AIQRATE in 2020 ….A walk to remember
“Enabling clients reimagine their decision making & accentuate the business performance with AI strategy in a transformation, innovation and disruption driven world”
In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture. Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems. To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it. AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.
In the age of the bionic enterprises, AI continues to dominate the technology & business landscape. Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres. Traditionally, human decisions are to a large extent based on intuition, gut and historical data. In the age of AI, several of our decisions will be taken by algorithms. Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business. All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage. If you have not started yet, you are already behind the competition, however large or pedigreed you might be.
So, where are you placed on your AI journey? At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.
At AIQRATE, we are here to help you with the art of the possible with AI. Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses. Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.
We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator. AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.
Wish you a great 2021!
Chief Executive Officer
Bangalore , India
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
In an exclusive interaction with SCIKEY, Sameer Dhanrajani, CEO at AIQRATE Advisory & Consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
“AI led algorithms can decide on how we need to emote, behave, react, transact or interact with an individual,” Sameer Dhanranjani
Sameer is a globally recognized AI advisor, business builder, evangelist and thought leader known for his deep knowledge, strategic consulting approaches in AI space. Sameer has consulted with several Fortune 500 global enterprises, Indian corporations , GCCs, startups , SMBs, VC/PE firms, Academic Institutions in driving AI led strategic transformation and innovation strategies. Sameer is a renowned author, columnist, blogger and four times Tedx speaker. He is an author of bestselling book – AI and Analytics: accelerating business decisions.
In an exclusive interaction with SCIKEY, Sameer Dhanranjani, CEO at AIQRATE advisory consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
Mr Dhanranjani, you have consulted with several Fortune 500 enterprises, GCCs also start-ups in driving AI-led strategic transformation strategies. What according to you, are the topmost strategic considerations to weigh for managing accelerating business in Post COVID world for a start-up?
The unprecedented times of COVID-19 have brought the aspect of decision making under consideration. This includes tactical, strategic, and operational decision making that is crucial to make the venture more sustainable. Today the use of artificial intelligence is quite high amongst organizations. It can be used by start-up ventures and other outfits to make decisions irrespective of the area that needs decision making.
Most decisions that need to be made strategically are being passed on to artificial intelligence-enabled interventions. The algorithm makes similar decisions based on the previous decisions taken. Algorithms can decide how we need to emote, behave, react, transact or interact with the opposite individual This advancement in AI brings the challenge for organizations to create products and services specific to each customer through hyper-personalization and micro-segmenting. However, it can also be considered as an opportunity for organizations to emerge from the pandemic with newer business models and experiences for customers. Start-ups, especially, can make use of such advancements to reinvent and rejuvenate the organizational ecosystem.
You are known for your passion for Artificial Intelligence and are an author to the bestselling book – AI and Analytics: Accelerating Business Decisions. Tell us where how can AI be strategically significant while building productive teams.
My experience has led me to deal with engagements in the entire value chain of HR, ranging from hiring to engagement to incentivization that has leveraged using AI. It is phenomenal to see how AI can help build, engage, and sustain productive teams. AI can help in hiring through the detection emotions, facial expressions, tone modulations of the interviewee through computer vision and image classification techniques.
In the creation of productive teams, AI can gauge the engagement levels of an employee. It tries to look at the various interventions made by an employee regarding their attendance, participation in virtual meetings, and propensity to ask and engage themselves in conversations. It also keeps in check the number of pauses, intervals, and breaks taken by an employee. Every aspect of the employee is being marked to see how productive, inclusive, as an individual and in teams.
What are the top 5 AI trends to watch out for in Post COVID the scenario of the next one year?
When it comes to AI, the first trend emerging is that AI is not a tool or a technology, but it is now being touted as a strategic imperative for any organization. This means that AI strategies will become an intrinsic part and feature of every organisation.
The second trend is the democratization of AI. There is a possibility of the emergence of an AI marketplace where virtual exchanges related to business problems, demo runs etc. can be conducted. One would actually be able to figure out which algorithm is best for them in customer experience, supply chain etc.
The third trend being the cloud will act as a catalyst for AI proliferation. The propensity for cloud providers to enable AI companies with possible aspects of microservice API’s, Product Solutions will be created on the go. This means that the cloud enablers will have options to see various possibilities specific to their organisation when it comes to AI-specific use cases.
The fourth trend is linked to skilling. AI today is a part of a lot of course curriculums. But what is missing is the whole aspect of how does it get applied? The new courseware will be focused on how is AI implemented, adopted in the organization.
The last fifth trend is decision-making enabled by AI, which means humans will have no option but to upskill and reskill themselves to take a more rational, pragmatic and sanguine approach. So new models, new emerging realities of decision making will emerge.
How is AI powering the Future of Work, what are critical considerations for business and tech leaders considering the rapidly changing business dynamics due to COVID?
The future of work will be about AI and what we call AI plus a set of exponential technologies. This means that every aspect of our performance interaction and our responses will be gauged very manually through these technologies. This indicates that the level of performances in terms of how we go up-to-date needs to be worked upon. The future of work is an ecosystem where one particular employer cannot do it all.
This means that if learning must occur through an external player, it must come through the ecosystem of co-employees and the employer. In the future, we will not be caged as mere professionals doing our job but will be encouraged to push our boundaries to explore more at work. At the same time, transformation, innovation, and disruption will be a part of the future’s performance metrics. They will become a major parameter for the organization to create a mediocre versus proficient employee or a professional. This is where the onus will fall on the employees to ensure that they are not just doing what is being called out, but are going beyond to create what we call a value creation for the organisation.
SCIKEY Market Network is a Digital Marketplace for Jobs, Work Business solutions, supported by a Professional Network and an integrated Services Ecosystem. It enables enterprises, businesses, job seekers, freelancers, and gig workers around the world. With its online events, learning certifications, assessments, ranking awards, content promotion tools, SaaS solutions for business, a global consulting ecosystem, and more, companies can get the best deals in one place.
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REPORT: Data Engineering 4.0: Evolution, Emergence and Possibilities in the next decade
Today, most technology aficionados think of data engineering as the capabilities associated with traditional data preparation and data integration including data cleansing, data normalization and standardization, data quality, data enrichment, metadata management and data governance. But that definition of data engineering is insufficient to derive and drive new sources of society, business and operational value. The Field of Data Engineering brings together data management (data cleansing, quality, integration, enrichment, governance) and data science (machine learning, deep learning, data lakes, cloud) functions and includes standards, systems design and architectures.
There are two critical economic-based principles that will underpin the field of Data Engineering:
Principle #1: Curated data never depletes, never wears out and can be used an unlimited number of use cases at a near zero marginal cost.
Principle #2: Data assets appreciate, not depreciate, in value the more that they are used; that is, the more these data assets are used, the more accurate, more reliable, more efficient and safer they become.
There have been significant exponential technology advancements in the past few years ; data engineering is the most topical of them. Burgeoning data velocity , data trajectory , data insertion , data mediation & wrangling , data lakes & cloud security & infrastructure have revolutionized the data engineering stream. Data engineering has reinvented itself from being passive data aggregation tools from BI/DW arena to critical to business function. As unprecedented advancements are slated to occur in the next few years, there is a need for additional focus on data engineering. The foundations of AI acceleration is underpinned by robust data engineering capabilities.
YourStory & AIQRATE curated and unveiled a seminal report on “Data Engineering 4.0: Evolution , Emergence & Possibilities in the next decade.” A first in the area , the report covers a broad spectrum on key drivers of growth for Data Engineering 4.0 and highlights the incremental impact of data engineering in the time to come due to emergence of 5G , Quantum Computing & Cloud Infrastructure. The report also covers a comprehensive section on applications across industry segments of smart cities , autonomous vehicles , smart factories and the ensuing adoption of data engineering capabilities in these segments. Further , it dwells on the significance of incubating data engineering capabilities for deep tech startups for gaining competitive edge and enumerates salient examples of data driven companies in India that are leveraging data engineering prowess . The report also touches upon the data legislation and privacy aspects by proposing certain regulations and suggesting revised ones to ensure end to end protection of individual rights , security & safety of the ecosystem. Data Engineering 4.0 will be an overall trojan horse in the exponential technology landscape and much of the adoption acceleration that AI needs to drive ; will be dependent on the advancements in data engineering area.
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Lock in winning AI deals : Strategic recommendations for enterprises & GCCs
Artificial Intelligence is unleashing exciting growth opportunities for the enterprises & GCCs , at the same time , they also present challenges and complexities when sourcing, negotiating and enabling the AI deals . The hype surrounding this rapidly evolving space can make it seem as if AI providers hold the most power at the negotiation table. After all, the market is ripe with narratives from analysts stating that enterprises and GCCs failing to embrace and implement AI swiftly run the risk of losing their competitiveness. With pragmatic approach and acknowledgement of concerns and potential risks, it is possible to negotiate mutually beneficial contracts that are flexible, agile and most importantly, scalable. The following strategic choices will help you lock in winning AI deals :
Understand AI readiness & roadmap and use cases
It can be difficult to predict exactly where and how AI can be used in the future as it is constantly being developed, but creating a readiness roadmap and identifying your reckoner of potential use cases is a must. Enterprise and GCC readiness and roadmap will help guide your sourcing efforts for enterprises and GCCs , so you can find the provider best suited to your needs and able to scale with your business use cases. You must also clearly frame your targeted objectives both in your discussions with vendors as well as in the contract. This includes not only a stated performance objective for the AI , but also a definition of what would constitute failure and the legal consequences thereof.
Understand your service provider’s roadmap and ability to provide AI evolution to steady state
Once you begin discussions with AI vendors & providers, be sure to ask questions about how evolved their capabilities and offerings are and the complexity of data sets that were used to train their system along with the implementation use cases . These discussions can uncover potential business and security risks and help shape the questions the procurement and legal teams should address in the sourcing process. Understanding the service provider’s roadmap will also help you decide whether they will be able to grow and scale with you. Gaining insight into the service provider’s growth plans can uncover how they will benefit from your business and where they stand against their competitors. The cutthroat competition among AI rivals means that early adopter enterprises and GCCs that want to pilot or deploy AI@scale will see more capabilities available at ever-lower prices over time. Always mote that the AI service providers are benefiting significantly from the use cases you bring forward for trial as well as the vast amounts of data being processed in their platforms. These points should be leveraged to negotiate a better deal.
Identify business risk cycles & inherent bias
As with any implementation, it is important to assess the various risks involved. As technologies become increasingly interconnected, entry points for potential data breaches and risk of potential compliance claims from indirect use also increase. What security measures are in place to protect your data and prevent breaches? How will indirect use be measured and enforced from a compliance standpoint? Another risk AI is subject to is unintentional bias from developers and the data being used to train the technology. Unlike traditional systems built on specific logic rules, AI systems deal with statistical truths rather than literal truths. This can make it extremely difficult to prove with complete certainty that the system will work in all cases as expected.
Develop a sourcing and negotiation plan
Using what you gained in the first three steps, develop a sourcing and negotiation plan that focuses on transparency and clearly defined accountability. You should seek to build an agreement that aligns both your enterprise’s and service provider’s roadmaps and addresses data ownership and overall business and security related risks. For the development of AI , the transparency of the algorithm used for AI purposes is essential so that unintended bias can be addressed. Moreover, it is appropriate that these systems are subjected to extensive testing based on appropriate data sets as such systems need to be “trained” to gain equivalence to human decision making. Gaining upfront and ongoing visibility into how the systems will be trained and tested will help you hold the AI provider accountable for potential mishaps resulting from their own erroneous data and help ensure the technology is working as planned.
Develop a deep understanding of your data, IP, commercial aspects
Another major issue with AI is the intellectual property of the data integrated and generated by an AI product. For an artificial intelligence system to become effective, enterprises would likely have to supply an enormous quantity of data and invest considerable human and financial resources to guide its learning. Does the service provider of the artificial intelligence system acquire any rights to such data? Can it use what its artificial intelligence system learned in one company’s use case to benefit its other customers? In extreme cases, this could mean that the experience acquired by a system in one company could benefit its competitors. If AI is powering your business and product, or if you start to sell a product using AI insights, what commercial protections should you have in place?
In the end , do realize the enormous value of your data, participate in AI readiness, maturity workshops and immersion sessions and identification of new and practical AI use cases. All of this is hugely beneficial to the service provider’s success as well and will enable you to strategically source and win the right AI deal.
(AIQRATE advisory & consulting is a bespoke global AI advisory & consulting firm and provides strategic advisory services to boards, CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions & create AI powered enterprises . Visit www.aiqrate.ai , reach out to us at firstname.lastname@example.org )
AI for Strategic Innovation
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.
- 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 email@example.com )
AI led strategy for business transformation : A guided approach for CXOs
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.
AI For CXOs — Redefining The Future Of Leadership In The AI Era
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.
Delivering Business Value Through AI To Impact Top Line, Bottom Line And Unlock ROI
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.
Building AI-enabled organisations
The adoption and benefit realisation from cognitive technologies is gaining increasing momentum. According to a PwC report, 72% of business executives surveyed believe that artificial intelligence (AI) will be a strong business advantage and 67% believe that a combination of human and machine intelligence is a more powerful entity than each one on its own.
Another survey conducted by Deloitte reports that on an average, 83% of respondents who have actively deployed AI in the enterprise see moderate to substantial benefits through AI – a number that goes further up with the number of AI deployments.
These studies make it abundantly clear that AI is occupying a high and increasing mindshare among business executives – who have a strong appreciation of the bottom line impact delivered by cognitive systems, through improved efficiencies.
Having said that, with AI becoming more and more mainstream in an organisational setup, piecemeal implementations will deliver a lower marginal impact to organisations’ competitive advantage. While once early adopters were able to realise transformational benefits through siloed AI deployments, now that it is fast maturing as a must-have in the enterprise and we will need a different approach.
To realise true competitive advantage, organisations need to have an AI-first mindset. It is the new normal in accelerating business decisions. It was once said that every company is a technology company – meaning that all companies were expected to have mature technology backbones to deliver business impact and customer satisfaction. That dictum is now being amended to say – every company is a cognitive company.
To deliver on this promise, companies need to weave AI into the very fabric of their strategy. To realise competitive advantage tomorrow, we need to embed AI across the organisation today, with a strong, stable and scalable foundation. Here are three building blocks that are needed to create that robust foundation.
1. Enrich Data & Algorithm Repositories
If data is indeed the new oil (which it is), organisations that hold the deepest reserves and the most advanced refinery will be the ones that win in this new landscape. Companies having the most meaningful repository of data, along with fit-for-purpose proprietary algorithms will most likely enjoy a sizeable competitive advantage.
So, companies need to improve and re-invent their data generation and collection mechanisms. Data generation will help reduce their reliance on external data providers and help them own the data for conducting meaningful, real-time analysis by continuously enriching the data set.
Alongside, corporations also need to build an ‘algorithm factory’ – to speed up the development of accurate, fit-for-purpose and meaningful algorithms. The algorithm factory would need to push out data models in an iterative process in a way that improves the speed and accuracy.
This would enable the data and analysis capabilities of companies to grow in a scalable manner. While this task would largely fall under the aegis of data science teams, business teams would be required to provide timely interventions and feedback – to validate impact delivered by these models, and suggest course-corrections where necessary.
Another key aspect of this process is to enable a transparent cross-organisation view into these repositories. This will allow employees to collaborate and innovate rapidly by learning what is already been done and will reduce needless time and effort spent in developing something that’s already there.
2. AI Education for Workforce
Operationalising AI requires a convergence of different skill sets. According to the above-cited Deloitte survey, 37% of respondents felt that their managers didn’t understand cognitive technology – which was a hindrance to their AI deployments.
We need to mix different streams of people to build a scalable AI-centric organisation. For instance, business teams need to be continuously trained on the operational aspects of AI, its various types, use cases and benefits – to appreciate how AI can impact their area of business.
Technology teams need to be re-skilled around the development and deployment of AI applications. Data processing and analyst teams need to better understand how to build scalable computational models, which can run more autonomously and improve fast.
Unlike a typical technology transformation, AI transformation is a business reengineering exercise and requires cross-functional teams to collaborate and enrich their understanding of AI and how it impacts their functions, while building a scalable AI programme.
The implicit advantage of developing topical training programmes and involving a larger set of the workforce is to mitigate the FUD that is typically associated with automation initiatives. By giving employees the opportunity to learn and contribute in a meaningful way, we can eliminate bottlenecks, change-aversion and enable a successful AI transformation.
3. Ethical and Security Measures
The 4th Industrial Revolution will require a re-assessment of ethical and security practices around data, algorithms and applications that use the former two.
By introducing renewed standards and ethical codes, enterprises can address two important concerns people typically raise – how much power can/should AI exercise and how can we stay protected in cases of overreach.
We are already witnessing teething trouble – with accidents involving self-driving cars resulting in pedestrian deaths, and the continuing Facebook-Cambridge Analytica saga.
Building a strong grounding for AI systems will go a long way in improving customer and social confidence – that personal data is in safe hands and is protected from abuse – enabling them to provide an informed consent to their data. To that end, we need to continue refining our understanding around the ethical standards of AI implementations
AI and other cyber-physical systems are key components of the next generation of business. According to a report by semiconductor manufacturer, ARM, 61% of respondents believe that AI can make the world a better place. To increase that sentiment even further, and to make AI business-as-usual, and power the cognitive enterprise, it is critical that we subject machine intelligence to the same level of governance, scrutiny and ethical standards that we would apply to any core business process.
The New Age Enterprise – Enabled by AI
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.