In the time of uncertainty and disruption….Soon, organizations will increasingly be competing on the AI prowess and their supremacy. AI promises to play a critical role ; artificial intelligence can detect patterns in complex data sets at extreme speed and scale, enabling dynamic learning. This will allow organizations to constantly adapt to changing realities and surface new opportunities, which will be increasingly important in an uncertain and fast-changing environment.

But for companies to compete on AI, it is not enough to merely adopt AI, which alone can accelerate learning only in individual activities. As with previous transformative technologies, unlocking the full potential of AI and future of workforce will require fundamental organizational innovation , transformation and disruption. Leaders will need to re-invent the enterprise as an AI driven organization :

• Velocity & Scale : The growing opportunity and need to perform at high velocity bringing scale driven by AI is well known—algorithmic trading, dynamic pricing, real-time customized product recommendations are already a reality in many businesses. But it is perhaps under-appreciated that slow moving forces are also becoming important. For example, trade institutions, political structures and social attitudes are slowly changing in ways that could have a profound impact on business. Gone are the days when business leaders could focus only on business and treat these broader variables as constants or stable trends. But such shifts unfold over many years or even decades. In order to thrive sustainably, businesses must learn at high velocity .
• Rebalancing Humans and Machines equation : Machines have been crucial components of businesses for centuries—but in the AI age, they will likely expand rapidly into what has traditionally been considered white-collar work. Instead of merely executing human-directed and designed processes, machines will be able to learn and adapt, and will therefore have a greatly expanded role in future organizations. Humans will still be indispensable, but their duties will be quite different when complemented or substituted by intelligent machines.
• Integrating External ecosystems with corporate strategies : Businesses are increasingly acting in multi company ecosystems that incorporate a wide variety of players. Indeed, seven of the world’s largest companies, and many of the most profitable ones, are now platform businesses. Ecosystems greatly expand learning potential: they provide access to exponentially more data, they enable rapid experimentation, and they connect with larger networks of suppliers of customers. Harnessing this potential requires redrawing the boundaries of the enterprise and effectively influencing economic activity beyond the orchestrating company.
• Evolving the Organization : The need for dynamic learning does not apply just to customer-facing functions—it also extends to the inner workings of the enterprise. To take advantage of new information and to compete in dynamic, uncertain environments, the organizational context itself needs to be evolve in the face of changing external conditions.

 Today’s organizations, which were designed for more stable business environments, are not well-suited to perform these functions. Reinventing the organization for the next decade will require embracing four imperatives:

1. Integrate AI into the core operating model for survival
2. Migrate human cognition to mature work spheres
3. Re balance the relationship between machines and humans.
4. New age leadership & management approaches

1.Integrate AI into the core operating model for survival : As powerful as today’s level of AI is , it will yield only incremental gains if it simply enhance individual steps of existing processes. The effective rate of an organization’s learning is gated by its ability to act on new insights. And classical organizations act slowly, owing to their reliance on human decision making and hierarchy. Organizations will need not only to automate but also to “embed AI in to the operating model” of significant parts of their businesses.

In order to truly accelerate the speed of learning to algorithmic timescales, organizations will need not only to automate but also to “embed AI ” into significant parts of their businesses. In traditional automation, machines execute a pre-designed process repeatedly and consistently. In AI led transformation, machines use continuous feedback to act, learn, and adapt on their own—without the bottleneck of human intervention.

AI driven systems are designed by combining multiple algorithms into integrated learning loops. Data from digital platforms automatically flows into AI algorithms, which mine the information in real time to facilitate new insights and decisions. These are wired directly into action systems, which continuously optimize outcomes under changing conditions. These actions produce yet more data that can be fed back through the cycle, closing the loop and allowing the organization to learn at the speed of algorithms.

In contrast, traditional organizational approaches—for example, unchanging rules or hierarchical decision processes—can impede companies’ ability to harness the rapid learning potential unlocked by AI ; Actions that companies can take to harness AI include :

• Gather real-time data on all aspects of the business by leveraging algorithms
• Deploy AI at scale, integrated with data and decision-making systems.
• Take human hierarchy “out of the loop” of routine, data-based decision making.

2. Migrate Human Cognition to Mature Work Spheres :The widespread adoption of AI naturally raises the question of what role human workers will play in the organization of the future. Today, there is already widespread concern about the speed at which AI will disrupt the future of work. To shape this future—and to maximize organizational learning capabilities—businesses need to focus human cognition on its unique strengths. Humans should increasingly focus their efforts on these higher-level activities. For example, while correlative analysis is generally sufficient for learning about repeated actions on fast timescales, it is less useful for learning about slow-moving forces, such as political, social, and economic trends. These shifts are unique and depend on the historical context and trajectory, which means there is no repeated data set in which to find patterns. Human abilities, such as understanding causal relationships and generalizing from limited data, are necessary to decode these forces and adapt the organization accordingly.

Counterfactual thinking is also critical, as businesses need increasingly to compete on Imagination. Existing business models are being exhausted faster, and long-term growth is declining, which means companies must continually generate new ideas to grow sustainable. But businesses today, which are often implicitly designed for efficiency and the maximization of short run financial outcomes, are not conducive to imagination. Organizations will need to better facilitate individual and collective imagination.

In addition to imagination and making sense of non-repeated events, there will be many other activities where humans are advantaged, including organizational design, algorithmic governance, ethics, and purpose, to name a few. In these domains of human activity, organizations will need to become more effective at dynamic collaboration to get the most out of their teams. This requires emphasizing self-organization and experimentation by creating an organizational context in which responsive decision making and learning can thrive, rather than by relying on direct instructions.

3. Rebalance the Relationship Between Humans and Machines : The first two imperatives call for a hybrid organization, one that combines the comparative advantages of machines and humans: machines’ ability to rapidly identify complex patterns in big data and humans’ ability to decode complex causal relationships and imagine new possibilities. Together, these will enable the organization to learn on an expanded range of timescales—faster and slower.

But in hybrid organizations, humans and machines will increasingly have to collaborate in new and more effective ways. This includes tasks that require thinking on multiple levels or timescales simultaneously, as well as tasks that demand social interaction, another dimension in which humans are currently far more effective. Organizations will thus need to reimagine the relationship between humans and machines to bring the best out of both and maximize synergies.

 Today’s AI models tend to be “black boxes” that are not designed to be interoperable and may therefore impede trust. For these new types of human-machine relationships to succeed, organizations need to develop effective human-machine interfaces that allow for seamless collaboration. Organizations will need to overcome these hurdles by developing and implementing interfaces that provide transparency into how AI makes recommendations, allowing humans to understand and validate machines’ actions. Similarly, humans and algorithms are rarely matched for bandwidth and complexity. Choosing the right level of abstraction and compression for communication between humans and computers is critical: too much compression will suppress subtlety and prevent the tinkering through which human innovation proceeds, while too little will overwhelm human overseers.

 4. New Age Leadership & Management Approaches :Collectively, the above imperatives point to a very different way of designing and operating organizations with AI —which in turn will significantly change the role of leadership. In particular, leaders will need to focus on several new challenges.Developing governance principles for AI and autonomous machines. : As machines play a greater part in learning and action, the role of leadership in setting guardrails and priorities will take on greater importance. In the last decade, tech companies could sidestep these topics, as the promise and potential of new technologies gave them a license to move fast. But as social scrutiny of technology increases, questions about governance, trust, and ethics are coming to the forefront. And as AI is adopted more widely, all businesses will have to deal with these difficult questions.

 The organizations that will survive and become pioneer will look much different from today’s: they will use different AI driven capabilities; they will operate at different speeds and scales of influence; they will contain different structures and responsibilities; and they will embody different leadership models to enable all of the above. AI will become a force multiplier and will define the DNA of tomorrow’s organization.At the end of the day, its a matter of survival….

Global Capability Centers(GCC’s) are at an inflection point as the pace at which AI is changing every aspect is exponential and at high velocity. The rapid transformation and innovation of GCC’s today is driven largely by ability for them to position AI strategic imperative for their parent organizations. AI is seen to the Trojan horse to catapult GCC’s to the next level on innovation & transformation. In recent times; GCC story is in a changing era of value and transformative arbitrage. Most of the GCCs are aiming towards deploying suite of AI led strategies to position themselves up as the model template of AI center of Excellence . It is widely predicted that AI will disrupt and transform capability centers in the coming decades. How are Global Capability Centers in India looking at positioning themselves as model template for developing AI center of competence? How have the strategies of GCCs transformed with reference to parent organization? whilst delivering tangible business outcomes , innovation & transformation for parent organizations?

Strategic imperatives for GCC’s to consider to move incrementally in the value chain & become premier AI center of excellence

AI transformation

Artificial Intelligence has become the main focus areas for GCCs in India. The increasing digital penetration in GCCs across  business verticals has made it imperative to focus on AI. Hence, GCCs are upping their innovation agenda by building bespoke AI CoEs. Accelerated AI adoption has transcended industry verticals, with organizations exploring different use cases and application areas. GCCs in India are strategically leveraging one of the following approaches to drive the AI penetration ahead –

• Federated Approach: Different teams within GCCs drive AI initiatives
• Centralized Approach: Focus is to build a central team with top talent and niche skills that would cater to the parent organization requirements
• Partner ecosystem : Paves a new channel for GCCs by partnering with research institutes , start-ups , accelerators
• Hybrid Approach: A mix of any two or more above mentioned approaches, and can be leveraged according to GCC’s needs and constraints.

Ecosystem creation : Startups /research institutes/Accelerators

One of the crucial ways that GCCs can boost their innovation agenda is by collaborating with start-ups, research institutes , accelerators. Hence, GCCs are employing a variety of strategies to build the ecosystem. These collaborations are a combination of build, buy, and partner models:

• Platform Evangelization: GCCs offer access to their AI platforms to start-ups
• License or Vendor Agreement: GCCs and start-ups enter into a license agreement to create solutions
• Co-innovate: Start-ups and GCCs collaborate to co-create new solutions & capabilities
• Acqui-hire: GCCs acquire start-ups for the talent & capability
• Research centers : GCCs collaborate with academic institutes for joint IP creation , open research , customized programs
• Joint Accelerator program : GCCs & Accelerators build joint program for customized startups cohort

To drive these ecosystem creation models, GCCs can leverage different approaches. Further, successful collaboration programs have a high degree of customization, with clearly defined objectives and talent allocation to drive tangible and impact driven business outcomes.

AI Center of Competence/ Capability

GCCs are increasingly shifting to competency , capability creation models to reduce time-to-market. In this model, the AI Center of Competence teams are aligned to capability lines of businesses where AI center of competence are responsible for creating AI capabilities , roadmaps and new value offerings, in collaboration with parent organization’s business teams. This alignment and specific roles have clear visibility of the business user requirement. Further, capability creation combined with parent organization’s alignment helps in tangible value outcomes. In several cases, AI teams are building new range of innovation around AI based capabilities and solutions to showcase ensuing GCC as model template for innovation & transformation . GCCs need to conceptualize a bespoke strategy for building and sustaining AI Center of Competence and keep it up on the value chain with mature and measured transformation & innovation led matrices.

Talent Mapping Strategy

With the evolution of analytics ,data sciences to AI , the lines between different skills are blurring. GCCs are witnessing a convergence of skills required across verticals. The strategic shift of GCCs towards AI center of capability model has led to the creation of AI , data engineering & design roles. To build skills in AI & data engineering, GCCs are adopting a hybrid approach. The skill development roadmap for AI is a combination of build and buy strategies. The decision to acquire talent from the ecosystem or internally build capabilities is a function of three parameters –Maturity of GCC ’s existing AI capabilities in the desired or adjacent areas ,Tactical nature of skill requirement & Availability and accessibility of talent in the ecosystem. There’s always a heavy Inclination towards building skills in-house within GCCs and a majority of GCCs have stressed upon that the bulk of the future deployment in AI areas will be through in-house skill-building and reskilling initiatives. However, talent mapping strategy for building AI capability is a measured approach else can result in being a Achilles heel for GCC and HR leaders.  

GCCs in India are uniquely positioned to drive the next wave of growth with building high impact AI center of competence , there are slew of innovative & transformative models that they are working upon to up the ante and trigger new customer experience , products & services and unleash business transformation for the parent organizations. This will not only set the existing GCCs on the path to cutting-edge innovation but also pave the way for other global organizations contemplating global center setup in India.AI is becoming front runner to drive innovation & transformation for GCCs.

Algorithms will not only drive scores of business processes, but also build other self-intuitive algorithms, much as robots can build other robots. And rather than using apps, future users’ lives will revolve personalized algorithms to drive individual choices and behaviors .

Enterprises will license, trade, sell and even give away non-lynch pin algorithms and single-function software snippets that provide new opportunities for innovation by other enterprises. Enterprises will also partner with cloud-based, automated suppliers with the industry expertise to advice on ways to avoid future risk and adapt to technology trends.

Imaginative thinking ! but it’s no surprise that future value will come from increased density of interactions, relationships and sharing between people, businesses and things ̶ or what I call “ Algorithm Economy “ .The greater the maturity of algorithms , the greater potential value you can reap. We’ve seen interconnection coming of age for a while now and have invested heavily in a platform to empower enterprises with fast, direct and secure interconnections with business partners and network and cloud service providers.

Redefining Business Architecture with Algorithms

The term “algorithm economy” is relatively new, but the practical use of algorithms is already well established in many industries. In my opinion , CXOs must begin designing their algorithmic business models, both to capitalize on their potential for business differentiation, and to mitigate the possible risks involved.

Established businesses need to adopt a “bi modal strategy” and build what I called an algorithmic platform, completely separate from legacy systems, that harnesses repository of algorithms, interconnections, the cloud and the Internet of Things (IoT) to innovate, share value, increase revenues and manage risk.

New platforms based on this bimodal model should be far simpler, more cloud-based and more flexible than in the past, with the ability to add and remove capabilities “like Velcro” to support new short- and long-term projects. At the same time, IT should start divesting itself of older systems and functions that are outliving their usefulness or could be better done by other methods. The significant development and growth of smart machines is a major factor in the way algorithms have emerged from the shadows, and become more easily accessible to every organization. We can already see their impact in today’s world, but there is much work ahead to harness the opportunities and manage the challenges of algorithmic business.

CXOs should examine how algorithms and intelligent machines are already used by competitors and even other enterprises to determine if there is relevance to their own needs. The retail sector has long been at the leading edge of using smart algorithms to improve business outcomes. Today, many retail analysts believe that the algorithms that automate pricing and merchandising may soon become the most valuable asset that a retailer can possess. In HR function, algorithms are already transforming talent acquisition as they are able to rapidly evaluate the suitability of candidates for specific roles, but the same technology could easily be applied within an enterprise to allocate workloads to the right talent. In healthcare, the open availability of advanced clinical algorithms is transforming the efficiency of healthcare delivery organizations and their ability to deliver care. The practice of sharing and co-developing algorithms between enterprises with mutual interests could be relevant to most enterprises.

The Challenges of Algorithm Economy

The advances and benefits of algorithm economy will come hand in hand with obstacles to navigate. Whether the problems are anticipated or unexpected, as quantum computing becomes more pervasive, the implications have the potential to make or break organizations. For example, an extreme point of view is that any beneficial effects of algorithms on humanity may be nullified by algorithmically driven systems that are antithetical to human interests. Or, while an algorithmic business model may be deployed with good intentions, it could be manipulated by malicious humans to achieve undesirable outcomes. Undesirable, at least, from the point of the view of the person or organization that owns or controls the algorithm. Algorithms rely on the data they are fed, and their decisions are only as good as the data they are based on. Moreover, tricky ethical problems that do not necessarily have a “correct” answer will be inevitable, as a greater complexity of decision making is left in the hands of automated systems.

The scale of change that is made possible by smart machines and algorithm economy warrants considerable planning and testing. Enterprises that fail to prepare risk being left behind or facing unexpected outcomes with negative implications.

The Transformation required in Algorithm Economy

Making sense of all the data about how customers behave, and what connected things tell an organization, will require algorithms to define business processes and create a differentiated customer experience. Algorithms will evaluate suppliers, define how our cars operate, and even determine the right mix of drugs for a patient. In the purely digital world, agents will act independently based on our algorithms, in the cloud. In the 2020s, we’ll move away from using apps to rely on virtual assistants – basically, algorithms in the cloud – to guide us through our daily tasks. People will trust personal algorithms that thinks and acts for them. Take this to another level and the algorithms themselves will eventually become smart by learning from experience and producing results their creators never expected.

The Final Frontier

Therefore, we have to get the architecture of algorithms robust and steady to derive meaningful objectives. In essence, algorithms spot the business moments, meaningful connections, and predict ill behaviors and threats. CXOs need to be the strategic voice on the use of information, to build the right set of intelligent insights. Experience the Algorithm Economy and the ensuing strategic value for your enterprise . Are you geared up ?

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 consult@aiqrate.ai )

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

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

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

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

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

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

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

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

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

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

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

Existing Challenges

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

Strategic approaches of unlocking innovation with AI :

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

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

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

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

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

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

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

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

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

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

1. Accentuate strategic business acumen

2. Augment AI driven expertise for decision making

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

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

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

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

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

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

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

Strategic Steps towards AI led Transformation:

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

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

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

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

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

Play to win with AI

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

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

Winning thru competitive advantage with AI

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

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

Running the AI led transformation program

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

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

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

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

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

2. Put your money in building an AI led strategy

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

3.Are you ready for disruption?

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

4.Will our company take this seriously?

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

5.Is the leadership ready for the transformation?

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

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

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

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

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

Enter AI led strategy for boards

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

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

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

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

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

1.Can AI make the boards understand the industry dynamics

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

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

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

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

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

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

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

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

“Enterprise Data Engineering” may sound dated and not so cutting-edge buzzword now. But in this age of several disruptive ideas, concepts and technologies, Enterprise Data Engineering has caught-up with all the necessary advancements and capabilities equally if not better. With organizations becoming more and more keen to be data driven and embrace AI, there is heightened necessity of robust Enterprise Data Engineering Strategy (EDE Strategy). Organizations that do not have efficient EDE Strategy laid out would be ignoring or mismanaging their data asset. Inefficient use of data asset and insights would weaken the competitive edge and their competitors will leap ahead multi-fold in no-time.

EDE Strategy being the master plan of enterprise wide data infrastructure, is a foundational component for any meaningful corporate initiative in recent years. “Explosive growth of data” is being identified as a challenge, an opportunity, a new trend, a significant asset, a source of immense insights, etc. Social media, connected devices and detailed transaction logging are generating huge stream of data which simply can’t be ignored. In simple terms, a panoramic view and wholesome control of a data setup is essential to make data a powerhouse for organizational strategic growth.

Global IP traffic annual run rate is projected to reach 3.3 zettabytes by 2021 (TV & Smartphones together will be accounted for more than 60% of this IP traffic) -CISCO

Along with changing landscape of data, associated disciplines are now aligning to the new demands. EDE Strategy ensures that, these alignments are in line with the roadmap and caters to business users and their requirements.

EDE Strategy encompasses multiple strategy elements that are seeking increased attention now-a-days due to multiple advancements in & around them. Let me introduce some of those strategy elements here:

Automated Data Ingestion & Enrichment

Data Ingestion has now become seriously challenging due to variety & number of sources (social media, new devices, IoT, etc.), new types of data (text stream, video, images, voice, etc), volume of data and the increasing demand for immediately consumable data. Data Ingestion & Enrichment are almost coming together in several scenarios. Due to this, Data Ingestion is almost going off the ETL tools and is fast adopting Pythons and Sparks. Streamed data is not just about absorbing the data as it happens at the source. It is also about enabling AI within the ingestion pipeline to perform data enrichment by bringing together needed data sets (internal & external) and making the Ingestion-to-Insight transformation automatically in seconds/minutes.

ML/AI Models and Algorithms

ML/AI infuses smartness in building data objects, tables, views and models to oversee the data flow across data infrastructure. It uses intelligence in identifying data types/keys/join paths, find & fix data quality issues, identify relationships, identify required data sets to be imported, derive insights, etc. So, the advent of ML/AI in data engineering infuses intelligence into learning, adjusting, alerting and recommending by leaving complex tasks & administration to humans.

Cloud Strategy

The most significant shift seen in the digital world recently is the amount of data being generated and transported. Studies say, 90% of the data existing today were generated in just last 2 years. This is going to increase multi-fold in the coming years. The on-prem based infrastructure and provisioning processes aren’t agile enough to scale rapidly on demand. Even if this is managed, the associated overhead of buying, managing and securing the infrastructure becomes highly expensive and error prone. So, it is essential for organizations to opt for highly efficient and intelligent data platforms on cloud. Cloud offers several advantages across cost, speed, scale, performance, reliability and security. It is also maturing away from initial IaaS into newer services and players. But it doesn’t mean organizations simply initiate the cloud migration and get it done at the press of a button. There should be a carefully drafted Cloud strategy & execution roadmap for adopting cloud in alignment to organization requirements and constraints. Data and information on cloud has the potential to give organizations the flexibility, scalability and ability to discover powerful insights. Cloud also enables applying ML/AI for discovering dark data, monetizing opportunities and disruptive business insights.

Data Lake

Among the data-management technologies most significant space is of data lake. Data Lake is not a specific technology but a concept of housing “one source of truth” data for an organization. When implemented, data lakes can hold and process both structured and unstructured data. Though name indicates huge infrastructure, data lakes are less costly to operate if on cloud. It doesn’t require data to be indexed or prepared to fit specific storage requirements. Instead it holds data in their native formats. Data is then accessed, formatted or reconfigured when needed. Though data lakes are easy to initiate due to easily accessible and affordable cloud offerings, it requires careful planning and incremental adoption model for large scale implementations. In addition, the ever-changing data regulatory & compliance standards add to challenges of implementing and managing data lakes.

Master Data Management (MDM)

Though there is ongoing debate on whether MDM is needed where data lake is the central theme. Schema-on-Write, Schema-on-Read, unstructured data, cloud, etc., are the main contention points in these debates. No matter who wins these debates, it is important for us to know more about MDM while discussing on data engineering. Because, MDM is very essential for organizations to serve their customers/clients near real-time and with better efficiency. As Master Data is the key reference for transactions, typically all independent applications maintain them locally. This leads to redundancy, inconsistency and inefficiency when these data are brought together. It becomes a big challenge while integrating and processing these data due to complexity, chances for errors and increased cost. So, it is important to address MDM element in the EDE strategy carefully by considering organization objectives. Multiple models are practiced for implementing MDM like, registry, hybrid, hub, repository, coexistence, consolidation, etc., which would be discussed in my next articles.

Visualization

Visualization is an exercise that helps in understanding the data in a visual context like patterns, trends, relations, etc. This sounds like an external element or a client to the data engineering. Then, why is Virtualization an important element in EDE Strategy?

Gone are the days of graphs and charts for human analysis. Especially due to data deluge, fitting so much information in a graph or chart is almost impossible for human-beings. They need help in building meaningful representations by consuming huge amount of data. ML/AI is the answer to this wherein its models/algorithms show patterns and correlations by studying huge data-sets in no-time. So, to enable AI to process data, it is important to arrange and label the data suiting it the best. Hence, Visualization is an important aspect to be considered while constructing an Enterprise wide Data Engineering Strategy.

Conclusion

In this new era of data being the new oil, every interest required is being taken to improve the way data is received, cleansed, enriched, assembled and transformed.  EDE Strategy ensures establishing effective deployment/management guidelines and continuously improves them because data environments are living organisms. A solid EDE Strategy is highly essential to cater to the demands of this new age. All organizations must have EDE Strategy for realizing their digital vision. Ignoring to have a well laid Enterprise Data Engineering Strategy is as good as regressing in this competitive world.