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Sameer Dhanrajani, CEO, AIQRATE Advisory & Consulting, is part of speakers line up at TEDxNMIMS Bangalore.. TEDx speaking engagement, his fifth. A marquee platform with a distinguished & ensemble line up of speakers.
The TEDx theme “miniature yet monumental “ is apt in topical times . I will be sharing my perspectives on AI taking center stage in the enterprise decision making process .. the unknown phenomenon yet the biggest one !!
AI & Decision Making session at Spectrum 6.0 Learning Unlimited event at Reliance Industries Limited
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Sameer Dhanrajani, CEO, AIQRATE Advisory & Consulting, presented AI framework & approach on the art of possible & decision making at scale with AI at Spectrum 6.0 Learning Unlimited event at Reliance Industries Ltd on 12th October, 2021. The session was attended by 300+ HR senior leaders & executives. The session carried aspects on reimagining HR business chain with AI and the ensuing impact across employee journey & experience .
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A group of CXOs from several enterprises in Bengaluru came together recently at a round table organised by Forbes India in partnership with Microsoft. They discussed the state of AI and its impact on innovation in their businesses. Below are the edited excerpts:
“Whether we like it or not, AI is absolutely all-pervasive. It’s been there for about 50 years as a technology, but the adoption rate has gone up significantly in just the last four to five years due to multiple reasons – capability, visioning algorithms, better underlying infrastructure to make it run and just the sheer drive of the industry to drive more and more ROI.” said Rohit Adlakha, chief digital and information officer and global head, Wipro HOLMES at Wipro. “Human capability is getting pushed to the limits. How do we augment that with a certain technology that can work hand in hand, while not truly replacing them, but more in terms of enhancement? Looking at the size and scale of a seven billion population across the globe, it is clear that mere technology adoption will not get us there. Something as drastic as an Uber to the power of Uber is what we need actually to make this happen.”
“In terms of AI, adoption is really happening now — it’s no longer just theoretical. Some factors that are helping this are first, the ecosystem. Second, the computing power, the actual physical infrastructure for all this that we have today.” said Satyakam Mohanty, founder of Lymbyc, an AI startup, which has been acquired by Larsen & Toubro Infotech. “One of the challenges is that people are looking at technologies instead of problems. This is creating a lot of silos. If instead, one looked at the problem that needs to be solved and then asked how AI can be applied, then you have a better way to solve this. To get to the world hunger solving stage, you have to industrialise AI, which doesn’t exist today.
Right now, all of the conversation we’re having is the mechanics of how do you make these things operational, but for adoption beyond or even within organizations for larger issues, we have to look at the risk factor.” Satyakam explains further. “Because, as with any technology or business process, there is always a risk factor, right? And the larger the organization, the greater the risk, therefore, slower the adoption that’s the standard paradigm, so how do you de-risk it?”
“At Tech Mahindra we always ask – How can I bring AI into it? The use cases that we did initially were more towards leading process automation or IT operations automation and so on. Now, we have also invested in an open-source AI project called Acumos with AT&T, one of our largest customers,” said George Mundassery, senior VP and global head, Automation and AI at Tech Mahindra. Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardises the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation. “When it comes to applying the AI and making it more and more vibrant and applying it on the ground, I’m sure that that’s when the real benefits will start to be felt,” said George.
“Companies today have no option but to adopt digital. So at every point, they have to redesign their operations, be it their supply chain or the way they connect with various networks or with customer support. So that’s where those possibilities are enormous with AI,” said Prithvijit Roy, CEO and co-founder of BRIDGEi2i, a data analytics startup. “How can we embed AI in terms of creating our customer support without talking to customers, or how do you give the client or the customer what they need without having them articulate it? So there it’s not necessarily just customer experience. How do you train machines to learn on their own and create an application?” Roy said.
“A customer of mine made this statement: ‘Can we go AI with AI?’ What he meant was can we go ‘All In with AI’!” said Sayandeb Banerjee, CEO, and co-founder of a startup The Math Company. “And then we started talking about what is really stopping us from going AI with AI. The point that came out is the democratisation of the thinking is not happening as fast or the democratisation of the ideas is not happening as fast, which in my mind is what is creating a roadblock. Most of the time, my experience is that the roadblock is really the imagination and the visions of what can be done,” Banerjee said. “If you invariably have a good vision, good leader, things are moving, when you don’t have, everybody has access to the same technology, as we have access to the same platform. Why does it finally boils down to vision from a few?”
“When AI becomes more industrial and gets embedded in many places, the question of our biases becomes more important because you’re no longer thinking about them,” said Rohini Srivathsa, National Technology Officer at Microsoft India. “Take the case of an AI driven translator that interprets a doctor as he, and a nurse as she. We are coming to assume that that’s okay. We are not questioning it because it is so much a part of our thinking that the previous data has brought the pronoun to be changed to.”
Rohini continues, “So I think it creates a bigger question as AI becomes pervasive, industrialised and democratised. Are we putting in the right checks and balances? And when I talk to organisations about checks and balances, I think in some ways it is making us think about our own values first.”
“Eventually, what we are saying is that an algorithm is more about reimagining the decision making in your enterprise,” said Sameer Dhanrajani, CEO and co-founder of AIQRATE, an AI consultancy. “Now, if historically, all the decisions in the boards by the CXOs have been taken in the usual manner, in the conventional organisational structure, that may not be relevant anymore. But when you have an algorithm that works for you – embedded let’s say in the value chain of your business and doing a trade for you – it is, therefore, top of the mindshare for boardrooms, senior leaders, CXOs. Eventually, everyone is saying – look we want AI to revolutionise or reimagine our decision making.”
Sameer clarifies further, “if AI is about mimicking the human brain, organisations must have strategies, which are not defined piecemeal, isolated ad-hoc projects, or the Geek Squad. That’s a fundamental challenge.”
“Where AI is going, I think the new systems will be objective basically because you say I just want to increase my visitors to my store by 5 percent and that’s what it should do — help you make the right changes,” said Atul Batra, CTO at Manthan Software Service. “That’s the sophistication one is looking for. So basically, the systems are getting much more contextual for a specific business role like a merchandiser and store operation and so on. And one is seeing a lot of those systems deployed globally by a lot of vendors. I think that’s where it’s going – where there’s continuous feedback because you’re talking to the system and you’re getting feedback, and you’re helping evolve it.”
“We work to impact livelihoods across 14 disabilities. Purpose driven approach will make people do the right AI,” said Shanti Raghavan, founder of Enable India. “With AI, I’m expecting to be able to nudge people in their journey. Can I make them better at crowdsourcing solutions? We’ve done a lot of product management on this, like, how do you get more people to be like your TripAdvisor contributors, right? So we started introducing star users. The next time somebody comes on the program and says, you know what, I’m a star user, you can see that it’s making a difference. So, we have tons of data on how people are behaving on it, how often they log in, what do they actually listen to? We have all of that. We need AI to make sense of it. Now imagine all this data for the entire country; I cannot do this without having AI.”
“The human brain is not tuned towards trust very easily. So when you look at something physical, it’s very easy to understand. But now you come back and say that beyond the computer’s physical screen there is something, which sits on the cloud, which is a bot, which runs intelligence. I’m telling you, close to 100% will disagree.” Says Rohit Adlakha, chief digital and information officer and global head, Wipro HOLMES at Wipro. “The good part is we feel that AI is going to push the limits of the human brain to do much more than what you were able to do.”
“The challenge is that if a human is ultimately going to train a data set, which is going to train data set, you will always have your biases. So given the practical situation in mind, how do you make sure that you have a larger set of people, which will nullify each other’s biases?” says Rohit. “How do you balance it? How do you augment? How do you know humans and bots coexist? How do you make sure that both coexist and build the cast factor? I think we as an industry should push to move it from an enterprise scale to a global scale.”
“There is one more point out there, which is very, very topical today – that AI itself is not enough,” said Ritwik Batabyal, chief of technology and engineering head, Next-gen Business Products at Wipro. “Now we’re talking about these large, complex unresolved documents in office use cases. I mean somewhere algorithms the best of let’s say these kinds of models cannot solve. And in some way, I think there is a facet that is being understood by enterprises, which is can you bring in behavioral science? How do you design for a subconscious mind? You can have an algorithm, but if it’s not adopted, if it’s not implemented? What’s the use there?”
“I was certainly saying when you look at the most applied systems today, it’s getting better and better in terms of cognitive, but all said and done it is coming from some pattern,” said George Mundassery, senior VP and global head, Automation and AI at Tech Mahindra. “Today, I don’t think, it’s able to tell you that decision you made is the right one,” he said.
“There is no going back in this game; once the genie is out of the bottle we can’t put it back. So it’s like any other revolution, I think it’s going to happen,” says Prithvijit Roy. “The truth is that AI is going to take care of certain kinds of jobs that are repetitive. And that’s going to have an impact because mundane work is people’s livelihood in many parts.”
“So it’s not about replacing the human with other parts; it is making humans do certain things, which they were not able to do, which is the bigger part of the story, but this is going to be an impact in the short-term, which we all know how we will face it. So, it will work with enough time to balance out and will hopefully have better augmentation with it.”
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According to a 2019 report from the Centre for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deep fakes when they include doctored video images and audio generated with help from AI) to cyber attacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that don’t require a human controller.
What happens when injustices are propagated not by individuals or organizations but by a collection of machines? Lately, there’s been increased attention on the downsides of artificial intelligence and the harms it may produce in our society, from unequitable access to opportunities to the escalation of polarization in our communities. Not surprisingly, there’s been a corresponding rise in discussion around how to regulate AI.
AI has already shown itself very publicly to be capable of bad biases — which can lead to unfair decisions based on attributes that are protected by law. There can be bias in the data inputs, which can be poorly selected, outdated, or skewed in ways that embody our own historical societal prejudices. Most deployed AI systems do not yet embed methods to put data sets to a fairness test or otherwise compensate for problems in the raw material.
There also can be bias in the algorithms themselves and in what features they deem important (or not). For example, companies may vary their product prices based on information about shopping behaviors. If this information ends up being directly correlated to gender or race, then AI is making decisions that could result in a PR nightmare, not to mention legal trouble. As these AI systems scale in use, they amplify any unfairness in them. The decisions these systems output, and which people then comply with, can eventually propagate to the point that biases become global truth.
The unrest on bringing AI Ethics
Of course, individual companies are also weighing in on what kinds of ethical frameworks they will operate under. Microsoft president Brad Smith has written about the need for public regulation and corporate responsibility around facial recognition technology. Google established an AI ethics advisory council board. Earlier this year, Amazon started a collaboration with the National Science While we have yet to reach certain conclusions around tech regulations, the last three years have seen a sharp increase in forums and channels to discuss governance. In the U.S., the Obama administration issued a report in 2016 on preparing for the future of artificial intelligence after holding public workshops examining AI, law, and governance; AI technology, safety, and control; and even the social and economic impacts of AI. The Institute of Electrical and Electronics Engineers (IEEE), an engineering, computing, and technology professional organization that establishes standards for maximizing the reliability of products, put together a crowdsourced global treatise on ethics of autonomous and intelligent systems. In the academic world, the MIT Media Lab and Harvard University established a $27 million initiative on ethics and governance of AI, Stanford is amid a 100-year study of AI, and Carnegie Mellon University established a centre to explore AI ethics.
Corporations are forming their own consortiums to join the conversation. The Partnership on AI to Benefit People and Society was founded by a group of AI researchers representing six of the world’s largest technology companies: Apple, Amazon, DeepMind/Google, Facebook, IBM, and Microsoft. It was established to frame best practices for AI, including constructs for fair, transparent, and accountable AI. It now has more than 80 partner companies. Foundation to fund research to accelerate fairness in AI — although some immediately questioned the potential conflict of interest of having research funded by such a giant player in the field.
Are data regulations around the corner?
There is a need to develop a global perspective on AI ethics, Different societies around the world have very different perspectives on privacy and ethics. Within Europe, for example, U.K. citizens are willing to tolerate video camera monitoring on London’s central High Street, perhaps because of IRA bombings of the past, while Germans are much more privacy oriented, influenced by the former intrusions of East German Stasi spies , in China, the public is tolerant of AI-driven applications like facial recognition and social credit scores at least in part because social order is a key tenet of Confucian moral philosophy. Microsoft’s AI ethics research project involves ethnographic analysis of different cultures, gathered through close observation of behaviours, and advice from external academics such as Erin Meyer of INSEAD. Eventually, we could foresee that there will be a collection of policies about how to use AI and related technologies. Some have already emerged, from avoiding algorithmic bias to model transparency to specific applications like predictive policing.
The longer take is that although AI standards are not top of the line sought after work, they are critical for making AI not only more useful but also safe for consumer use. Given that AI is still young but quickly being embedded into every application that impacts our lives, we could envisage an array of AI ethics guidelines by several countries for AI that are expected to lead to mid- and long-term policy recommendations on AI-related challenges and opportunities.
Chief AI ethical officer on the cards?
As businesses pour resources into designing the next generation of tools and products powered by AI, people are not inclined to assume that these companies will automatically step up to the ethical and legal responsibilities if these systems go awry.
The time when enterprises could simply ask the world to trust artificial intelligence and AI-powered products is long gone. Trust around AI requires fairness, transparency, and accountability. But even AI researchers can’t agree on a single definition of fairness: There’s always a question of who is in the affected groups and what metrics should be used to evaluate, for instance, the impact of bias within the algorithms.
Since organizations have not figured out how to stem the tide of “bad” AI, their next best step is to be a contributor to the conversation. Denying that bad AI exists or fleeing from the discussion isn’t going to make the problem go away. Identifying CXOs who are willing to join in on the dialogue and finding individuals willing to help establish standards are the actions that organizations should be thinking about today. There comes the aspect of Chief AI ethical officer to evangelize, educate, ensure that enterprises are made aware of AI ethics and are bought into it.
When done correctly, AI can offer immeasurable good. It can provide educational interventions to maximize learning in underserved communities, improve health care based on its access to our personal data, and help people do their jobs better and more efficiently. Now is not the time to hinder progress. Instead, it’s the time for enterprises to make a concerted effort to ensure that the design and deployment of AI are fair, transparent, and accountable for all stakeholders — and to be a part of shaping the coming standards and regulations that will make AI work for all
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Artificial Intelligence will deliver revolutionary impact on how enterprises make decisions today. In the last few years alone, we have rapidly moved beyond heuristics-based decision-making to analytics-driven decision-support. In the VUCA phase, businesses globally are now pivoting to an AI-led, algorithm-augmented style of decision-making. With huge computing power and ever-increasing data storage and analytics prowess, we are entering a new paradigm, a probable and interesting scenario wherein, Artificial Intelligence will play a huge role in augmenting human intelligence and enabling decision-making with complete autonomy. The big hope is that this new paradigm will not only reduce human biases and errors that are common with heuristic decisions, but also reduce the time involved in making these critical decisions.
Here, I’ll attempt to focus on how we moved from simpler data driven decision-support to AI-powered decisions. The evolution of this technology has been breathtaking to behold and just might provide clues as to what we can expect in the future. Further, I’ll cover a few critical aspects that need to be inculcated by organizations on the AI transformation journey, and provide a few insightful cues that will make this journey exciting and fruitful.
Transformation of Decision-Making: From Analytics to AI
First, let us look at how we got here. Some truly pathbreaking events happened along the way while we were trying to make more accurate business decisions, leading us to reimagine how decisions will be made in the enterprise.
Organizations are Becoming Math Houses
With data deluge and digital detonation, combined with the appreciation of the fact that robust analytical capabilities lead to more informed decisions, we are witnessing AI savvy organizations rapidly maturing into ‘math houses.’ Data science – the ability to extract meaningful insights out of data has become de rigueur. Why? Because we now know that data, when seen in isolation, is inherently dumb. It is the ability to process this data and identify patterns and anomalies – using sophisticated algorithms and ensemble techniques – that makes all the difference. These self-intuitive algorithms are where real value resides – as they define the intelligence required to uncover insights and make smart recommendations. Organizations today are evolving into algorithm factories. There is a real understanding today that by enabling continuous advancement in mathematical algorithms, we can deliver consistent decisions based on prescribed as well as evolving business rules.
It is now an established reality that companies with robust mathematical capabilities possess a huge advantage over those that don’t. Indeed, it’s this math-house orientation that separates companies like Amazon and Google from the ones they leave in their wake, with their ability to understand their customers better, identify anomalies and recognize key patterns.
AI: From Predictive to Prescriptive
We saw a similar evolution in the age of analytics – wherein the science and value veered from descriptive analytics, providing diagnostics of past events to prescriptive analytics, helping see and shape the future. We are seeing a similar evolution in how AI gets leveraged in the enterprise and where its maximum value lies.
In early implementations, it was common to see AI as just a tool to predict and forecast future conditions, while accounting for the dynamism seen in the external environment. Today, AI-enabled decision-making is more prescriptive, with AI providing enterprises not just a look into the future, but also key diagnostics and suggestions on potential decision options and their payoffs. Such evolved applications of AI can help businesses make decisions that can potentially exploit more business opportunities, while averting potential threats much earlier.
Mr. Algorithm to Drive Decision Making
The culmination of this AI-era advancement would be the introduction of smart algorithms in every walk of life and business. Algorithms will become further mainstream leading to what will be the most sweeping business change since the industrial revolution. Organizations – those that already aren’t – will start developing a suite of algorithmic IP’s that will de-bias most enterprise decisions.
If Mr. Algorithm is going to drive most enterprise decisions of tomorrow, we need to create some checks and balances to ensure that it does not go awry. It is more critical today than ever before that the algorithmic suite developed by enterprises has a strong grounding in ethics and can handle situations appropriately for which explicit training may not have been provided.
How to Enable this AI Era of Change
Ushering into an AI-centric era of decision-making will require organizational transformation from business, cultural and technical standpoints. The following facets will be the enablers of this change:
Developing an Engineering Mindset
Instrumenting AI in the enterprise requires a combination of data scientists and computer scientists. As AI matures in the enterprise, the users, use cases and data will increase exponentially. To deliver impactful AI applications, scale and extensibility is critically important. This is where having an engineering mindset comes in. Imbibing an engineering mindset will help standardize the use of these applications while ensuring that they are scalable and extensible.
Learning, Unlearning, Relearning
The other critical aspect to a culture where AI can thrive is creating an environment supporting continuous unlearning and relearning. AI can succeed if the people developing and operating it are rewarded for continuous experimentation and exploration. And just like AI, people should be encouraged to incorporate feedback loops and learn continuously. As technology matures it’s important that the existing workforce keeps up. For one, it’s critical that the knowledge of algorithm theory, applied math alongside training on AI library and developer tools, is imparted into the workforce – and is continuously updated to reflect new breakthroughs in this space.
Embedding Design-Thinking and Behavioral Science at the Center of this Transformation
Finally, given the nature of AI applications, it’s critical that they are consumed voraciously. User input very often activates the learning cycles of artificial intelligence applications. To ensure high usage of these applications, it’s very important that we put the user at the center while designing these applications. This is where the application of behavioral sciences and human-centered design will deliver impact. By imparting empathy in these applications for the user, we will be able to design better and more useful AI applications.
As we augment decision-making with algorithmic, AI-centered systems and platforms – the big expectation is that they will bring untold efficiencies in terms of cost, alongside improvement in the speed and quality with which decisions get made. It’s time to reimagine and deliver on enterprise decision-making that is increasingly shaped through artificial intelligence. These aspects – how the AI is progressing and how to exploit its potential are of paramount importance to keep in mind for an AI transformation.