required should understand the following:
- AI is more than an issue for the technology team. Its impact resonates across the organization and implicates those managing legal, marketing, and human resources functions, among others.
- AI is not a siloed thing. It is a system comprising the technology itself, the human teams who manage and interact with it, and the data upon which it runs.
- AI systems need the accountability of C-level strategy and oversight. They are highly complex and contextual and cannot be trustworthy without integrated, strategic guidance and management.
- AI is not static. It is designed to adapt quickly and thus requires continuous oversight.
- The AI systems most in use by business today are efficient and powerful prediction engines. They generate these predictions based on data sets that are selected by engineers, who use them to train and feed algorithms that are, in turn, optimized on goals articulated — most often — by those developers. Those individuals succeed when they build technology that works, on time and within budget. Today, the definition of effective design for AI may not necessarily include guardrails for its responsible use, and engineering groups typically aren’t resourced to take on those questions or to determine whether AI systems operate consistently with the law or corporate strategies and objectives.
The choices made by AI developers — or by an HR manager considering a third-party resume-screening algorithm, or by a marketing manager looking at an AI-driven dynamic pricing system — are significant. Some of these choices may be innocuous, but some are not, such as those that result in hard-to-detect errors or bias that can suppress diversity or that charge customers different prices based on gender. Board oversight must include requirements for policies at both the corporate level and the use-case level that delineate what AI systems will and will not be used for. It must also set standards by which their operation, safety, and robustness can be assessed. Those policies need to be backed up by practical processes, strong culture, and compliance structures.
Enterprises may be held accountable for whether their uses of algorithm-driven systems comply with well-established anti-discrimination rules. The U.S. Department of Housing and Urban Development recently charged Facebook with violations of the federal Fair Housing Act for its use of algorithms to determine housing-related ad-targeting strategies based on protected characteristics such as race, national origin, religion, familial status, sex, and disabilities. California courts have held that the Unruh Civil Rights Act of 1959 applies to online businesses’ discriminatory practices. The legal landscape also is adapting to the increasing sophistication of AI and its applications in a wide array of industries beyond the financial sector. For instance, the FTC is calling for the “transparent, explainable, fair, and empirically sound” use of AI tools and demanding accountability and standards. The Department of Justice’s Criminal Division’s updated guidance underscores that an adequate corporate compliance program is a factor in sentencing guidelines.
From the board’s perspective, compliance with existing rules is an obvious point, but it is also important to keep up with evolving community standards regarding the appropriate duty of care as these technologies become more prevalent and better understood. Further, even after rules are in force, applying them in particular business settings to solve specific business problems can be difficult and intricate. Boards need to confirm that management is sufficiently focused and resourced to manage compliance well, along with AI’s broader strategic trade-offs and risks.
Risks to brand and reputation. The issue of brand integrity — clearly a current board concern — may most likely drive AI accountability in the short term. Recent issues faced by individuals charged with advancing responsible AI within companies found that the “most prevalent incentives for action were catastrophic media attention and decreasing media tolerance for the status quo.” Well before new laws and regulations are in effect, company stakeholders such as customers, employees, and the public are forming opinions about how an organization uses AI. As these technologies penetrate further into business and the home, their impact will increasingly define a brand’s reputation for trust, quality, and its mission.
The role of AI in exacerbating racial, gender, and cultural inequities is inescapable. Addressing these issues within the technology is necessary, but it is not sufficient. Without question, we can move forward only with genuine commitments to diversity and inclusion at all levels of technology development and technology consumption.
Business continuity concerns. Boards and executives are already keenly aware that technology-dependent enterprises are vulnerable to disruption when systems fail or go wrong, and AI raises new board-worthy considerations on this score. First, many AI systems rely on numerous and unknown third-party technologies, which might threaten reliability if any element is faulty, orphaned, or inadequately supported. Second, AI carries the potential of new kinds of cyber threats, requiring new levels of coordination within any enterprise. And bear in mind that many AI developers will tell you that they don’t really know what an AI system will do until it does it — and that AI that “goes bad,” or cannot be trusted, will need remediation and may have to be pulled out of production or off the market.
The ”NEW” strategic imperative for Boards
Regardless of how a board decides to approach AI fluency, it will play a critical role in considering the impact of the AI technologies that a business chooses to use. Before specific laws are in effect, and even well after they are written, businesses will be making important decisions about how to use these tools, how they will impact their workforces, and when to rely upon them in lieu of human judgment. The hardest questions a board will face about proposed AI applications are likely to be “Should we adopt AI in this way?” and “What is our duty to understand how that function is consistent with all of our other beliefs, missions, and strategic objectives?” Boards must decide where they want management to draw the line: for example, to identify and reject an AI-generated recommendation that is illegal or at odds with organizational values .
Boards should do the following in order to establish adequate AI fluency mechanics:
- Learn where in the organization AI and other exponential technologies are being used or planning to be used, and why.
- Set a regular cadence for management to report on policies and processes for governing these technologies specifically, and for setting standards for AI procurement and deployment, training, compliance, and oversight.
- Encourage the appointment of a C-level executive to be responsible for this work, across company functions.
- Encourage adequate resourcing and training of the oversight function.
It’s not too soon for boards to begin this work; even for enterprises with little investment in AI development, it will find its way into the organization through AI-infused tools and services. The legal, strategic, and brand risks of AI are sufficiently grave that boards need facility with them and a process by which they can work with management to contain the risks while reaping the rewards.AI Fluency is the new strategic agenda.