The extra ordinary promise of AI : Global & Indian enterprises have a lot to gain from unleashing innovation with AI —but harnessing their potential demands focused investment and a new way of working with external partners.

Here are few salient features of how AI has become game changing trend in spurring innovation; existing challenges and few strategic approaches of unlocking innovation with AI :

• 22% growth : From 2015 through 2019, disclosed private investment in seven deep tech sectors grew an average of 22% per year, equaling nearly $60 billion in total investment. Corporate venture capital is also playing an increasingly active role.
• Total investment : Nearly $60 Billion Invested in Deep Tech’s Fastest-Growing Sectors in 2019; Artificial intelligence corners close to $25 Bn
• About 1800 AI led startups in the US accounted for roughly half of this total investment, but other countries are catching up fast.

Existing Challenges

• Complex ecosystems : Multiple types of players including startups, venture capital firms, governments, universities and research centers, and early-adopter user groups
• Dynamic Interactions : Few central orchestrators; business relationships based on informal networks rather than formal contracts

Strategic approaches of unlocking innovation with AI :

• Cooperate in order to compete : Think beyond the enterprise’s immediate goals; commit to a long-term vision for the development of the ecosystem as whole
• Identify capabilities that add value : Define what the enterprise can offer to nurture the ecosystem and bring AI to market—not only money but also access to customers, data, networks, mentors, and technical experts
• Don’t pick winners in advance : AI startups are evolving rapidly. Continuously monitor the ecosystem to identify successful startups, applications, and business models as they emerge
• Blur the boundaries with partners : Make it easy for AI partners to navigate your corporate system. Define a clear role for them in your innovation strategy, ensure senior-executive sponsorship, and engage the core businesses
• Streamline decision making and governance : Success requires partnering more nimbly with fast-moving AI startups. Embrace agile ways of working.
• Develop breakthrough solutions by combining expertise from previously unconnected fields or industries. Be alert for game hanging opportunities that deliver both economic and social value.

AI will transform business and society in the future. The time to craft a AI strategy for unleashing innovation is now.

AIQRATE works closely with global & Indian enterprises , GCCs , VC/PE firms and has an extensive yet curated database of 1000 + global AI startups , boutique and niche firms benchmarked on our “Glow Curve” assessment.

(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 ; reach out to us at consult@aiqrate.ai )

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.

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

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 .

The Artificial Intelligence revolution in the enterprise is well under way. According to Gartner’s 2018 CEO and Senior Business Executive Survey, 65% of respondents think that AI will have a ‘material impact on an area of their business’. Due to the combination of three critical factors – improved data availability and machine learning techniques, increased computing power and storage, and a strong enterprise thrust on data-driven decision-making – AI has taken a strong foothold in some of the largest corporations in the world today, commanding executive-level interest, attention and urgency.

Beyond simple automation, AI is powering complex, critical decisions in several areas from Renaissance Technologies’ Medallion Fund, which uses statistical probabilities and quantitative models and has become one of the startling successes in the hedge fund industry, to complex image annotation and deep learning that helps radiologists detect cancer in MRI scans. Here is a look at some of the critical areas where AI is augmenting human decision-making:

Healthy Healthcare

As multiple countries grapple problems from an ageing population, rising healthcare costs and low doctor-to-patient ratios, AI can help improve healthcare outcomes in a variety of ways. For instance, AI is being leveraged for public health studies – from detection of potential physical or psychological pandemics to epidemiology – by mining social media and other data sources.

Further, startups and conglomerates are working on AI for diagnostics – from detection of early warning signals to identifying and quantifying abnormalities/tumours. In the pharma industry, AI is helping improve site studies, drug development and clinical trials through analysis of meaningful data.

Financial Services

A common AI use case for financial services is in the domain of fraud detection and anti-money laundering. AI can help surface bad actors by quickly scanning data for anomalous behaviour. Similarly, AI is also powering customer interaction decisions through intelligent chatbots that can address common concerns, thus reducing the need for human intervention in repetitive, menial tasks. We’re also seeing increased proliferation of robo-advisers – which are advanced AI tools that help make investment decisions by matching investible capital and returns expected.

Managing Media

The media and entertainment industry is going through an AI and digital disruption due to the combination of huge datasets and success of torchbearers like Netflix and Spotify. Content recommendation and personalisation are decisions that are autonomously delivered by AI, which can quickly scan a user’s history and match it with the preferences of similar users.

The industry is also relying on AI to make decisions around content creation, again taking a leaf out of Netflix to make content more engaging and sticky. There is also a strong use case of AI helping identify and attract customers by surfacing tailored content and promotions to increase subscriptions, loyalty and share-of-wallet.

Retail Rejig

Retail was one of the first industries to witness the rise of a data-powered competitor that eventually decimated incumbents. The brick-and-mortar retail industry is now incorporating AI in its decision-making process to replicate the customer experience expectations set by Amazon and the like.

Retailers leverage user purchase to identify next-best product and create tailored loyalty programmes. It is also being increasingly used for rapid experimentation to define store location, layouts and product-shelf decisions. Retailers can better anticipate demand, leading to leaner supply chains and warehouses, optimised inventory and fewer stockouts.

Efficient Manufacturing

Manufacturing companies are bringing in AI interventions to run leaner supply chains to cut the cost of transportation and wastage. AI also enables them to better anticipate demand by looking at historical sales, current uptake and other business environment factors to run on-demand production.

Some AI-led decisions are pervasive across multiple industries. For instance, digital personalisation, ie, serving targeted promotions to customers based on their key purchase drivers, is a multi-industry example of AI in action.

The other is for detecting security threats through anomaly detection and video analytics to identify unauthorised entry. Human Resources is another function that is rapidly changing, with companies using AI to speed up talent acquisition by scanning resumes for relevancy and reducing attrition by identifying key drivers that lead to employees leaving.

Successful AI-led Decisions

The business value of AI is significantly lowered when performed ad hoc, without a strong foundational strategy. It is important that the organisation clearly defines the decisions that should be powered by AI to maintain a high standard of outcomes. The responses will differ from company to company and from industry to industry, but it is important that corporations establish transparent standards for fair use.

We see enough examples of hastily implemented AI, leading to calamitous consequences and companies can no longer hide by saying, ‘The AI made me do it’. To demarcate the clear go and no-go zones for AI, here’s a handy questionnaire to ask yourself:

– Do we have enough superior quality data now and in the future for AI to make the best decision?
– Do we need to bring in insights from multiple sources to contribute to the decision-making process at a speed and scale, which cannot be efficiently handled by human cognition?
– Is human decision-fatigue or bias currently creating a sub-optimal outcome in this area?
– Could there be ethical or moral implications to an AI-led decision that might lead to disastrous consequences?

We also need to address the confidence issues. For instance, a lot of executives look down upon some of the black-box processes performed by AI algorithms. We need to find a way to address these issues by creating a transparent trail of AI decisions and the reasons why AI took a decision. Even in unsupervised learning scenarios, a trail of decisions will not only boost confidence but will also help build better AI and better businesses.

Re-imagined AI-powered decisions will become de rigueur only by the quality of the outcomes they deliver. According to Dr John Kelly, SVP — IBM Research and Solutions portfolio, “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.” This is a crucial way to look at and measure the impact of AI on our businesses, society and lives.

For the past few years, Artificial Intelligence has initiated unlocking value gains through the automation and augmentation of routinized operational activity. But are we underestimating the potential of machine intelligence? Does it make sense to relegate a powerful technology to perform tactical tasks? Or can AI move further upstream and help corporate boards make more accurate, strategic decisions?

The possibility of AI to enable better decision-making has been heavily discounted thus far. However, with Artificial Intelligence capably enabling more informed decisions in the realm of healthcare and investment banking – two of the most complex arenas where AI has been deployed – the possibility of having machine cognition in the boardroom no longer sounds too far-fetched. At the end of the day, corporate boards make complex decisions, that have huge ramifications for the future of their organizations. It is important that these decisions are based in fact, rather than judgement. AI can help corporate boards make faster, more accurate and unbiased decisions. AI can help inform strategy by giving executives a better understanding of their internal and external environments. Let us look at some key areas where senior executives in organizations can look at making better decisions using Artificial Intelligence.

AI for Executive Decision-Making

Corporate boards and top executives are charged with maintaining the health and competitiveness of an organization. They are responsible for the long-term sustainability and success of their organizations. This, in turn, requires them to stay ahead of the curve and understand their business landscape and intelligently deploy capital across inorganic and organic growth channels. Executives also own the key metrics for their organizations – and ensure that the overall return for the shareholder capital employed continuously beats industry expectations. Let us look at how AI can help transform the activity of executives in these areas.

Executive leaders often rely on numerous reports around key organizational metrics to make decisions that can have massive implications for their businesses. Is a particular segment of the business growing rapidly? Are some cost centers underperforming on their efficiency metrics? Are there laggards in the product portfolio of the enterprise that are dragging performance down? All these numbers have to figuratively be at the tip of an executive’s tongue – so that in key meetings decisions that affect the future of the business can be made more accurately and quickly.

AI-powered smart assistants would be extremely critical to help push the needle on making executive decisions with accuracy and speed. With intelligent bots, executives can be provided updates on the most critical metrics that they care for at the right time when they need them. With AI, it is possible to personalize the insights that are sent to executives – so that they are able to drill down and understand the basis for each metric.

Unbiased Capital Allocation on R&D and M&A Activities

Corporate boards and executives also need to take the long term view of how their companies evolve to thrive in the future. This requires intelligent bets to be taken on budgetary spending – for both organic and inorganic activities. How much money needs to be realistically spent on Research and Development activity and how it can it help corporations maintain larger moats against their competition? Can corporations look at inorganic acquisitions to accelerate the growth of synergistic capabilities that can form much more compelling value propositions?

AI will soon be able to provide comprehensive answers to such questions. By leveraging data from multiple sources combined with intelligent algorithms, AI will be able to weigh these multiple options and identify which one is best suited for each unique situations. In this way again, AI can help executives forecast which decisions can have maximum impact on financial metrics and model the long-term health of the organization.

As corporate boardrooms take serious cognizance of having robotic counterparts augmenting the decision-making process, it is important to consider certain caveats. For AI to work to its full potential, it is important to ensure that it is provided high quality data and continuously refined algorithms. We have seen the fallouts of algorithms going awry before. Biased algorithms working off bad data sets create issues that could potentially disrupt the fabric of the organization. It is therefore important that organizations ensure the implementation of explainable AI that can provide the rationale and take accountability of the decisions that it powers. Finally, it is important that executive leaders also create the right culture within their organizations for AI to thrive. A combination of human intelligence and artificial intelligence is the future and hence it is critical that companies relook at their culture to ensure that both can amicably survive together and put the organization on the right path.

According to research by McKinsey, it is estimated that 16 percent of board of directors did not fully understand how the dynamics of their industries were changing and how new technologies could impact their businesses. This gives AI a huge window of opportunity to permeate through global boardrooms and power better decisions. Decisions that can keep their organizations financially healthy, focused on the long-term and competitively differentiated against their competitors.