AI = The Future of “H” in HR : Introducing AIQRATE’s consulting offerings for CHRO/CPO/HR leaders

AI = The future of “H” in HR . In today’s competitive businesses , the role of AI in planning, operations & strategy has transformed from being a competitive differentiation to a competitive necessity . The age of “ trust me , this will work” is over. In the current business mandate , where HR is held accountable for delivering business results , it has become imperative to harness the power of AI . AI can elevate HR from a tactical support function to a strategic transformative function . HR business function disruption thru Talent Sciences : business capability of using AI and algorithmic modeling to drive HCM decision making will form the backbone of HR function.

Introducing AIQRATE’s consulting offering for Chief Human Resource Officer (CHRO) / Chief people officer (CPO) / Chief Talent officer (CTO) /HR Leaders working across Enterprises , GCCs , SMBs , Startups , Public Institutions : 

1. AI master class session : Contextualized for CHRO , CPO : demystify AI , AI strategy canvas , AI landscape & wide applications , HR vale chain interventions
2. AI advisor on-demand : Build AI led decision making strategies and processes across the HR value chain and strategic interventions
3. AI talent mapping strategies : Execute AIQRATE “T-REX” framework for building enterprise wise AI skilling & learning regime
4. AI led interventions for CHRO/CPO : Reimagine HR domain , HR business function problems and scenarios leveraging AIQRATE consulting expertise
5. Analytics to AI maturity assessment : Gauge your enterprise AI adoption maturity with AIQRATE “Elevate” transformation journey framework

AIQRATE’s extensive yet bespoke consulting offerings for CHRO/CPO/HR leaders focuses on building AI led strategies on talent workforce decisions and tracking performance of HR strategic initiatives and also on building data driven discovery algorithms on improving HR process efficiencies and outcomes. 

AIQRATE’s attempts to gear up HR leaders to the future of work and our curated offerings will enable navigate four broad shifts for HR leaders :

1. Accentuate strategic business acumen

2. Augment AI driven expertise for decision making

3. Amplify “transformation driven impact “ within the HR business function.

4. Accelerate “innovation driven culture” within the HR team

Reach out to us at consult@aiqrate.ai for detailed view and approach on our extensive AI consulting offerings for CHRO/CPO/HR leaders .

Business transformation programs have long focused on productivity enhancements —taking a “better, faster, cheaper” approach to how the enterprise works. And for good reason: disciplined efforts can boost productivity as well as accountability, transparency, execution, and the pace of decision making. When it comes to delivering fast results to the bottom line, it’s a proven recipe that works.

The problem is, it’s no longer enough. Artificial Intelligence enabled disruption are upending industry after industry, pressuring incumbent companies not only to scratch out stronger financial returns but also to remake who and what they are as enterprises.

Doing the first is hard enough. Tackling the second—changing what your company is and does—requires understanding where the value is shifting in your industry (and in others), spotting opportunities in the inflection points, and taking purposeful actions to seize them. The prospect of doing both jobs at once is sobering.

How realistic is it to think your company can pull it off? The good news is that AIQRATE can demonstrate that it’s entirely possible for organizations to ramp up their bottom-line performance even as they secure game-changing portfolio wins that redefine what a company is and does. What’s more, AL led transformations that focus on the organization’s performance and portfolio appear to load the dice in favor of transformation results. By developing these two complementary sets of muscles, companies can aspire to flex them in a coordinated way, using performance improvements to carry them to the next set of portfolio moves, which in turn creates momentum propelling the company to the next level.

Strategic Steps towards AI led Transformation:

This aspect covers AI led “portfolio-related” moves. The first is active resource reallocation towards building AI led transformation units, which I define as the company shifting more than 20 percent of its capital spending across its businesses or markets over ten years. Such firms create 50 percent more value than counterparts that shift resources at a slower clip.

Meanwhile, a big move in programmatic M&A driven by AI led spot trending—the type of deal making that produces more reliable performance boosts than any other—requires the company to execute at least one deal per year, cumulatively amounting to more than 30 percent of a company’s market capitalization over ten years, and with no single deal being more than 30 percent of its market capitalization.

Making big moves tends to reduce the risk profile and adds more upside than downside. The way I explain this to senior executives is that when you’re parked on the side of a volcano, staying put is your riskiest move.

AI led Transformations that go ‘all in’ by addressing both a company’s performance and its portfolio yield the highest odds.

The implication of these transformation stories is clear: approaches that go all in by addressing both a company’s performance and its portfolio yield the highest odds of lasting improvement. Over the course of a decade, companies that followed this path nearly tripled their likelihood of reaching the top quin tile of the AI transformation power curve relative to the average company in the middle.

Play to win with AI

Life would be simpler if story ended here. However, you’re not operating in a competitive vacuum. As I described earlier, other forces influence your odds of success in significant ways—in particular, how your industry is performing. Research studies have indicated that companies facing competitive headwinds would face longer odds of success than those with tailwinds.

Companies that combined big performance moves with big portfolio moves (including capital expenditures, when not the only portfolio move employed) saw a big lift in their odds. Life is still challenging for these companies—their net odds are dead even—yet this is superior to the negative odds of the other situations.

Winning thru competitive advantage with AI

In an improving industry, the returns to performance improvement are amplified massively. This runs contrary to the very human tendency of equating performance transformations with turnaround cases

The takeaway from all this is that two big rules stand out as commonly and powerfully true whatever your context: first, get moving with AI , don’t be static; second, go all in if you can with AI led transformation programs —it’s always the best outcome (and also the rarest).

Running the AI led transformation program

In my experience, the companies that are most successful at transforming themselves with AI ,sequence their moves so that the rapid lift of performance improvement provides oxygen and confidence for big moves in M&A, capital investment, and resource reallocation. And when the right portfolio moves aren’t immediately available or aren’t clear, the improved performance helps buy a company time until the strategy can catch up.

To illustrate this point, consider the anecdote about Apple that Professor Richard Rumelt describes in his book, Good Strategy/Bad Strategy. It was the late 1990s; Steve Jobs had returned to Apple and cleaned house through productivity-improving cutbacks and a radically simplified product line. Apple was much stronger, yet it remained a niche player in its industry. When Rumelt asked Jobs how he planned to address this fact, Jobs just smiled and said, ‘I am going to wait for the next big thing.’

While no one can guarantee that your “next big thing” will be an iPod-size breakthrough, there’s nothing stopping you from laying the groundwork for a successful AI led transformation. To see how prepared, you are for such an undertaking, ask yourself—and your team—the following five questions. I sincerely hope they provoke productive and transformative discussion among your team.

1.Where is the new business value chain that’s driven by AI

Achieving success with big, portfolio-related moves requires understanding where the business value flows in your business and why. The structural attractiveness of markets, and your position in them, can and does change over time. Ignore this and you might be shifting deck chairs on the Titanic. Meanwhile, to put this thinking into action, you must also view the company as an ever-changing portfolio. This represents a sea change for managers who are used to plodding, once-a-year strategy sessions that are more focused on “getting to yes” and on protecting turf than on debating real alternatives. Get high-powered decision-making algorithms to navigate you thru this transformation.

2. Put your money in building an AI led strategy

Only 10% of the US fortune 200 companies have AI led strategy; this is an impending strategic aspect that cannot be ignored. The dimensions of reimagining customer experience, building innovative products and services and transforming the businesses need to have an AI led strategy move by the CXOs

3.Are you ready for disruption?

Increasingly, incumbent organizations are getting to the pointy end of disruption, where they must accelerate the transition from legacy business models to new ones and even allow potentially cannibalizing businesses to flourish. Sometimes this requires a very deliberate two-speed approach where legacy assets are managed for cash while new businesses are nurtured for growth.

4.Will our company take this seriously?

Embracing AI led transformative change requires commitment, and gaining commitment requires a compelling change story that everyone in the company can embrace. Philips recognized this in 2011 when it launched its “Accelerate” program. Along with productivity improvements and portfolio changes (including a big pivot from electronics to health tech), the company shaped its change story around improving three billion lives annually by 2030, as part of a broader goal of making the world healthier and more sustainable through innovation. Massive thrust and investment was laid by Phillips leadership team on AI led transformation programs.

5.Is the leadership ready for the transformation?

Leading a successful AI led transformation requires a lot more than just picking the right moves and seeing them through. Among your other priorities: build momentum, engage your workforce, and make the change personal for yourself and your company. All of this means developing new leadership skills and ways of working, while embracing a level of commitment as a leader that may be unprecedented for you.

In the end, AI led strategy for transformation is a process and start of a journey …. embrace it or feel the heat of leaving behind. The new age competition is agile and nimble and AI led transformation strategy is a right move to thwart the competition.

It’s time for boards to craft an AI led strategy . Three strategic aspects can help them and senior leaders to augment decision making process in the board meetings

In the boardroom, and the head of a major global conglomerate is in the hot seat. A director with a background in the manufacturing industry is questioning the economics, an assumption underlying the executive’s industry forecast: that the industry’s ratio of forecast will remain relatively constant. The business leader appears confident about the assumption of stability, which has implications for both the competitive environment and for financial results. But the director isn’t convinced: “In my experience, the forecast changes continuously with the economic cycle and needs to bake in assumptions,” he says, “and I’d feel a whole lot better about these estimates if you had some facts to prove that this has changed.” and the rest of the board doesn’t have it. Finally, the chairman intervenes: “The question being raised is critical and not just for our manufacturing business but for our entire strategy. We’re not going to resolve this today, but let’s make sure it’s covered thoroughly during our strategy off-site and he added , “let’s have some good staff work in place to inform the discussion.”

If the preceding exchange sounds familiar, it should: in the wake of the financial crisis, we find that uncomfortable conversations such as this one are increasingly common in boardrooms around the world as corporate directors and executives come to grips with a changed environment. Ensuring that a company has a great strategy is among a board’s most important functions and the ultimate measure of its stewardship. Yet even as new governance responsibilities and faster competitive shifts require much more—and much better—board engagement on strategy, a great number of boards remain hamstrung by familiar challenges.

Enter AI led strategy for boards

For starters, there’s the problem of time: most boards have about six to eight meetings a year and are often hard pressed to get beyond compliance-related topics to secure the breathing space needed for developing strategy. A recent survey of board members to learn where they’d most like to spend additional time, two out of three picked strategy. A related finding was that 44 percent of directors said their boards simply reviewed and approved management’s proposed strategies. Why such limited engagement? One likely reason is an expertise gap: only 10 percent of the directors felt that they fully understood the industry dynamics in which their companies operated. As a result, only 21 percent of them claimed to have a complete understanding of the current strategy .

What’s more, there’s often a mismatch between the time horizons of board members and of top executives , and that lack of alignment can diminish a board’s ability to engage in well-informed give-and-take about strategic trade-offs. “The chairman of my company has effectively been given a decade,” says the CEO of a company “and I have three years—tops—to make my mark. If I come up with a strategy that looks beyond the current cycle, I can never deliver the results expected from me. Yet I am supposed to work with him to create long-term shareholder value. How am I supposed to make this work?” It’s a fair question, particularly since recent shows that major strategic moves involving active capital reallocation deliver higher shareholder returns than more passive approaches over the long haul, but lower returns over time frames of less than three years.

Compounding these challenges is the increased economic volatility prompting many companies to rethink their strategic rhythm, so that it becomes less calendar driven and formulaic and more a journey involving frequent and regular dialogue among a broader group of executives. To remain relevant, boards must join management on this journey, and management in turn must bring the board along—all while ensuring that strategic co-creation doesn’t become confusion or, worse, shadow management. This is where curating AI strategy for competitive advantage and informed decision making comes to the picture.

Three strategic aspects to ponder on AI led strategy for Boards :

While no one-size-fits-all solution can guide companies as they set out, board members and senior managers ask themselves three simple questions as they approach the development of AI strategy. Using it should raise the quality of decision making , overall engagement and help determine the practical steps each group must take to get there. The usual annual strategic refresh is unlikely to provide the board with an appreciation of the context it would need to address the questions fully, let alone to generate fresh insights in response.

1.Can AI make the boards understand the industry dynamics

Most boards spend most of their strategic time reviewing plans, yet relatively few directors feel they have a complete understanding of the dynamics of the industries their companies operate in or even of how those companies create value. To remedy this problem and to avoid the superficiality it can engender, boards need time—some without management present—so they can more fully understand the structure and economics of the business, as well as how it creates value. They should use this time to get ahead of issues rather than always feeling a step behind during conversations on strategy or accepting management biases or ingrained habits of thought.AI can lay out comprehensive picture of industry and competitive industry dynamics with historical and future forward looking scenarios to make the job of the boards simpler.

2. Can AI trigger enough board–management debate before a specific strategy is discussed?

Aided thru AI and armed with a foundational view based on a clearer understanding of industry and company economics, boards are in a better position to have the kinds of informed dialogue with senior managers that ultimately help them prepare smarter and more refined strategic options for consideration. Board members should approach these discussions with data driven mind-set and with the goal of helping management to broaden its thinking by considering new, even unexpected, perspectives.

During such debates, management’s role is to introduce key pieces of content: a detailed review of competitors, key external trends likely to affect the business, and a view of the specific capabilities the company can use to differentiate itself. The goal of the dialogue is to develop a stronger, shared understanding of the skills and resources the company can use to produce strong returns, as opposed to merely moving with the tide. This is where boards can evangelize and seep in AI in the senior executives group for broader knowledge augmentation . 

3.Can AI bring in all strategic options and approaches to the table for board and management ?

Very often, the energizing discussions between the board and management about the business, its economics, and the competition represent the end of the debate. Afterward, the CEO and top team go off to develop a plan that is then presented to the board for approval. Instead, what’s needed at this point is for management to take some time—go thru the self-learning enabled algorithm —to formulate a robust set of strategic options, each followed through to its logical end state, including the implications for the allocation of people, capital, and other resources. These strategic options through the revised algorithmic exercise can then be brought back to the board for discussion and decision making.

Developing AI led strategy is a new phenomenon and will take time to mature —yet will become more powerful algorithmic based decision making process and with board’s increased involvement, which introduces new voices and expertise to the debate and puts pressure on management teams and board members alike to find the best answers. Yet this form of AI led strategy development, when done well, is invaluable. It not only leads to clearer strategies but also creates the alignment necessary to make bolder moves with more confidence and to follow through by committing resources to key decisions. AI led decision making for the boards is here….

(AIQRATE advisory & consulting is a bespoke AI advisory and consulting firm and provide strategic advisory services to boards , CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions and create AI powered enterprises . visit : www.aiqrate.ai )

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

What constitutes ‘fair use’ of data is increasingly coming under scrutiny by regulators across the world. With the digital detonation that has been unleashed in the past few years, leading to a deluge of data – organisations globally have jumped at the prospect of achieving competitive advantage through more refined data mining methods. In the race for mining every bit of data possible and using it to inform and improve algorithmic models, we have lost sight of what data we should be collecting and processing. There also seems to be a deficit of attention to what constitutes a breach and how offending parties should be identified and prosecuted for unfair use.

There’s growing rhetoric that all these questions be astutely addressed through a regulation of some form. With examples of detrimental use of data surfacing regularly, businesses, individuals and society at large are demanding an answer for exactly what data can be collected – and how it should be aggregated, stored, managed and processed.
If data is indeed the new oil, we need to have a strong understanding of what constitutes the fair use of this invaluable resource. This article attempts to highlight India’s stance on triggering regulatory measures to govern the use of data.Importance of Data Governance

Importance of Data Governance

Before we try to get into what data governance should mean in the Indian context, let us first look at the definition of data governance and why it is an important field of study to wrap our head around.

In simple terms, data governance is the framework that lays down the strategy of how data is used and managed within an organisation. Data governance leaders must stay abreast of the legal and regulatory frameworks specific to the geographies that they operate in and ensure that their organisations are compliant with the rules and regulations. A lot of their effort at present is aimed at maintaining the sanctity of organisational data and ensuring that it does not fall in the wrong hands. As such, the amount of time and effort expended on ensuring that these norms are adequately adhered to is contingent upon the risk associated with a potential breach or loss of data.

In effect, a framework of data governance is intended to ensure that a certain set of rules is applied and enforced to ensure that data is used in the right perspective within an organisation.

Data Governance in Indian Context

India is rapidly moving towards digitisation. Internet connectivity has exploded in the last few years, leading to rapid adoption of internet-enabled applications — social media, online shopping, digital wallets etc. The result of this increasing connectivity and adoption is a fast-growing digital footprint of Indian citizens. Add to this the Aadhaar programme proliferation and adoption – and we have almost every citizen that has personal digital footprint somewhere – codified in the form of data.

With a footprint of this magnitude, there is an element of risk attached. What if this data falls in the wrong hands? What if personal data is used to manipulate citizens? What are the protection mechanisms citizens have against potential overreach by stewards of the data themselves? It is time we found answers to these very pertinent questions – and data governance regulation is the way we will find comprehensive answers to these impending conversations

Perspectives for India

The pertinent departments are mulling over on a collective stand that should be taken while formulating data governance norms. For one, Indian citizens are protected by a recent Supreme Court ruling that privacy is a fundamental right. This has led to a heightened sense of urgency around arriving at a legislative framework for addressing genuine concerns around data protection and privacy, as well as cybersecurity.

As a result of these concerns, the Central government recently set up a committee of experts, led by Justice BN Srikrishna, tasked with formulating data governance norms. This committee is expected to maintain the delicate balance between protecting the privacy of citizens and fostering the growth of the digital economy simultaneously. Their initial work – legal deliberations and benchmarking activity against similar legal frameworks such as GDPR (General Data Protection Regulation) – has resulted in the identification of seven key principles around which any data protection framework needs to be built. Three of the most crucial pointers include:

1. Informed Consent: Consent is deemed to be an expression of human autonomy. While collecting personal data, it is critical that the users be informed adequately about the implications around how this data is intended to be used before capturing their express consent to provide this data
2. Data Minimisation: Data should not be collected indiscriminately. Data collected should be minimal and necessary for purposes for which the data is sought and other compatible purposes beneficial for the data subject.
3. Structured Enforcement: Enforcement of the data protection framework must be by a high-powered statutory authority with sufficient capacity. Without statutory authority, any remedial measures sought by citizens over data privacy infringement will be meaningless.
Striking the right balance between fostering an environment in which the digital economy can grow to its full potential, whilst protecting the rights of citizens is extremely difficult.

With a multitude of malafide parties today seeking to leverage personal data of citizens for malicious purposes, it is crucial that the government and the legal system set out a framework that protects the sovereignty and interests of the people. By allaying fears of misuse of data, the digital economy will grow as people become less fearful and more enthusiastically contribute information where a meaningful end outcome can be achieved.

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a “symbiotic autonomy.” If you may. It will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Mutual Co-existence – A Symbiotic Autonomy

In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

I don’t think at AI will become an existential threat to humanity. Not that it’s impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don’t think it’s true.

If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity. Also, there is a complete fallacy due to the fact that our only exposure to intelligence is through other humans. There are absolutely no reason that intelligent machines will even want to dominate the world and/or threaten humanity. The will to dominate is a very human one (and only for certain humans).

Even in humans, intelligence is not correlated with a desire for power. In fact, current events tell us that the thirst for power can be excessive (and somewhat successful) in people with limited intelligence.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Intelligence of Accountability

A lot of companies are working hard on making machines to be able to explain themselves — to be accountable for the decisions they make, to be transparent. A lot of the research we do is letting humans or users query the system. When Cobot, my robot, arrives to my office slightly late, a person can ask , “Why are you late?” or “Which route did you take?”

So they are working on the ability for these AI systems to explain themselves, while they learn, while they improve, in order to provide explanations with different levels of detail. People want to interact with these robots in ways that make us humans eventually trust AI systems more. You would like to be able to say, “Why are you saying that?” or “Why are you recommending this?” Providing that explanation is a lot of the research that is being done, and I believe robots being able to do that will lead to better understanding and trust in these AI systems. Eventually, through these interactions, humans are also going to be able to correct the AI systems. So they are trying to incorporate these corrections and have the systems learn from instruction. I think that’s a big part of our ability to coexist with these AI systems.

The Worst Case Contingency

A lot of the bad things humans do to each other are very specific to human nature. Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers, etc were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behavior unless we explicitly build these behaviors into them. Why would we?

Also, if someone deliberately builds a dangerous and generally-intelligent AI, other will be able to build a second, narrower AI whose only purpose will be to destroy the first one. If both AIs have access to the same amount of computing resources, the second one will win, just like a tiger a shark or a virus can kill a human of superior intelligence.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Musk explained that his attempt to sound the alarm on artificial intelligence didn’t have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity

Brain-machine interfaces could overhaul what it means to be human and how we live. Today, technology is implanted in brains in very limited cases, such as to treat Parkinson’s Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend’s trip to Antarctica — hearing the sound of penguins, feeling the cold ice — all while your body sits on your couch.

Final Word – Is AI Uncertainty really about AI ?

I think that the research that is being done on autonomous systems — autonomous cars, autonomous robots — it’s a call to humanity to be responsible. In some sense, it has nothing to do with the AI. The technology will be developed. It was invented by us — by humans. It didn’t come from the sky. It’s our own discovery. It’s the human mind that conceived such technology, and it’s up to the human mind also to make good use of it.

I’m optimistic because I really think that humanity is aware that they need to handle this technology carefully. It’s a question of being responsible, just like being responsible with any other technology every conceived, including the potentially devastating ones like nuclear armaments. But the best thing to do is invest in education. Leave the robots alone. The robots will keep getting better, but focus on education, people knowing each other, caring for each other. Caring for the advancement of society. Caring for the advancement of Earth, of nature, improving science. There are so many things we can get involved in as humankind that could make good use of this technology we’re developing

The idea that AI will conjure up an apocalyptic, robot-ruled future, where mechanical overlords govern humans is an extremely low probability event, even in the very distant future. In fact, not only are AI-driven interventions accelerating business outcomes – AI is also helping nations around the world find new avenues for enabling positive social outcomes for their people. 

For all the evolution and development of humanity and technology over the years, our world still faces pressing systemic challenges that affect humanity at a large scale. From our complex and labyrinthine legal systems to the inefficiencies in our healthcare sector, large-scale problems still abound. The need of the hour is to better connect the people with the basic facilities they require. AI may not be a panacea in and of itself, but it offers a huge potential to improve the quality of life of people across the globe. Thankfully, today multiple nations have the intellectual capital – our peers in the software engineering and AI domains – that can bring substantial dividends for the population at large.

In this article, I will attempt to touch upon how AI can be used to address large, complex and unsolved problems and contribute to improving the quality of life for humanity. In keeping with WTISD’s topic for this year – Enabling the Positive Use of AI for All – I’ll share a social perspective on how AI-powered innovations can be hugely transformational to the world: 

Improve Access to Healthcare Facilities

Available statistics show that over 45% of WHO Member States report to have less than 1 physician per 1000 people. (World Health Organization recommends a ratio of 1:1000). When you add to that the inequitable spread of doctors across certain countries, we have a poorly served population. While the life expectancy at a global level is 72 years (average across both males and females), the disparity between regions can be startling. For instance, the average in the WHO’s Africa region is a low 61.2 years. By imbibing AI, we can deliver an exponential improvement in health outcomes by improving medical adherence to reduce readmissions, tracking patient medical histories, improving access to physicians, reducing the time spent in clinics and prescribing personalized treatment pathways. Using AI, we can:

• Identify high population density areas that are currently underserved by hospitals. This can provide policymakers with inputs on how they can improve the deployment and availability of doctors, medical equipment and medication
• Leverage early warning signals through alternative mediums such as social media tracking for public health studies to provide guided diagnosis and interventions
• Create a digital record of patients’ medical histories and their clinical notes and use that as a reference for prescribing evidence-based treatment options and developing tailored treatment pathways
• Improve patient medical adherence by identifying individuals without health insurance, providing coverage and incentivizing the use of appropriate medication and treatment
• Speed up routine clinical processes such as scanning and annotating X-Rays and CT-Scans using computer vision and prescribing actions to physicians.

Revamp the Education System

The education system is undeniably critical for shaping future generations. However, both quality of and access to education is incredibly disparate across the developed and developing worlds. Curricula can often be outdated, thus not providing students with the skills they need for their careers. Problems abound in the education sector – from a high level of student dropouts, quality and methodology of teaching and lack of workforce readiness among students. While policymakers mull over how education can be made more contemporary and effective, AI can help provide guided interventions in the field of education by:

• Mapping the aptitude and interest of students in schools and universities with skills that are demanded by the market. This will help provide prescriptive career guidance that will be beneficial to both the employers and the future workforce
• Tracking the demand for skills in the market and the educational infrastructure available to supply those skills, through a Skills Repository. This will help keep education concurrent with current market demands and ensure much better alignment between academia and corporates
• Automate routine, time-consuming tasks – from creating and grading test papers, developing personalized benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject, and enabling teachers to focus on curriculum development, coaching and mentoring, and improving behavioral and personality aspects of students
• Identify potential school and university-level dropouts and their root-causes so educational institutions can take proactive steps to ensure student retention and course completion.

Address Legal and Law Enforcement Challenges

Globally, we face structural issues in areas of law enforcement and jurisprudence. Globally the average police-to-people ratio is 1 police personnel per 604 people, which is lower than the UN recommended standard of 1 per 454. Poor law enforcement eventually lends itself to a high crime rate and an overburdened legal system. AI can be a hugely pertinent gamechanger for global governance systems and help law enforcement officers improve surveillance by augmenting police efforts, automate a variety of routinized legal tasks and improve transparency in governance. By bringing the potential of AI in law enforcement, we can offer:

• Surveillance and identification of wrongdoers; areas recognized for high criminal activity can be done through computer vision
• Review and summary-creation of long drawn cases and their history can be done through natural language processing and voice recognition
• Routing Right-to-Information and governance-related citizen requests through intelligent bots, thus making it more efficient to get critical information
• Employ Anomaly Detection frameworks to surface fraudulent transactions – especially among land deals.

A global population of over 7.7 billion people, distributed across a huge landmass throws up a sizeable challenge when it comes to scalability. With many individual nations crippled by the inability to serve their populations, their public services need technology-centric solutions that are scalable and intelligent at the same time. Artificial intelligence will effectively address a number of these problems which are of socio-economic importance, and will go a long way in improving the quality of life of humanity at large. To enable this, public services need to act sooner rather than later and ramp up their data sets, identify and onboard technology, innovation and research partners for ideating and applying AI techniques that can power humanity’s next big leap. 

Global Capability Centers(GCC’s) are at a pivotal turning point as the pace at which digitization is changing every aspect is fast paced and agile. The rapid transformation and innovation of GCC’s today is driven by new age or exponential technologies :AI, internet of things (IoT), blockchain, cloud computing , RPA, Cyber security. Exponential technologies are seen to double their performance every couple of years while reducing their costs in half. In recent times; GCC story is in a changing era of value and transformative arbitrage. Most of the GCCs are aiming towards deploying suite of exponential technologies :RPA, Blockchain, IoT, AI to get into digital play. It is widely predicted that exponential technologies will disrupt and transform capability centers in the coming decades.

This blog aims to demystify emerging exponential technologies and examine the developing role that it could play in both the immediate and long-term future of GCC’s. From applying AI to exploring how blockchain could be used to transform businesses, we will envision ways to apply and adopt exponential technologies to GCC related challenges.

Cloud Based Digital Transformation

Big Data technology and cloud computing are widespread across the globe GCC’s are finding the right way to use it, so they can accomplish their business goals. As automation drives businesses, insights derived from big data analytics are like a data mine for businesses to make data-driven decisions. The onset of big data and cloud has led changing job roles and responsibilities in GCC such as BI/BD engineers, Cloud Architects, BI/BD Solutions Architects, Data Visualization Developer.

Automation, RPA for GCC’s

GCC’s today are rapidly adopting robotic process automation. The aim for the workforce is to focus more on value added tasks. Automation value can be leveraged when Cognitive strikes convergence with RPA and enable autonomous decision making, understanding natural language, self-learning and ability to handle scenarios that entail unstructured data and complex decision making.

Automation is seen as the current and huge opportunity in GCC’s. It has a huge potential in its ability to capture the rule-based market. Robotic Process Automation are delivered as virtual Robots, tools, or a set of scripts, an error free enabled automated process. Some of the emerging roles in this area include RPA developer, Deployment engineer

Blockchain

Increased collaboration between businesses, GCC’s and tech vendors unlock the power of blockchain across multiple use cases. Given its immutable and decentralized nature, blockchain will be invaluable in sectors such as manufacturing, supply chain and financial services and we will see innovative use cases coming out of these domains

Within blockchain, smart contracts specifically will gain immense traction. The business value of smart contracts is remarkably clear – they drastically reduce the time and effort for routine but lengthy paperwork processes, while maintaining the sanctity through a blockchain network.

Blockchain development is reshaping the GCC environment with  emerging distributed ledger technology. This requires niche skill sets and roles such as Blockchain developers/engineers, Blockchain legal consultant

Artificial Intelligence Predominance

AI’s ability to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025. The AI foundation consists of numerous technologies and techniques that have grown over many years: recommendation systems, decision trees, linear regression and neural networks impacting the next-gen GCC’s.

Following core trends in AI will dominate across GCC;s:

Adoption of “plug and play”, as-a-service solutions in AI for organizations with less than global-scale resources to think about integrating narrow AI.

Enterprise Conversational AI will see mainstream adoption and will look to add voice enabled interfaces to their existing point-and-click dashboards and systems.

AI and machine learning continue to be the most penetrable technology trends within GCC’s. The capability centers are adopting software tools that are enabled with machine learning and AI capabilities to eliminate manual intervention. the emerging job titles and roles evolve as Data scientists, Statisticians.

Internet of Things (IoT)

As capability centers are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.

The rise of exponential technologies and the need to stay upbeat with it, allows scope for the changing landscape of GCC’s through new opportunities and roles. Technologies :cloud computing, cyber security , AI , blockchain, robotics process automation (RPA) will continue to be in the fore front of this changing landscape. The GCC’s will continue to directly boost the need for skills on the exponential technologies front . Time for GCC heads and talent acquisition leaders to revamp their business and talent strategies .

Artificial intelligence is getting ubiquitous and is transforming organizations globally. AI is no longer just a technology. It is now one of the most important lenses that business leaders need to look through to identify new business models, new sources of revenue and bring in critical efficiencies in how they do businesses.

Artificial intelligence has quickly moved beyond bits and pieces of topical experiments in the innovation lab. AI needs to be weaved into the fabric of business. Indeed, if you see the companies leading with AI today, one of the common denominators is that there is a strong executive focus around artificial intelligence. AI transformation can be successful when there is a strong mandate coming from the top and leaders make it a strategic priority for their enterprise.

Given AI’s importance to the enterprise, it is fair to say that AI will not only shape the future of the enterprise, but also the future for those that lead the enterprise mandate on artificial intelligence.

Curiosity and Adaptability

To lead with AI in the enterprise, top executives will need to demonstrate high levels of adaptability and agility. Leaders need to develop a mindset to harness the strategic shifts that AI will bring in an increasingly dynamic landscape of business – which will require extreme agility. Leaders that succeed in this AI era will need to be able to build capable, agile teams that can rapidly take cognizance of how AI can be a game changer in their area of business and react accordingly. Agile teams across the enterprise will be a cornerstone of better leadership in this age of AI.

Leading with AI will also require leaders to be increasingly curious. The paradigm of conducting business in this new world is evolving faster than ever. Leaders will need to ensure that they are on top of the recent developments in the dual realms of business and technology. This requires CXOs to be positively curious and constantly on the lookout for game changing solutions that can have a discernible impact on their topline and bottom-line.

Clarity of Vision

Leadership in the AI era will be strongly characterized by the strength and clarity with which leaders communicate their vision. Leaders with an inherently strong sense of purpose and an eye for details will be forged as organizations globally witness AI transformation.

It is not only important for those that lead with AI to have a clear vision. It is equally important to maintain a razor sharp focus on the execution aspect. When it comes to scaling artificial intelligence in the organization, the devil is very often in the details – the data and algorithms that disrupt existing business processes. For leaders to be successful, they must remain attentive to the trifecta of factors – completeness of their vision for AI transformation, communication of said vision to relevant stakeholders and monitoring the entire execution process. While doing so, it is important to remain agile and flexible as mentioned in my earlier section – in order to be aware of possible business landscape shifts on the horizon.

Engage with High EQ

AI transformation can often seem to be all about hard numbers and complex algorithms. However, leaders need to also infuse the human element to succeed in their efforts to deliver AI @ Scale. The third key for top executives to lead in the age of AI is to ensure that they marry high IQs with equally or perhaps higher levels of EQ.

Why is this so very important? Given the state of this technology today, it is important that we build systems that are completely free of bias and are fair in how they arrive at strategic and tactical decisions. AI learns from the data that it is provided and hence it is important to ensure that the data it is fed is free from bias – which requires a human aspect. Secondly, AI causes severe consternation among the working population – with fears of job loss abounding. It is important to ensure that these irrational fears of an ‘AI Takeover’ are effectively abated. For AI to be successful, it is important that both types of intelligence – artificial and human – symbiotically coexist to deliver transformational results.

AI is undoubtedly going to become one of the sources of lasting competitive advantage for enterprises. According to research, 4 out of 5 C-level executives believe that their future business strategy will be informed through opportunities made available by AI technology. This requires a leadership mindset that is AI-first and can spot opportunities for artificial intelligence solutions to exploit. By democratizing AI solutions across the organization, enterprises can ensure that their future leadership continues to prioritize the deployment of this technology in use cases where they can deliver maximum impact.

The age of Artificial Intelligence is upon us. Businesses and society are now looking towards AI for transformative outcomes. Businesses specifically are investing huge amounts of money on AI technology that will not only bring in efficiencies across multiple processes, but also unlock new revenue streams that will deliver quantum bottom-line impact. With the AI transformation playing out rapidly in our personal and professional lives, we need to deeply understand what the future of work will look like in the age of AI.

Within the business organization, there is a huge need to ramp up skill development interventions. The traditional roles of employees in an organization are rapidly changing as they are expected to stay in step with the developments in the world of AI. Business executives are now needed to deeply understand the potential of Artificial Intelligence and translate it into a viable roadmap for their business. Technology leaders need to take centre-stage in how their organizations adopt and harness the power of AI. The CIO is now fast becoming the key custodian of the most valuable resource in business today i.e. data. We are seeing a fast proliferation of digital evangelists and transformation officers who are charged with developing a framework within which the future of the organization will operate.

Ushering the Future of Work

On a tactical level, the burning question now is how subjects such as Data Science, Artificial Intelligence and Machine Learning can be infused in the career pathways of existing employees. How can organizations can build a steady pipeline of future talents with expertise in AI? Mastery of exponential technologies (AI, cloud computing, blockchain, IOT, cybersecurity etc.) will be remarkably important for both business and technical professionals. It is critical that transformation leaders and digital evangelists are well-versed in building internal capabilities that converge around the nexus of technology competencies, managing a hybrid workforce and ensuring the adoption and dispersion of AI.

For us to usher in the future of work powered by Artificial Intelligence, we need to ensure that a few key enablers come together. We need to expand the scope of executive education and the courseware that goes with it. Next, we need to seriously consider the potential impact of shorter, tactical courses. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization. Finally, we need to reimagine multiple, personalized career pathways. We need to move away from the traditional one-size-fits-all training and deliver more tailored, fit-for-purpose and relevant education to employees. Here are the three critical interventions for the business and technology leaders to execute in order to usher in the future of work that is enabled by AI.

1.Develop New Age Skills and Competencies in AI Technology

Upgrading the technology competencies and skills of business and technology leaders and their teams seems like the most critical first step. With the landscape of technology is rapidly evolving, we need to urgently upskill the present and future workforce to ensure a quality supply of talent. We need new age coursework in computer science that can hugely develop the ability of students in subjects such as Artificial Intelligence Machine Learning, Deep Learning, Natural Language Processing and other AI related concepts. On a broader scale, we also need Universities and colleges to improve the existing knowledge-base of AI enabling technologies such as Cloud, DevOps, Blockchain etc as well for the workforce.

At present we see a decent level of advancement in the field of computer science training and education. However, other trades within the technical area which also require to be upgraded as well. By doing so, we will be able to ensure a wholesome and future-proof education for the aspirants who wish to build their careers in the world of AI. For instance, students studying for a major in the field of electronics could shape their focus on mastering AI-enabling technologies such as GPUs and Quantum Computing. The students presently pursuing a specialization in mechanical engineering could achieve some level of sophistication in allied subjects of robotics and 3D Printing. Subject matter experts in the fields of industrial engineering, operations and supply chain would also do well to extend their skill sets to machine learning and blockchain as well thus creating a convergence of their interest areas and realities of the market – which will empower them with the required tools to succeed in the workplace of the future.

2. Reimagining the Process of Developing of New Age Technology

This interventions pertains to the embedding the design in the process of development and user adoption of AI technology. A commonly held misconception around design of a product or software is that it is restricted to simply the look or feel of the product or software. This is simply not true. As a Steve Jobs once proclaimed – Design is not just what is looks like and feels like. Design is how it works.

For the growth of AI to live up to the hype, we need to reimagine the process by which we develop new age technology. We need to build design into the fabric of the development and engagement process to ensure that the conceived idea is brought to fruition. Transformation evangelists aiming to spearhead the future of work should treat design as the creative process that aids the development of breakthrough products.

We are already seeing several inroads that design frameworks such as Human Centered Design and Empathy-led Design are making in the technology realm. These frameworks not only guide the development process, but also the user experience of the final software / hardware being developed. These frameworks do so by putting the user at the center of the journey.

3.Managing the ‘People’ of the Future Workforce

 As I mentioned before the understanding of traditional roles in the future of work is rapidly changing. New roles are also emerging where data custodians and algorithm at scale engineers are put to work to develop the technology that powers the business of the future. On the macro level, we are seeing rapid changes in the paradigm of staffing as well. With the gig economy in full force, we are seeing more dynamic team compositions – where individuals with varied skill sets are required to continuously augment teams on a need basis. Advances in the fields of technology and management typically ordain large-scale transformation in the manner in which organizations manage their workforce.

On the micro level we are seeing that increased instances of automation are requiring managers to build and scale blended teams comprising humans and AI. This disruption requires a paradigm shift how the future workforce is managed. Teams in the future will showcase increased diversity and will be more interdisciplinary than ever before. Managing teams, careers and coaching for improved performance in the future will require a new set of metrics. Change evangelists need to devise these metrics – which will be imperative to how the workforce of the future is managed.

New technologies will require new approaches to project management and staffing. To ensure the supply of these critical skills, we also need courses that provide an education of subjects such as people management.

Our very understanding of our workplace is being rapidly disrupted. Increasingly a convergence of the right people, process and technology is required to unearth insights from a seemingly exponentially increasing size of data. To turn this data into actionable intelligence that powers business processes must be the focus of business and technology leaders – as well as educationists that build the talent pipeline for the future. Academia is required to urgently intervene and provide theoretical and practical training in AI subjects to both the existing workforce and the future pipeline of talent. We also need a dispersion of soft skills that will enable and evangelize this change. With growing interest and appreciation of technologies and platforms around Artificial Intelligence and the Digital Workplace, organizations need to ask tough questions of themselves. The time is now to consider the various forces at play. With increased AI augmentation and the transformation of processes and people that enable it, the topic of the Future of Work requires immediate and urgent attention.