At the enterprise level, AI assumes enormous power and potential , it can disrupt, innovate, enhance, and in many cases totally transform businesses . Multiple reports predicts a 300% increase in AI investment in 2020-2022 and estimates that the AI market amongst several exponential technologies will be the highest . There are solid instances that the AI investment can pay off—if CEO’s can adopt the right strategy. Organizations that deploy AI strategically enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, enhanced customer experiences, and working-capital optimization. Multiple surveys also shows that the companies winning at AI are also more likely to enjoy broader businesses.

So How to make your Enterprise AI Ready?

72 % of the organizations say they are getting significant impact from AI. But these enterprises have taken clear, practical steps to get the results they want. Here are five of their strategic orientation to embark on the process to make AI Enterprise Ready :

• Core AI A-team assimilation with diversified skill sets
• Evangelize AI amongst senior management
• Focus on process, not function
• Shift from system-of-record to system-of-intelligence apps, platforms
• Encourage innovation and transformation

Core AI A-team assimilation with diversified skill sets

Through 2022, organization using cognitive ergonomics and system design in new AI projects will achieve long term success four times more often than others    

With massive  investments in AI startups in 2021 alone, and the exponential efficiencies created by AI, this evolution will happen quicker than many business leaders are prepared for. If you aren’t sure where to start, don’t worry – you’re not alone. The good news is that you still have options:

• You can acquire, or invest in a company applying AI/ML in your market, and gain access to new product and AI/ML talent.
• You can seek to invest as a limited partner in a few early stage AI focused VC firms, gaining immediate access and exposure to vetted early stage innovation, a community of experts and market trends.
• You can set out to build an AI-focused division to optimize your internal processes using AI, and map out how AI can be integrated into your future products. But recruiting in the space is painful and you will need a strong vision and sense of purpose to attract and retain the best.

Process Based Focus Rather than Function Based

One critical element differentiates AI success from AI failure: strategy. AI cannot be implemented piecemeal. It must be part of the organization’s overall business plan, along with aligned resources, structures, and processes. How a company prepares its corporate culture for this transformation is vital to its long-term success. That includes preparing talent by having senior management that understands the benefits of AI; fostering the right skills, talent, and training; managing change; and creating an environment with processes that welcome innovation before, during, and after the transition.

The challenge of AI isn’t just the automation of processes—it’s about the up-front process design and governance you put in to manage the automated enterprise. The ability to trace the reasoning path AI use to make decisions is important. This visibility is crucial in banking &  financial services, where auditors and regulators require firms to understand the source of a machine’s decision.

Evangelize AI amongst senior management

One of the biggest challenges to enterprise transformation is resistance to change. Surveys have found that senior management is the inertia led to AI implementation. C-suite executives may not have warmed up to it either. There is such a lack of understanding about the benefits which AI can bring that the C-suite or board members simply don’t want to invest in it, nor do they understand that failing to do so will adversely affect their top & bottom line and even cause them to go out of business. Regulatory uncertainty about AI, rough experiences with previous technological innovation, and a defensive posture to better protect shareholders, not stakeholders, may be contributing factors.

Pursuing AI without senior management support is difficult. Here the numbers again speak for themselves. The majority of leading AI companies (68%) strongly agree that their senior management understands the benefits AI offers. By contrast, only 7% of laggard firms agree with this view. Curiously, though, the leading group still cites the lack of senior management vision as one of the top two barriers to the adoption of AI.

The Dawn of System-of-Intelligence Apps & Platforms

Analysts report predicts that an Intelligence stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future.

By 2022, AI platform services will cannibalize revenues for 30% of market leading companies    

It will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behaviour, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

The Process of Supporting Innovation

Besides developing capabilities among employees, an organization’s culture and processes must also support new approaches and technologies. Innovation waves take a lot longer because of the human element. You can’t just put posters on the walls and say, ‘Hey, we have become an AI-enabled company, so let’s change the culture.’ The way it works is to identify and drive visible examples of adoption. Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the top AI uses cases by 2025. It is forecasted that predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years.

In the End, it’s about Transforming Enterprise

AI is part of a much bigger process of re-engineering enterprises. That is the major difference between the automation attempts of yesteryear and today’s AI: AI is completely integrated into the fabric of business, allowing private and public-sector organizations to transform themselves and society in profound ways. Enterprises that will deploy AI at full scale will reap tangible benefits at both strategic & operational levels.

AI is invoking shifts in the business value chains of enterprises. And it is redefining what it takes for enterprises to achieve competitive advantage. Yet, even as several enterprises have begun applying AI engagements with impressive results, few have developed full-scale AI capabilities that are systemic and enterprise wide.

The power of AI is changing business as we know it. AIQRATE AI@scale advisory services allow you to transform your operating model, so you can move beyond isolated AI use cases toward an enterprise wide program and realize the full value potential.

We have realized that that unleashing the true power of AI requires scaling it across the entire business functions and value chain and its calls for “transforming the business “.

An AI@scale transformation should occur through a series of top-down and bottom-up actions to create alignment, buy-in, and follow-through. This ensures the successful industrialization of AI across enterprises and their value chains.

The following strategic interventions are to be initiated to build AI@scale transformation program:

• AI Maturity Assessment: This strategic top-down establishes the overall context of the transformation and helps prevent the enterprises from pursuing isolated AI pilots. The maturity assessment is typically based on a blend of AI masterclass, surveys and assessments
• Strategic AI Initiatives and business value chains: This bottom-up step provides a baseline of current AI initiatives. It should include goals, business cases, accountabilities, work streams, and milestones in addition to an analysis of data management, algorithms, performance metrics. A review of the current business value chain and proposed transformational structure should also be conducted at this stage.
• Strategic mapping & gap Analysis: The next top-down step prioritizes AI initiatives, focusing on easy wins and low hanging fruits. This step also identifies the required changes to the operating business model.
• AI@scale transformation program: This critical strategic step consists of both the transformation roadmap, including the order of initiatives to be rolled out, and the creation of a planned program management approach to oversee the transformation.
• AI@scale implementation: This covers implementation, detailing the work streams, responsibilities, targets, milestones, talent and partner mapping. 

By systematically moving through these steps, the implementation of AI@scale will proceed with much greater speed and certainty. Enterprises must be aware that AI@scale requires deep transformative changes and need strategic and operational buy ins from management for long term business gains and impact . 

AIQRATE works closely with global & Indian enterprises , GCC’s , VC/PE firms to provide end-to-end AI@scale advisory services

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 )

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.

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.

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.

Introducing, the first of its kind, must have primer for CxOs, executives and professionals on executing AI and Analytics strategies in their enterprises for end-to-end transformative impact. Includes:Introducing, the first of its kind, must have primer for CxOs, executives and professionals on executing AI and Analytics strategies in their enterprises for end-to-end transformative impact. Includes:

• Exhaustive repertoire of AI and Analytics strategy roadmaps, frameworks and methodologies for CXO’s, coupled with broad exhibit plan of making the enterprises AI ready
• A comprehensive overview of the algorithm economy and its deep transformative potential of morphing enterprises into math houses
• Incisive study of C-suite stakeholders – CMO, CPO, CFO, CIO’s radical role and functional changes on strategic and operational sides underpinned by AI and Analytics infusion
• Outline of the immense AI and Analytics adoption and consumption scenarios in high impact industries of Banking, Insurance, Healthcare, Life Sciences, Retail and CPG
• Thought provoking facets of AI and Analytics pervasive interventions in exponential technologies: Chatbots , RPA , IoT , Cybersecurity , Blockchain , Cryptocurrency

Today’s artificial intelligence (AI) revolution has been made possible by the algorithm revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.

But current AI technologies are a collection of big data-driven point solutions, and algorithms are reliable only to the extent that the data used to train them is complete and appropriate. One-off or unforeseen events that humans can navigate using common sense can lead algorithms to yield nonsensical outputs.

Design thinking is defined as human-centric design that builds upon the deep understanding of our users (e.g., their tendencies, propensities, inclinations, behaviours) to generate ideas, build prototypes, share what you’ve made, embrace the art of failure (i.e., fail fast but learn faster) and eventually put your innovative solution out into the world.  And fortunately for us humans (who really excel at human-centric things), there is a tight correlation between the design thinking and artificial intelligence.

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.

Divergence from More Powerful Intelligence To More Creative Intelligence

While algorithms can automate many routine tasks, the narrow nature of data-driven AI implies that many other tasks will require human involvement. In such cases, algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. We don’t want to ascribe to AI algorithms more intelligence than is really there. They may be smarter than humans at certain tasks, but more generally we need to make sure algorithms are designed to help us, not do an end run around our common sense.

Design Thinking at Enterprise Premise

Although cognitive design thinking is in its early stages in many enterprises, the implications are evident. Eschewing versus embracing design thinking can mean the difference between failure and success. For example, a legacy company that believes photography hinges on printing photographs could falter compared to an internet startup that realizes many customers would prefer to share images online without making prints, and embraces technology that learns faces and automatically generates albums to enhance their experience.

To make design thinking meaningful for consumers, companies can benefit from carefully selecting use cases and the information they feed into AI technologies. In determining which available data is likely to generate desired results, enterprises can start by focusing on their individual problems and business cases, create cognitive centres of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowdsource ideas from employees and customers alike.

In assessing what constitutes proper algorithmic design, organizations may confront ethical quandaries that expose them to potential risk. Unintended algorithmic bias can lead to exclusionary and even discriminatory practices. For example, facial recognition software trained on insufficiently diverse data sets may be largely incapable of recognizing individuals with different skin tones. This could cause problems in predictive policing, and even lead to misidentification of crime suspects. If the training data sets aren’t really that diverse, any face that deviates too much from the established norm will be harder to detect. Accordingly, across many fields, we can start thinking about how we create more inclusive code and employ inclusive coding practices.

CXO Strategy for Cognitive Design Thinking

CIOs can introduce cognitive design thinking to their organizations by first determining how it can address problems that conventional technologies alone cannot solve. The technology works with the right use cases, data, and people, but demonstrating value is not always simple. However, once CIOs have proof points that show the value of cognitive design thinking, they can scale them up over time.

CIOs benefit from working with business stakeholders to identify sources of value. It is also important to involve end users in the design and conception of algorithms used to automate or augment cognitive tasks. Make sure people understand the premise of the model so they can pragmatically balance algorithm results with other information.

Enterprise Behavioral Science – From Insights to Influencing Business Decisions

Every January, how many people do you know say that they want to resolve to save more, spend less, eat better, or exercise more? These admirable goals are often proclaimed with the best of intentions, but are rarely achieved. If people were purely logical, we would all be the healthiest versions of ourselves.

However, the truth is that humans are not 100% rational; we are emotional creatures that are not always predictable. Behavioral economics evolved from this recognition of human irrationality. Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making.

Decision making is one of the central activities of business – hundreds of billions of decisions are made everyday. Decision making sits at the heart of innovation, growth, and profitability, and is foundational to competitiveness. Despite this degree of importance, decision making is poorly understood, and badly supported by tools. A study by Bain & Company found that decision effectiveness is 95% correlated with companies’ financial performance.

Enterprise Behavioral Science is not only about understanding potential outcomes, but to completely change outcomes, and more specifically, change the way in which people behave. Behavioral Science tells us that to make a fundamental change in behavior that will affect the long-term outcome of a process, we must insert an inflection point.

As an example, you are a sales rep and two years ago your revenue was $1 million. Last year it was $1.1 million, and this year you expect $1.2 million in sales. The trend is clear, and your growth has been linear and predictable. However, there is a change in company leadership and your management has increased your quota to $2 million for next year. What is going to motivate you to almost double your revenues? The difference between expectations ($2 million) and reality ($1.2 million) is often referred to as the “behavioral gap” . When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive can initiate an inflection point and influence a change in behavior. Perhaps that incentive is an added bonus, President’s Club eligibility, a promotion, etc.

Cognitive Design Thinking – The New Indispensable Reskilling Avenue

Artificial intelligence, machine learning, big data analytics and mobile and software development will be the top technology areas where the need for re-skilling will be the highest. India will need 700 million skilled workforce by 2022 to meet the demands of a growing economy. Hence, while there is a high probability that machine learning and artificial intelligence will play an important role in whatever job you hold in the future, there is one way to “future-proof” your career…embrace the power of design thinking.

In fact, integrating design thinking and artificial intelligence can give you “super powers” that future-proof whatever career you decide to pursue. To meld these two disciplines together, one must:

• Understand where and how artificial intelligence and behavioural science can impact your business initiatives. While you won’t need to write machine learning algorithms, business leaders do need to learn how to “Think like a data scientist” in order understand how AI can optimize key operational processes, reduce security and regulatory risks, uncover new monetization opportunities.
• Understand how design thinking techniques, concepts and tools can create a more compelling and emphatic user experience with a “delightful” user engagement through superior insights into your customers’ usage objectives, operating environment and impediments to success.

Design thinking is a mindset. IT firms are trying to move up the curve. Higher-end services that companies can charge more is to provide value and for that you need to know that end-customers needs. For example, to provide value services to banking customers is to find out what the bank’s customer needs are in that country the banking client is based. Latent needs come from a design thinking philosophy where you observe customer data, patterns and provide a solution that the customer does not know. Therefore, Companies will hire design thinkers as they can predict what the consumer does not know and hence charge for the product/service from their clients. Idea in design thinking is to provide agile product creation or solutions.

Without Design Thinking & Behavioural Science, AI Will be Only an Incremental Value

Though organizations understand the opportunity that big data presents, many struggles to find a way to unlock its value and use it in tandem with design thinking – making “big data a colossal waste of time & money.” Only by combining quantitative insights gathered using AI, machine/deep learning, and qualitative research through behavioural science, and finally design thinking to uncover hidden patterns and leveraging it to understand what the customer would want, will we be able to paint a complete picture of the problem at hand, and help drive towards a solution that would create value for all stakeholders.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo’s success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer — independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It’s not implausible to imagine that one day a “strategist in a box” could autonomously develop and execute a business strategy. We’ve spoken to CXOs and leaders who express such a vision — and companies such as Amazon and Alibaba are already beginning to make it a reality.

Business Processes – Increasing productivity by reducing disruptions

AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures,

As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. The plan was to increase the production rate of that aircraft faster than ever before. To do that, they needed to address issues like responding quickly to disruptions in the factory. Because they will happen. Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems.

AI led to rectification of about 70% of the production disruptions for Airbus, by matching to solutions used previously — in near real time.

Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

Integrated Strategy Machine – The Implementation Scope Augmented AI

The integrated strategy machine is the AI analog of what new factory designs were for electricity. In other words, the increasing intelligence of machines could be wasted unless businesses reshape the way they develop and execute their strategies. No matter how advanced technology is, it needs human partners to enhance competitive advantage. It must be embedded in what we call the integrated strategy machine. An integrated strategy machine is the collection of resources, both technological and human, that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights.

Amazon represents the state-of-the-art in deploying an integrated strategy machine. It has at least 21 data science systems, which include several supply chain optimization systems, an inventory forecasting system, a sales forecasting system, a profit optimization system, a recommendation engine, and many others. These systems are closely intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that the popularity of an item is increasing, it starts a cascade of changes throughout the system: The inventory forecast is updated, causing the supply chain system to optimize inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimization system adjusts pricing, again updating the sales forecast.

Manufacturing Operations – An AI assistant on the floor

CXOs at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. They have something called the BP well advisor that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. They  are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.

Customer-facing activities – near real time scoring

Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An’s cognitive AI capabilities including voice and imaging recognition.

AI Strategy for Different Operational Models

To make the most of this technology implementation in various business operations in your enterprise, consider the three main ways that businesses can or will use AI:

Assisted intelligence

Now widely available, improves what people and organizations are already doing. For example, Google’s Gmail sorts incoming email into “Primary,” “Social,” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides. Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks.

Assisted intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. For example, one auto manufacturer has developed a simulation of consumer behavior, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles, and the variations in those patterns for different city topologies, marketing approaches, and vehicle price ranges. The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. As the automaker introduces new cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.

Augmented intelligence

Augmented Intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do. Unlike assisted intelligence, it fundamentally alters the nature of the task, and business models change accordingly.

For example, Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behavior, but on those of the audience at large. A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos more tailored to the way they feel at any given moment. Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalization (and AI).

Autonomous intelligence

Being developed for the future, Autonomous Intelligence creates and deploys machines that act on their own. Very few autonomous intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75 percent of Nasdaq trading is conducted autonomously) and facial recognition. In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations, and perform other tasks inherently unsafe for people.

As you contemplate the introduction of artificial intelligence, articulate what mix of the three approaches works best for you.

• Are you primarily interested in upgrading your existing processes, reducing costs, and improving productivity? If so, then start with assisted intelligence, probably with a small group of services from a cloud-based provider.
• Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an augmented intelligence approach, probably with more complex AI applications resident on the cloud.
• Are you developing a genuinely new technology? Most companies will be better off primarily using someone else’s AI platforms, but if you can justify building your own, you may become one of the leaders in your market.

The transition among these forms of AI is not clean-cut; they sit on a continuum. In developing their own AI strategy, many companies begin somewhere between assisted and augmented, while expecting to move toward autonomous eventually.