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There’s little doubt that Artificial Intelligence (AI) is driving the decisive strategic elements in multiple industries, and algorithms are sitting at the core of every business model and in the enterprise DNA. Conventional wisdom, based on no small amount of research, holds that the rise of AI will usher radical, disruptive changes in the incumbent industries and sectors in the next five to 10 years.
Additionally, it’s never been a better time to launch an AI venture. Investments in AI-focused ventures have grown 1800% in just six years. The rationale behind these numbers comes, in part, from the fact that enterprises expect AI to enable them to move into new business segments, or to maintain a competitive edge in their industry.
Strategists believe this won’t come as a surprise to CXOs and decision-makers as acceleration of AI adoption and proliferation of smart, intuitive and ML algorithms spawn the creation of new industries and business segments and overall, trigger new opportunities for business monetization. However, a few questions loom large for CXOs: How will these new industries and business segments be created with AI? And, what strategic shifts can leadership make to monetize these new business opportunities?
The creation of new industries and business segments depends on dramatic advances in AI that can take a swift adoption journey to move from discovery to commercial application to a new industry. New industry segments around AI are in the making and are far from tapped. A cursory look at new age businesses: Micro-segmented, hyper-personalized online shopping platforms, GPS driven ride-sharing companies, recommendation-driven streaming channels, adaptive learning based EdTech companies, conversational AI-driven new and work scheduling are just a few of the imminent and visible examples. Yet a lot more can be done in this space.
AI adoption brings intentional efforts to adapt to this onslaught of algorithms and how it’s affecting customer and employee behavior. As algorithms become a permanent fixture in everyday life, organizations are forced to update legacy technology strategies and supporting methodologies to better reflect how the real world is evolving. And the need to do so is becoming increasingly obligatory.
On the other side, traditional and incumbent enterprises are reverse engineering investments, processes, and systems to better align with how markets are changing. Because it’s focusing on customer behavior, AI is actually in its own way, making businesses more human. As such, Artificial Intelligence is not specifically about technology, it’s empowered by it. Without an end in mind, self-learning algorithms continually seek out how to use technology in ways that improve customer experiences and relationships. It also represents an effort that introduces new models for business and, equally, creates a way of staying in business as customers become increasingly aware and selective.
Today, AI expertise is focused more on developing commercial applications that optimize efficiencies in existing industries and is focused less on developing patented algorithms that could lead to new industries. These efficiencies are accelerating the sectoral consolidation and convergence, and are less about new industry creation.
However, AI’s most potent, long-term economic use may just be to augment the discovery and pursuit of solving large, complex and unresolved problems that could be the foundations of new industry segments. Enterprises have started realizing the significance of having a long-term strategic interest and investments in using AI in this way. Yet few of the above mentioned examples are testimony to AI triggering new industry segments and business opportunities. The real winners in the algorithm-driven economy will be business leaders that align their strategies to augment AI expertise from ground zero, keep a continuous tab on blockbuster algorithms, and redefine new business segments that enable monetization of new opportunities.
AI has immense potential to jumpstart the creation of new industries and the disruption of existing ones. The curation of this as a strategic roadmap for business leaders is far from easy, but it carries great rewards for businesses. It takes a village to bring about change, and it also takes the spark and perseverance of an AI strategist to spot important trends and create a sense of urgency around new possibilities.
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The idea that AI will conjure up an apocalyptic, robot-ruled future, where mechanical overlords govern humans is an extremely low probability event, even in the very distant future. In fact, not only are AI-driven interventions accelerating business outcomes – AI is also helping nations around the world find new avenues for enabling positive social outcomes for their people.
For all the evolution and development of humanity and technology over the years, our world still faces pressing systemic challenges that affect humanity at a large scale. From our complex and labyrinthine legal systems to the inefficiencies in our healthcare sector, large-scale problems still abound. The need of the hour is to better connect the people with the basic facilities they require. AI may not be a panacea in and of itself, but it offers a huge potential to improve the quality of life of people across the globe. Thankfully, today multiple nations have the intellectual capital – our peers in the software engineering and AI domains – that can bring substantial dividends for the population at large.
In this article, I will attempt to touch upon how AI can be used to address large, complex and unsolved problems and contribute to improving the quality of life for humanity. In keeping with WTISD’s topic for this year – Enabling the Positive Use of AI for All – I’ll share a social perspective on how AI-powered innovations can be hugely transformational to the world:
Improve Access to Healthcare Facilities
Available statistics show that over 45% of WHO Member States report to have less than 1 physician per 1000 people. (World Health Organization recommends a ratio of 1:1000). When you add to that the inequitable spread of doctors across certain countries, we have a poorly served population. While the life expectancy at a global level is 72 years (average across both males and females), the disparity between regions can be startling. For instance, the average in the WHO’s Africa region is a low 61.2 years. By imbibing AI, we can deliver an exponential improvement in health outcomes by improving medical adherence to reduce readmissions, tracking patient medical histories, improving access to physicians, reducing the time spent in clinics and prescribing personalized treatment pathways. Using AI, we can:
- Identify high population density areas that are currently underserved by hospitals. This can provide policymakers with inputs on how they can improve the deployment and availability of doctors, medical equipment and medication
- Leverage early warning signals through alternative mediums such as social media tracking for public health studies to provide guided diagnosis and interventions
- Create a digital record of patients’ medical histories and their clinical notes and use that as a reference for prescribing evidence-based treatment options and developing tailored treatment pathways
- Improve patient medical adherence by identifying individuals without health insurance, providing coverage and incentivizing the use of appropriate medication and treatment
- Speed up routine clinical processes such as scanning and annotating X-Rays and CT-Scans using computer vision and prescribing actions to physicians.
Revamp the Education System
The education system is undeniably critical for shaping future generations. However, both quality of and access to education is incredibly disparate across the developed and developing worlds. Curricula can often be outdated, thus not providing students with the skills they need for their careers. Problems abound in the education sector – from a high level of student dropouts, quality and methodology of teaching and lack of workforce readiness among students. While policymakers mull over how education can be made more contemporary and effective, AI can help provide guided interventions in the field of education by:
- Mapping the aptitude and interest of students in schools and universities with skills that are demanded by the market. This will help provide prescriptive career guidance that will be beneficial to both the employers and the future workforce
- Tracking the demand for skills in the market and the educational infrastructure available to supply those skills, through a Skills Repository. This will help keep education concurrent with current market demands and ensure much better alignment between academia and corporates
- Automate routine, time-consuming tasks – from creating and grading test papers, developing personalized benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject, and enabling teachers to focus on curriculum development, coaching and mentoring, and improving behavioral and personality aspects of students
- Identify potential school and university-level dropouts and their root-causes so educational institutions can take proactive steps to ensure student retention and course completion.
Address Legal and Law Enforcement Challenges
Globally, we face structural issues in areas of law enforcement and jurisprudence. Globally the average police-to-people ratio is 1 police personnel per 604 people, which is lower than the UN recommended standard of 1 per 454. Poor law enforcement eventually lends itself to a high crime rate and an overburdened legal system. AI can be a hugely pertinent gamechanger for global governance systems and help law enforcement officers improve surveillance by augmenting police efforts, automate a variety of routinized legal tasks and improve transparency in governance. By bringing the potential of AI in law enforcement, we can offer:
- Surveillance and identification of wrongdoers; areas recognized for high criminal activity can be done through computer vision
- Review and summary-creation of long drawn cases and their history can be done through natural language processing and voice recognition
- Routing Right-to-Information and governance-related citizen requests through intelligent bots, thus making it more efficient to get critical information
- Employ Anomaly Detection frameworks to surface fraudulent transactions – especially among land deals.
A global population of over 7.7 billion people, distributed across a huge landmass throws up a sizeable challenge when it comes to scalability. With many individual nations crippled by the inability to serve their populations, their public services need technology-centric solutions that are scalable and intelligent at the same time. Artificial intelligence will effectively address a number of these problems which are of socio-economic importance, and will go a long way in improving the quality of life of humanity at large. To enable this, public services need to act sooner rather than later and ramp up their data sets, identify and onboard technology, innovation and research partners for ideating and applying AI techniques that can power humanity’s next big leap.
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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.
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As is the case with investments in any other area of technology, AI needs to deliver demonstrable impact to business top line and bottom line. In today’s competitive landscape of business, enterprises are expected to measure the incremental ROI for every expense and every investment made – technology or otherwise. The case of Artificial Intelligence is no different. It is critical that technology and business leaders demand ROI impact for this technology in order to foster its growth and justify its proliferation in business.
To be sure, there are two key areas where Artificial Intelligence can contribute immense value; Increasing top line figures by unlocking new revenue streams and improving the bottom line through efficiencies in operations. Needless to say, top line gains eventually percolate their way into showcasing bottom line improvement – but for the purpose of this post, we’ll refer to bottom line impact as areas where AI brings in cost efficiencies by helping organizations reduce their overall cost of operations.
Artificial Intelligence driven applications can have a discernible impact on business top lines and bottom lines and help organizations unlock ROI from their implementation.
AI-Powered Topline Growth
Artificial Intelligence-led applications have huge potential to add to top line revenue growth for any organization. Typical AI interventions for this purpose range from improving the effectiveness of marketing and sales functions, improving customer loyalty through laser-guided customer experience initiatives and direct and indirect data monetization.
New Revenue Streams Enabled by Data Monetization:
Business leaders need to realize AI’s potential to unlock new sources of revenue in addition to improving customer targeting and loyalty. One of these ways is data monetization. What is data monetization? Simply put, data monetization refers to the act of generating measurable economic benefits from available data resources. According to Gartner, there are two distinct ways in which business leaders can monetize data. The most commonly seen method from the two is Direct Monetization. The way to realize value from this avenue involves directly adding AI as a feature to existing offerings. Companies like Nielsen, D&B, TransUnion, Equifax, Acxiom, Bloomberg and IMS run their business on licensing their data in a raw format or as part of their application infrastructure. With emerging Data-as-a-Service models and the application for direct insight delivery through intelligent application of AI, direct data monetization is simpler than ever. By wrapping insights alongside the data source, vendors can create a symbiotically powerful exchange of information for both the buyers and sellers of data. On the other hand, Indirect Monetization involves embedding AI into traditional business processes with a focus on driving increased revenue. A popular example of this is corporations who come out with branded, paid-for reports based on the data they own. For instance, professional services companies such as Aon, Deloitte, McKinsey, etc., regularly bring forward insightful industry and function-specific reports based on the data they collect as part of their consulting assignments.
Enabling Intelligent Marketing and Sales
Many of the most prominently cited successes of AI-enabled business transformation comes from the marketing and sales arena. Sales and marketing are constantly on the forefront for exciting inventions in AI since they contribute directly to top line growth. Use cases discovered in this arena span social media sentiment mining, programmatic selection of advertising properties, measuring effectiveness of marketing programs, ensuring customer loyalty and intelligent sales recommendations. AI also has huge potential to drive businesses to explore and exploit eCommerce platforms as a credible channel for sales and to help drive the digital agenda forward. Available tools are helping drive better customer conversions on eCommerce properties – by analysing the digital footprints (clickstream, etc.) of prospective customers, persuading them into making a purchase. In such use cases, AI helps improve personalization at the point-of-purchase, improve conversions and reduce cart abandonment. Marketing and sales use cases today are pretty much at the epicentre of an AI disruption and business leaders need to uncover more use cases that can help drive effective top line growth.
AI Redefining Customer Experience
Customers are the epicentre of every successful organization. Today, we live in times where customers have numerous competitor options to choose from while the switching costs for customers are increasingly lower. Given this scenario, for businesses to win with their customers they need to have a smarter approach to customer experience management.
We have progressed well beyond pre-programmed bots addressing frequently asked questions. AI-enabled systems today go further and provide customers with personalized guidance. The travel and hospitality industries, for instance, are ripe for such disruptive innovations. In many cases, we see chatbots that help customers identify and recommend interesting activities and events that tourists can avail. When applied with human creativity, AI can ensure this redefined understanding of customer experience, while maintaining a lower cost of delivering that experience.
AI for Improving Bottom Line Performance
At an operational level as well, AI can help organizations run a more efficient business. For instance, corporations across industries need to find innovative and fail-safe ways to reduce the cost of manufacturing as well as capping their outlay on the supply chain network. AI-centric solutions can drive down the turnaround time for talent acquisition and transform other facets of the Human Capital function too.
AI Driving Operational Efficiencies
Traditional manufacturing processes are now increasingly augmented by robotics and AI. These technologies are bringing increasing sophistication to the manufacturing process. The successes combine human and machine intelligence making AI-augmented manufacturing a pervasive phenomenon. Today, business leaders in the Industry 4.0 generation need to seriously consider planning a hybrid labour force powered by human and artificial intelligence – and ensure that the two coexist by implementing the right policies and plans in place.
Smarter Supply Chains Powered by AI
Orchestrating a leaner, more predictable supply chain is ripe for an AI-led disruption. We are witnessing not just new products and categories but also new formats of retailers proliferating the industry. This varied portfolio of offerings and channels requires corporations to manage their outlay efficiently on the overall network responsible for the network that manages the entire process from procurement and assembly to stocking and last mile delivery. Multiple use cases exist that leverage multi-source data from internal and external repositories, combining them with information from IOT sensors. AI algorithms are then applied over this combined data infrastructure with the objective of helping business users quickly identify possible weaknesses/flaws in the process such as delays and possible shortages. Business leaders are constantly on the lookout for solutions that can directly lift their bottom line by bringing in more intelligence and automation to their supply chain networks – thus unlocking savings for their businesses.
An Artificial Facelift for the Human Resources Function
The human resources function has historically been considered a cost-center in organizations. In addition to bringing down the costs associated with talent acquisition and management – AI would also help HR teams become leaner, more organized and reduce the turnaround time for talent acquisition. AI interventions are being seen in the areas of employee engagement and attrition management, but some of the most exciting use cases come from the talent acquisition area within the HR function. Multiple organizations are already working on solutions that can eliminate the need for HR staff to scan through each job application individually. By using AI intelligently, talent acquisition teams can determine the framework conditions for a job on offer and leave the creation of assessment tasks to Artificial Intelligence-powered systems. The AI-empowered system can then communicate the evaluation results and recommend the most suitable candidates for further interview rounds.
One of the key reasons why AI is in vogue today is the demonstrable ROI impact that it promises to bring to business processes. With greater computational power and more data, AI has become more practicable than before, but what will sustain its growth is how much incremental value it can eventually unlock for businesses across the globe and power new revenue models for businesses to tap into. It is critical that business and technology leaders earnestly kick off discussions around how to justify the impact of AI and mark down the key metrics that will be used to measure it. Partners and service providers too need to stay on top of finding ways to showcase measurable improvements that their software or services can bring to technology buyers. This will enable the entire AI ecosystem to flourish.
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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.
The traditional paradigm of understanding the business environment is shifting rapidly. It is estimated that 50% of the present Fortune 500 companies in the US will fall off the list by 2027. This is due to increasing competitive pressure from incumbents from disruptive, tech-driven startups as well as lateral moves from companies outside the traditional industry.
Such a fast-changing environment requires solutions that can provide insights at a comparable pace. AI can help executives better understand the trajectory of their present industry and provide deep insights on the expectations of customers, suppliers and other stakeholders. AI can also be deployed to monitor the entry of new competitors while benchmarking the organization against incumbent competitors – providing insights around improving operational efficiency, customer loyalty and marketing effectiveness. The key advantage of incorporating AI into this process is to improve the speed at which these insights can be mined, as well as separating the wheat from the chaff in terms of the criticality of the insights. These insights can be power key decision points for executives from where they can make more informed decisions around strategy.
Accentuate Awareness of Competitive Landscape and Business Environment
Leverage AI Assistants for Improving Speed of Decision-Making
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.
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The advent of Artificial Intelligence in the corporate world is disrupting existing business processes and changing the way organizations are run. AI is fast becoming a cornerstone of how businesses manage their bottom line, while opening new revenue streams that could provide a boost to their toplines as well. Given the scale of its impact, there is no doubt that AI will also have a severe impact on the science that governs how organizations are run today.
I am obviously referring to incumbent management theories and models that govern modern organizational management. In classic terms, management theories are frameworks of wisdom which guide the decisions made by organizational leaders that have survived phenomenally well over the period of the modern enterprise. Sure, there have been reasons to fine-tune each one to the realities of each era and industry, but the core construct has been omnipresent through the years.
With AI’s entry into the mainstream of business, management theories may need to be re-evaluated and tweaked appropriately. While the core construct remains powerfully relevant, an injection of the new-age reality of AI will help managers and business leaders apply them in a more contemporary manner on a few theories and models that are being redefined by AI.
Porter’s Five Forces
The theory of the Five Competitive Forces put forth by Michael Porter in 1979 is one of the marquee and evergreen theories in management thought schools. Michael Porter suggests that organizations looking for an understanding of their competitor environment need to consider the impact from five perspectives and work on reducing the risks associated: 1) Threat of new entrants, 2) Threat of Substitutes, 3) Bargaining Power of Customers, 4) Bargaining Power of Suppliers and 5) Intra-industry Rivalry. The construct of this theory is that when businesses need to evaluate the competitiveness (or for that matter, the probability of success) in a business or an industry, they need to keep in consideration these five levers that determine an industry’s attractiveness.
With AI now entering the fray, it is time to reimagine our understanding of Porter’s theory. Specifically, when it comes to the threat of new entrants. Over the years, AI has levelled the playing field as a secret sauce, moving even the most established incumbents from their positions in traditional industries. One must look at how AI is fuelling Amazon’s massive growth – which has hugely disrupted the traditional retail industry. Amazon uses AI in a variety of ways – from identifying the next likely purchase to piloting drone-based deliveries. It was no surprise when Amazon’s announcement last year that it will be entering the healthcare industry led to a tumble in the share price of traditional healthcare companies. AI puts enterprises in a pole position and organizations that harness its’ power correctly stand to gain huge ground over those that do not.
Elton Mayo’s Human Relations Theory
Elton Mayo’s landmark research in the field of organizational productivity comes from his studies in the 1920s at Hawthorne plants in Chicago. In seeking to answer questions around how to improve human productivity, he and his assistants tried tinkering with multiple variables that might have an impact on the quality of the labour force’s work – such as light, duration of breaks and duration of working hours. After all these variables proved inconclusive on how to uplift worker productivity, Mayo finally hit upon his hypothesis i.e. giving attention to employees is what truly resulted in improved performances. Giving your workers a voice in the decision-making process, an experience of greater freedom and autonomy and considering the inherent social needs of people – is the most critical lever in the productivity puzzle.
Enter Artificial Intelligence. With AI taking away much of the scud work involved in managing the varied bureaucracies inherent in organizations, leaders will find a lot more time in managing the performance of its most valued asset – human talent. By simplifying routine and repetitive processes for leadership and the people, we can afford to pay much more attention to the well-being of our human talent, celebrate successes and course-correct flagging performances – with the much-needed human(e) touch.
Total Quality Management (TQM)
Many models and theories surround the overall framework for TQM (Total Quality Management) – a science that owes much of its early evolution to manufacturing techniques originating in Japan. At its very essence, TQM is the science that governs the quality in the manufacturing process. It relates to the adherence of manufactured products with agreed specifications, evolved keeping in mind the needs of the end user. TQM bridges multiple concepts – from customer centricity, lowering the waste in manufacturing processes with a view to increasing the overall quality of the manufacturing output.
The theories surrounding this domain may also be due for a revamp. TQM has long been a data-driven process – relying heavily on a post-mortem understanding of evidence-based decision-making and process improvement. With AI in the picture, organizations can improve predictions around off-specified products earlier, leading to a quantum leap in manufacturing quality. AI is also helping improve the forecasting process, thus reducing the waste created through unused, unsold inventory. Similarly, AI will reduce the overhead associated with identifying anomalous manufacturing conditions and provision for predictive machine maintenance as well to keep up the quality standards in manufacturing activity.
The Future of Organizational Management
The defining case for AI to changing existing models and theories of management boils down to the need for creating a blended workforce comprising both humans and machines. Management science today is largely rooted in building more efficient and agile organizations for humans. In the future, humans and AI will work side-by-side to achieve shared organizational goals. This means that AI will help remove a lot of administrative work that often throttles the productivity of leaders – and allow them to direct their energies towards more complex, judgement driven work that requires them to think creatively. Intelligent machines will soon be considered by the workforce to be ‘colleagues’ and the evolution of management thought needs to account for policies and systems that make the most out of this hybrid workforce.
In conclusion, infusing AI will make business more human centric. Ironic as it may sound, putting AI in charge of the day-to-day, routinized activities will lead to more time for compassionate interactions between humans and unleash human creativity in a huge way. New management theories and models that emerge in the future will hence need to account for the impact of AI – and help organizations and their leaders understand how to navigate this new normal in business.
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The excitement around artificial intelligence is palpable. It seems that not a day goes by without one of the giants in the industry coming out with a breakthrough application of this technology, or a new nuance is added to the overall body of knowledge. Horizontal and industry-specific use cases of AI abound and there is always something exciting around the corner every single day.
However, with the keen interest from global leaders of multinational corporations, the conversation is shifting towards having a strategic agenda for AI in the enterprise. Business heads are less interested in topical experiments and minuscule productivity gains made in the short term. They are more keen to understand the impact of AI in their areas of work from a long-term standpoint. Perhaps the most important question that they want to see answered is – what will my new AI-enabled enterprise look like?
The question is as strategic as it is pertinent. For business leaders, the most important issues are – improving shareholder returns and ensuring a productive workforce – as part of running a sustainable, future-ready business. Artificial intelligence may be the breakout technology of our time, but business leaders are more occupied with trying to understand just how this technology can usher in a new era of their business, how it is expected to upend existing business value chains, unlock new revenue streams, and deliver improved efficiencies in cost outlays. In this article, let us try to answer these questions.
AI is Disrupting Existing Value Chains
Ever since Michael Porter first expounded on the concept in his best-selling book, Competitive Advantage: Creating and Sustaining Superior Performance, the concept of the value chain has gained great currency in the minds of business leaders globally. The idea behind the value chain was to map out the interlinkages between the primary activities that work together to conceptualize and bring a product / service to market (R&D, manufacturing, supply chain, marketing, etc.), as well as the role played by support activities performed by other internal functions (finance, HR, IT etc.). Strategy leaders globally leverage the concept of value chains to improve business planning, identify new possibilities for improving business efficiency and exploit potential areas for new growth.
Now with AI entering the fray, we might see new vistas in the existing value chains of multinational corporations. For instance:
- Manufacturing is becoming heavily augmented by artificial intelligence and robotics. We are seeing these technologies getting a stronger foothold across processes requiring increasing sophistication. Business leaders need to now seriously consider workforce planning for a labor force that consists both human and artificial workers at their manufacturing units. Due attention should also be paid in ensuring that both coexist in a symbiotic and complementary manner.
- Logistics and Delivery are two other areas where we are seeing a steady growth in the use of artificial intelligence. Demand planning and fulfilment through AI has already reached a high level of sophistication at most retailers. Now Amazon – which handles some of the largest and most complex logistics networks in the world – is in advanced stages of bringing in unmanned aerial vehicles (drones) for deliveries through their Amazon Prime Air program. Business leaders expect outcomes to range from increased customer satisfaction (through faster deliveries) and reduction in costs for the delivery process.
- Marketing and Sales are constantly on the forefront for some of the most exciting inventions in AI. One of the most recent and evolved applications of AI is Reactful. A tool developed for eCommerce properties, Reactful helps drive better customer conversions by analyzing the clickstream and digital footprints of people who are on web properties and persuades them into making a purchase. Business leaders need to explore new ideas such as this that can help drive meaningful engagement and top line growth through these new AI-powered tools.
AI is Enabling New Revenue Streams
The second way business leaders are thinking strategically around AI is for its potential to unlock new sources of revenue. Earlier, functions such as internal IT were seen as a cost center. In today’s world, due to the cost and competitive pressure, areas of the business which were traditionally considered to be cost centers are require to reinvent themselves into revenue and profit centers. The expectation from AI is no different. There is a need to justify the investments made in this technology – and find a way for it to unlock new streams of revenue in traditional organizations. Here are two key ways in which business leaders can monetize AI:
- Indirect Monetization is one of the forms of leveraging AI to unlock new revenue streams. It involves embedding AI into traditional business processes with a focus on driving increased revenue. We hear of multiple companies from Amazon to Google that use AI-powered recommendation engines to drive incremental revenue through intelligent recommendations and smarter bundling. The action item for business leaders is to engage stakeholders across the enterprise to identify areas where AI can be deeply ingrained within tech properties to drive incremental revenue.
- Direct Monetization involves directly adding AI as a feature to existing offerings. Examples abound in this area – from Salesforce bringing in Einstein into their platform as an AI-centric service to cloud infrastructure providers such as Amazon and Microsoft adding AI capabilities into their cloud offerings. Business leaders should brainstorm about how AI augments their core value proposition and how it can be added into their existing product stack.
AI is Bringing Improved Efficiencies
The third critical intervention for a new AI-enabled enterprise is bringing to the fore a more cost-effective business. Numerous topical and early-stage experiments with AI have brought interesting success for reducing the total cost of doing business. Now is the time to create a strategic roadmap for these efficiency-led interventions and quantitatively measure their impact to business. Some food for thought for business leaders include:
- Supply Chain Optimization is an area that is ripe for AI-led disruption. With increasing varieties of products and categories and new virtual retailers arriving on the scene, there is a need for companies to reduce their outlay on the network that procures and delivers goods to consumers. One example of AI augmenting the supply chain function comes from Evertracker – a Hamburg-based startup. By leveraging IOT sensors and AI, they help their customers identify weaknesses such as delays and possible shortages early, basing their analysis on internal and external data. Business leaders should scout for solutions such as these that rely on data to identify possible tweaks in the supply chain network that can unlock savings for their enterprises.
- Human Resources is another area where AI-centric solutions can be extremely valuable to drive down the turnaround time for talent acquisition. One such solution is developed by Recualizer – which reduces the need for HR staff to scan through each job application individually. With this tool, talent acquisition teams need to first determine the framework conditions for a job on offer, while leaving the creation of assessment tasks to the artificial intelligence system. The system then communicates the evaluation results and recommends the most suitable candidates for further interview rounds. Business leaders should identify such game-changing solutions that can make their recruitment much more streamlined – especially if they receive a high number of applications.
- The Customer Experience arena also throws up very exciting AI use cases. We have now gone well beyond just bots answering frequently asked questions. Today, AI-enabled systems can also provide personalized guidance to customers that can help organizations level-up on their customer experience, while maintaining a lower cost of delivering that experience. Booking.com is a case in point. Their chatbot helps customers identify interesting activities and events that they can avail of at their travel destinations. Business leaders should explore such applications that provide the double advantage of improving customer experience, while maintaining strong bottom-line performance.
The possibilities for the new AI-enabled enterprises are as exciting as they are varied. The ideas shared in this article are by no means exhaustive, but hopefully seed in interesting ideas for powering improved business performance. Strategy leaders and business heads need to consider how their AI-led businesses can help disrupt their existing value chains for the better, and unlock new ideas for improving bottom-line and top-line performance. This will usher in a new era of the enterprise, enabled by AI.
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Three Ways to Facilitate a Symbiotic Relationship Between Cognitive Intelligence and Behavioral Sciences
After every conference I speak at about the transformative power of Artificial Intelligence and its potential to unlock untold business value, the one question that often crops up from the audience is.. if AI is expected to perform much of the grunt work in the enterprise world, what scope is there for the so-called ‘human’ qualities?
Is the future of business and technology so deeply intertwined that it leaves virtually no scope in the future for the vagaries of human intelligence and behavior? The answer is quick and simple – absolutely not. Artificial Intelligence, while a great paradigm-shifter in the world of business, is still one of the tools that will be used by humans for making better decisions. At the end of the day, AI will still be developed and used by humans. And maintaining the ‘human element’ in the way it is made, delivered, used and improved will most certainly make it a lot more successful. AI exists to make human life simpler and richer – and hence it is critical that AI practitioners and data scientists adopt a human-centric approach to its development, deployment and adoption. Even the best AI will become quickly redundant without inputs from real humans on how to accelerate strategic decisions and processes.
How do we then build in that ‘human element’ into our Artificial Intelligence tools? This is where humanities-centric subjects of design and behavioral science come into the picture. What is behavioral science? Simply put, it is the study of internal cognitive processes of humans and societies and how these processes manifest into external perceptible and imperceptible actions and interactions. Behavioral science typically stands at a nexus of various subjects – borrowing aspects from sociology, anthropology, psychology and even economics and political science. Its interdisciplinary nature precludes the scale of impact it can have if applied correctly. In technology, and specifically in AI, behavioral science will and should impact how we build, use and interact with technology.
I see primarily three key areas where the symbiosis of AI-led cognitive intelligence systems and behavioral science can unlock massive value for enterprises that marry these two starkly different, but extremely complementary fields of study:
Appeal to the Non-Conscious
We have known for nearly a century now that a large majority of human biases, inferences, preferences and reactions are largely controlled by the dark recesses of our non-conscious brain. For technologists to build successful AI products, that are widely adopted and used they need to reach out inside the non-conscious parts of the human brain and orchestrate responses from there.
In AI technology specifically, user adoption is often the difference between make and break for products. Numerous AI products are mostly informed by the data they gather from human actions and their preferences. This data feeds the algorithms running in the background and makes them more sophisticated to better understand their human overlords. To that end, AI products need to have a strong underpinning in behavioral science, so that they can appeal to the non-conscious and improve adoption.
Take for example the work done by Nir Eyal for his book, ‘Hooked: How to Build Habit Forming Products.’ In the book, Eyal writes about multiple ways in which human subjects get applied to technology development. One of them is the Hook canvas – a loop comprising triggers, actions, rewards and investments – which are the cornerstone features of every addictive software you’ve used – from Facebook to Instagram, Snapchat and YouTube. Another is the idea of using the trinity of emotion, features and incentives – extremely relevant ideas to anyone working on building AI products. Another example comes from Worxogo – an Indian startup that employs behavioral design, neuroscience in tandem with predictive analytics to enhance employee performance through nudges to the non-conscious.
Build with Humans
Not only is AI built to serve humans, it is also built by humans. To that end, it becomes extremely important to consider what emotional triggers help define what we build and how we build it. Again, behavioral science practitioners have a key role to play in order to engage empathy in defining the requirements and going about the development of AI. Learnings from behavioral science can bring to light immeasurably important interventions for how we manage and lead teams, collaborate between a team and across multiple teams – all while maintaining a high level of motivation by appealing to a higher sense of purpose. It is worth examining how something as simple as empathy can be extremely valuable in how we build software. For instance, with improved self-awareness and empathy, developers can feel an intrinsic desire to write cleaner code while maintaining proper documentation. Also, given that AI is largely deployed using the DevOps methodology – empathy can be the difference between whether we can build a trust-based bridge between how we build, deploy and automate releases faster.
Beyond the ‘how’ of AI development, behavioral science can also contribute meaningfully to the ‘what’. Currently a lot of concern around AI is related to ethics – will AI lead to loss of meaningful work for humans? What data privacy issues can rear their head when we deploy large-scale data capture systems to improve our algorithms? We need to move the dial from apathy to empathy in the process of conceptualizing software – and knowledge of behavioral science will undoubtedly help AI practitioners develop more responsible AI.
Artificial Emotional Intelligence
The third key application of behavioral science – and possibly the most game-changing of the lot is – how can we apply behavioral science to make our systems more ‘human’? Is it possible to add a dash of EQ to these high IQ systems?
I certainly think there is a huge scope for developing AI that has a strong human bent. Consider the applications we are building today with AI and robotics – companions for the elderly, coaching apps for autistic children, even something as comparatively mundane is chatbots for customer service. Behavioral science holds the key to achieving the holy grail of how we can better balance the human-machine equation, by infusing human qualities into artificial systems.
To enable this, it is important to know who we are building for and what are their intrinsic and non-conscious needs. Behavioral science holds the clues that can complement AI’s ability to eliminate biases, while serving the emotional needs of humans. For example, StressSense tracks when people are highly stressed and helps them avoid anxious situations. This kind of breakthrough research can help in multiple AI applications, teaching them how to behave with humans, while ensuring a strong impact.
As technology providers and businesses work together to build transformational artificial intelligence systems and data science teams, it is very important to consider the human element. These teams would do well to develop a better understanding of whom the AI is built for and how it is used – through techniques offered by behavioral science. Balancing the human-machine equation and powering a complementary relationship between AI systems and the people who use them necessitates an infusion of behavioral science into the process. Ultimately, for AI to succeed, we need both – the foresight of technology as well as the insight of humans.
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Artificial Intelligence will deliver revolutionary impact on how enterprises make decisions today. In the last few years alone, we have rapidly moved beyond heuristics-based decision-making to analytics-driven decision-support. In the VUCA phase, businesses globally are now pivoting to an AI-led, algorithm-augmented style of decision-making. With huge computing power and ever-increasing data storage and analytics prowess, we are entering a new paradigm, a probable and interesting scenario wherein, Artificial Intelligence will play a huge role in augmenting human intelligence and enabling decision-making with complete autonomy. The big hope is that this new paradigm will not only reduce human biases and errors that are common with heuristic decisions, but also reduce the time involved in making these critical decisions.
Here, I’ll attempt to focus on how we moved from simpler data driven decision-support to AI-powered decisions. The evolution of this technology has been breathtaking to behold and just might provide clues as to what we can expect in the future. Further, I’ll cover a few critical aspects that need to be inculcated by organizations on the AI transformation journey, and provide a few insightful cues that will make this journey exciting and fruitful.
Transformation of Decision-Making: From Analytics to AI
First, let us look at how we got here. Some truly pathbreaking events happened along the way while we were trying to make more accurate business decisions, leading us to reimagine how decisions will be made in the enterprise.
Organizations are Becoming Math Houses
With data deluge and digital detonation, combined with the appreciation of the fact that robust analytical capabilities lead to more informed decisions, we are witnessing AI savvy organizations rapidly maturing into ‘math houses.’ Data science – the ability to extract meaningful insights out of data has become de rigueur. Why? Because we now know that data, when seen in isolation, is inherently dumb. It is the ability to process this data and identify patterns and anomalies – using sophisticated algorithms and ensemble techniques – that makes all the difference. These self-intuitive algorithms are where real value resides – as they define the intelligence required to uncover insights and make smart recommendations. Organizations today are evolving into algorithm factories. There is a real understanding today that by enabling continuous advancement in mathematical algorithms, we can deliver consistent decisions based on prescribed as well as evolving business rules.
It is now an established reality that companies with robust mathematical capabilities possess a huge advantage over those that don’t. Indeed, it’s this math-house orientation that separates companies like Amazon and Google from the ones they leave in their wake, with their ability to understand their customers better, identify anomalies and recognize key patterns.
AI: From Predictive to Prescriptive
We saw a similar evolution in the age of analytics – wherein the science and value veered from descriptive analytics, providing diagnostics of past events to prescriptive analytics, helping see and shape the future. We are seeing a similar evolution in how AI gets leveraged in the enterprise and where its maximum value lies.
In early implementations, it was common to see AI as just a tool to predict and forecast future conditions, while accounting for the dynamism seen in the external environment. Today, AI-enabled decision-making is more prescriptive, with AI providing enterprises not just a look into the future, but also key diagnostics and suggestions on potential decision options and their payoffs. Such evolved applications of AI can help businesses make decisions that can potentially exploit more business opportunities, while averting potential threats much earlier.
Mr. Algorithm to Drive Decision Making
The culmination of this AI-era advancement would be the introduction of smart algorithms in every walk of life and business. Algorithms will become further mainstream leading to what will be the most sweeping business change since the industrial revolution. Organizations – those that already aren’t – will start developing a suite of algorithmic IP’s that will de-bias most enterprise decisions.
If Mr. Algorithm is going to drive most enterprise decisions of tomorrow, we need to create some checks and balances to ensure that it does not go awry. It is more critical today than ever before that the algorithmic suite developed by enterprises has a strong grounding in ethics and can handle situations appropriately for which explicit training may not have been provided.
How to Enable this AI Era of Change
Ushering into an AI-centric era of decision-making will require organizational transformation from business, cultural and technical standpoints. The following facets will be the enablers of this change:
Developing an Engineering Mindset
Instrumenting AI in the enterprise requires a combination of data scientists and computer scientists. As AI matures in the enterprise, the users, use cases and data will increase exponentially. To deliver impactful AI applications, scale and extensibility is critically important. This is where having an engineering mindset comes in. Imbibing an engineering mindset will help standardize the use of these applications while ensuring that they are scalable and extensible.
Learning, Unlearning, Relearning
The other critical aspect to a culture where AI can thrive is creating an environment supporting continuous unlearning and relearning. AI can succeed if the people developing and operating it are rewarded for continuous experimentation and exploration. And just like AI, people should be encouraged to incorporate feedback loops and learn continuously. As technology matures it’s important that the existing workforce keeps up. For one, it’s critical that the knowledge of algorithm theory, applied math alongside training on AI library and developer tools, is imparted into the workforce – and is continuously updated to reflect new breakthroughs in this space.
Embedding Design-Thinking and Behavioral Science at the Center of this Transformation
Finally, given the nature of AI applications, it’s critical that they are consumed voraciously. User input very often activates the learning cycles of artificial intelligence applications. To ensure high usage of these applications, it’s very important that we put the user at the center while designing these applications. This is where the application of behavioral sciences and human-centered design will deliver impact. By imparting empathy in these applications for the user, we will be able to design better and more useful AI applications.
As we augment decision-making with algorithmic, AI-centered systems and platforms – the big expectation is that they will bring untold efficiencies in terms of cost, alongside improvement in the speed and quality with which decisions get made. It’s time to reimagine and deliver on enterprise decision-making that is increasingly shaped through artificial intelligence. These aspects – how the AI is progressing and how to exploit its potential are of paramount importance to keep in mind for an AI transformation.
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We are well and truly in the midst of the AI revolution. Research houses, academicians, think-tanks, business and technology leaders all agree upon the significant value waiting to be unlocked through the positive and progressive use of Artificial Intelligence – by re-engineering the old and envisioning the new. According to a research by Gartner, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long term success four times more often than others. Citing research by the MIT Center for Digital Business, from a competitive standpoint, companies that embrace digital transformation are 26 percent more profitable than their average industry competitors and enjoy a 12 percent higher market valuation.
The writing is on the wall. Intelligent business interventions made through AI will, to a large extent, define if your business will be an industry leader or a laggard tomorrow. And with that end in mind, businesses are rapidly changing their mindset and approach to AI – from topical experiments performed by forward-thinking business units, to more of a strategic mandate for enabling competitive differentiation. Businesses realize that for truly unlocking business value, they need to not only weave AI into the fabric of their enterprise, but also operationalize it – with the right personnel and change management initiatives. Given that AI can bring both cost efficiencies to business as well as potentially new revenue streams, businesses today are exploring an ‘AI Transformation’ – moving the dial on what is truly possible through a business model, engineered around AI. To enable your organization to do so, here are three powerful ideas to map the AI Transformation journey of your business.
Ensure Enterprise Readiness to Build and Adopt AI
The first step in the journey to AI Transformation for your enterprise is to understand and address if there are any disparities between your vision for AI and the ability of your organization to follow through with it. To that end, it is important to assess just how ready your enterprise is, in its current state, to build, deploy, adopt and benefit from AI-centric solutions. Ideas for AI Transformation need to be communicated clearly and grounded in the realities of organizational capabilities. When they are not, even the best intentions can go awry.
To do so, it is critical that business leaders measure their current AI maturity and assess the availability of internal skills. This will enable you to baseline just how empowered your current workforce is to develop industry-leading AI solutions. Once such a baseline is established on workforce readiness for building and adopting AI-led solutions, organizations need to start improving on these metrics – through internal trainings and external capability augmentation.
By developing this baseline score for AI readiness – organizations can have an objective view of where they are, how far they need to go and what the potential milestones to be achieved are in the journey to AI Transformation. This sort of pre-survey, combined with relevant training and assessment can help organizations craft a relevant roadmap with realistic timelines, as well as concrete actionables.
Build an AI ‘Win Team’
An AI Transformation is not unlike an extremely complex business re-engineering exercise. It entails massive changes – from the way you do business to how you run internal processes and staff multiple business units. Not only is it important to reskill a huge section of the workforce, there is also an important aspect of enabling change management to reinforce the importance of an AI-centric mindset.
To overcome this challenge, enterprises need to foster the consensus and engagement of a ‘win-team.’ This win-team would typically comprise functional and technical leaders who would be responsible for enabling the AI Transformation within their business units – from orienting the employees to the new mindset and ensuring capability readiness for the tasks at hand. On one hand, functional leaders can help their teams identify the processes that can be re-imagined using AI and manage resistance to change. On the other hand, technical leaders would lead the solutioning of technical components, while setting the training priorities and calendars for the workforce.
On change management, enterprises need employees to clearly appreciate the topline and bottomline benefits of an AI Transformation and focus towards enabling it. Employees stand to benefit themselves – as the professional benefits of making this transformation will accrue for their future. To further explore how companies can reduce the defensiveness in implementing AI-led processes further, they could also set innovation objectives for stakeholders as part of their performance metrics. Doing this will help create a strong alignment between individual, team and organizational objectives. A key aspect of AI transformation is ensuring large-scale adoption and usage of AI-powered solutions. AI applications typically fare better with every incremental user feedback and enriched data sources. Adoption and continuous use is a key parameter for the success of this transformation.
Integrated Business Processes over Siloed Business Functions
For years, the view of technology transformation and procurement has been of one that happens at a department / functional level – HR teams buy talent management software, finance teams sanction the purchase of accounting software, and CRMs get implemented to aid the efforts of sales teams. While this serves small technology initiatives, a sea-change is required for progressing an AI Transformation. To foster this, enterprises need to make a shift from a siloed, function-centric mindset to an integrated, process-centric mindset.
This is because AI use cases can often span multiple business units and functions, while tapping into multiple data sources for providing cross-team value, seamlessly. The very nature of AI deployments thus requires a process-centric view, with a strong consensus and buy-in from multiple stakeholders. Furthermore, the budget for purchasing AI services / applications is likely to come from the allocations of multiple beneficiaries across functions. This makes it all the more imperative that enterprises deprioritize functions in favor of processes.
An AI Transformation is doubtless the most strategic subject to be tackled by organizations today. Successful transformations will ensure enterprises go beyond mere automation and cost-cutting strategies and unveil previously unseen business and revenue opportunities. It is also extremely important to consider the role of digitization in building a new technology infrastructure that is AI-ready – possibly decentralized, cloud-based and highly available. There is now an urgent need for business leaders to have more than just a superficial understanding of AI and its successes. They will now be tasked with building and delivering a concrete, value-oriented roadmap for enabling a key transformation in the history of their organizations.