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 )

When it comes to AI, businesses think ambitiously. Nearly 85% of executives believe AI will allow their company to obtain or sustain a competitive advantage in the marketplace. Contrastingly, just one in five companies have incorporated AI into their organization and less than 39% of companies have an AI strategy.

Exactly why is AI so disruptive to traditional business models and traditional notions of industry competition? A useful way to analyse the situation is by looking at Porter’s model of the five forces of industry competition and exploring how artificial intelligence is impacting each of the various forces.

According to Michael E. Porter, in one of his landmark books, titled Competitive Strategy, “In any industry, whether it is domestic or international or produces a product or a service, the rules of competition are embodied in five competitive forces: the entry of new competitors, the threat of substitutes, the bargaining power of buyers, the bargaining power of suppliers, and the rivalry among the existing competitors.”

Figure 1: Porter’s Five Forces

Let’s look at each of these five forces and examine the role and impact of AI:

The entry of new competitors

There’s no doubt that AI is changing the nature of competition. Today, it’s not just traditional industry competitors you need to worry about, but new entrants from outside your industry, equipped with new AI based business models and value propositions.

This is often tech giants and startups that have envisioned and built a new business model from the ground up, powered by a new platform ecosystem for AI. They’re leveraging the familiar social, mobile, analytics and cloud technologies, but are often adding in personas and context, intelligent automation, chatbots and the Internet of Things, to further enhance the value proposition of their platform.

Why can new entrants move in so easily? Digital business changes the rules by lowering the traditional barriers to entry. A digitally based business model requires far less capital and can bring large economies of scale for example. Read more about how AI Startups are creating disruptive competition here.

The threat of substitutes

The threat of substitutes is high in many industries since switching costs are low and buyer propensity to substitute is high. For example, In the taxi services, customers can easily switch from traditional models to the new digital app based taxi services, employing AI routines to create differential pricing and intelligent route mapping to increase margin as well as decrease price for the customers. Propensity to switch from the traditional model is high due to consumer wait times for taxis, lack of visibility into taxi location and so on.

In case of BPO industry, the advent of AI has been extremely disruptive, with their clients completely substituting their services with building in-house automation offerings and circumventing their need, sometimes completely. Read more in detail about the disruption of BPO/BPM by AI here.

The bargaining power of buyers

Perhaps the strongest of the five forces impacting industry competition is the bargaining power of buyers since the biggest driver of AI and digital business comes from the needs and expectations of consumers and customers themselves.

This bargaining power lays out a new set of expectations for the AI and digital customer experience and necessitates continual corporate innovation across business models, processes, operations, products and services.

For example, the most used instances of chatbots are through customer support, and now they are heading in the direction of changing the retail sector altogether. The expectations of the Millennials are directing the course of this new technology. This is why chatbots have the burden to exceed the expectations in the retail sector.

Also, in another example, in the customer facing marketing aspect, AI is causing circular rise in customer expectations as rise of expectations, mostly from millennials, has forced the companies to adopt an AI solution to the problem, which further has emboldened their expectations. Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases. Read more about how AI is accentuating customer experience to address rising expectations Here.

The bargaining power of suppliers

Suppliers can accelerate or slow down the adoption of a AI based business model based upon how it impacts their own situation. Those pursuing AI models themselves, such as the use of APIs to streamline their ability to form new partnerships and manage existing ones, may help accelerate your own model.

Those who are suppliers to the traditional models, and who question or are still determining their new role in the digital equivalent, may use their bargaining power to slow down or dispute the validity or legality of the new model.

Good examples are the legal and business issues surfacing around the digital-sharing economy (i.e. ride-sharing, room-sharing etc.) where suppliers and other constituents work to ensure the AI based business model and process innovations (like route optimization, or deep customer behaviour analysis using private data) still adhere to established rules, regulations, privacy, security and safety. This is a positive and needed development since, coupled with bargaining power of buyers, it can help to keep new models “honest” in terms of how they operate.

The rivalry among the existing competitors

A lot of organisations are in exploratory stages as they realise that their strategy and customer engagement needs to get smarter. The combination of optimism and fear that clients today have shows that for them it is a competitive necessity to adopt AI and digital technologies.

In 20 years, probably every job will be touched by AI. The technology is growing universally. WhatsApp and Facebook — everything is driven by AI. And what this means is that on the job front, there may be blood. Once AI, ML, and virtual and augmented reality go mainstream, these technologies will prove to be a huge job creator.

But currently, the most competitive space in AI adoption is in the implementation of chatbots across industries and functions. While we might see chatbots starting to appear through the likes of Facebook Messenger and WhatsApp platforms in the coming 12 months, and will be dedicating teams of engineers to train the platforms, rather than relying on the general public. Read more about the competitive atmosphere and underlying need to better customer experience using chatbot here.

How AI will transform Strategic Planning Process

How can managers — from the front lines to the C-suite — thrive in the age of AI? In many ways, the lack of understanding when it comes to AI is due to the variety of ways AI can be implemented as a part of strategic planning for a business. Different industries, or even different companies within the same industry, may use AI in different ways. Ping An, which employs 110 data scientists, has launched about 30 CEO-sponsored AI initiatives that support, in part, its vision – that technology will be the key driver to deliver top-line growth for the company in the years to come. Yet in sharp contrast, elsewhere in the insurance industry, other large companies’ AI initiatives are limited to experimenting with chatbots. Obviously, integrating AI is not going to be simple. There will be a massive learning curve for organizations before they’re able to start implementing AI effectively. But the core shift in strategic planning will happen in the following ways:

AI will take over almost all Administrative Tasks

According to an HBR report, managers across all levels spend more than half of their time on administrative coordination and control tasks. (For instance, a typical store manager or a lead nurse at a nursing home must constantly juggle shift schedules because of staff members’ illnesses, vacations, or sudden departures.) These are the very responsibilities that the same managers expect to see AI affecting the most. And they are correct: AI will automate many of these tasks.

Figure 2: Source –  HBR (How Artificial Intelligence Will Redefine Management)

For example, in case of report writing The Associated Press expanded its quarterly earnings reporting from approximately 300 stories to 4,400 with the help of AI-powered software robots. In doing so, technology freed up journalists to conduct more investigative and interpretive reporting.

Strategy Managers will focus more on Judgement-oriented Creative Thinking Work

The human factor, which AI still cannot permeate – the application of experience, expertise and a capacity to improvise, to critical business decisions and practices – need to be focused on by strategy managers. Many decisions require insight beyond what artificial intelligence can squeeze from data alone. Managers use their knowledge of organizational history and culture, as well as empathy and ethical reflection. Managers we surveyed have a sense of a shift in this direction and identify the creative thinking skills and experimentation, data analysis and interpretation, and strategy development as three of the four top new skills that will be required to succeed in the future. And since the potential of machine learning is the ability to help make decisions, the AI technology would be better placed as an assisting hand than administrative mind.

Think of AI not as Machines, but Colleagues

Managers who view AI as a kind of colleague will recognize that there’s no need to “race against a machine.” While human judgment is unlikely to be automated, intelligent machines can add enormously to this type of work, assisting in decision support and data-driven simulations as well as search and discovery activities. In fact, 78% of the surveyed managers believe that they will trust the advice of intelligent systems in making business decisions in the future.

Not only will AI augment managers’ work, but it will also enable managers to interact with intelligent machines in collegial ways, through conversation or other intuitive interfaces.

For example, Kensho Technologies, a provider of next-generation investment analytics, allows investment managers to ask investment-related questions in plain English, such as, “What sectors and industries perform best three months before and after a rate hike?” and get answers within minutes.

Design Thinking needs to be adopted both ways – Managers & AI

While managers’ own creative abilities are vital, perhaps even more important is their ability to harness others’ creativity. Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. Creative thinking and experimentation is a key skill area that managers need to learn to stay successful as AI increasingly takes over administrative work. ‘Collaborative Creativity’ is the operating word here.

But this doesn’t mean that design thinking necessarily need to become a forte exclusive to managers. Even though AI engines may not have reached radical thinking and improvisation as humans, AI algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. This calls for Divergence from More Powerful Intelligence To More Creative Intelligence in AI.

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

Create New Business Processes manifested from Augmented Working Strategy

Simply put, my recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment. If the current shortage of analytical talent is any indication, organizations can ill afford to wait and see whether their managers are equipped to work alongside AI. This calls for change in business processes, and the way they are implemented itself. To navigate in an uncertain future, managers must explore early, and experiment with AI and apply their insights to the next cycle of experiments.

AI augmentation will drive the adoption of new key performance indicators. AI will bring new criteria for success: collaboration capabilities, information sharing, experimentation, learning and decision-making effectiveness, and the ability to reach beyond the organization for insights.

Accordingly, organizations need to develop training and recruitment strategies for creativity, collaboration, empathy, and judgment skills. Leaders should develop a diverse workforce and team of managers that balance experience with creative and social intelligence — each side complementing the other to support sound collective judgment.

Final Word

While oncoming AI disruptions in Management Principles and Strategic Planning space won’t arrive all at once, the pace of development is faster and the implications more far-reaching than most executives and managers realize. Those managers capable of assessing what the workforce of the future will look like can prepare themselves for the arrival of AI.

In recent years, there has been a lot of hype around “big” data in the marketing world. Big data is extremely helpful with gathering quantitative information about new trends, behaviors and preferences, so it’s no wonder companies invest a lot of time and money sifting through and analyzing massive sets of data. However, what big data fails to do is explain why we do what we do.

“Thick” data fills the gap. Thick data is qualitative information that provides insights into the everyday emotional lives of consumers. It goes beyond big data to explain why consumers have certain preferences, the reasons they behave the way they do, why certain trends stick and so on. Companies gather this data by conducting primary and secondary research in the form of surveys, focus groups, interviews, questionnaires, videos and other various methods. Ultimately, to understand people’s actions and what drives them to your business (or not), you need to understand the humanistic context in which they pursue these actions.

Human Behavior vs Human Data

It’s crucial for successful companies to analyze the emotional way in which people use their products or services to develop a better understanding of their customers. By using thick data, companies can develop a positive relationship with their customers and it becomes easier for those companies to maintain happy customers and attract new ones.

Take for example Lego, a successful company that was near collapse in the early 2000’s because they lost touch with their customers. After failed attempts to reposition the company with action figures and other concepts, Jørgen Vig Knudstorp, CEO of the Danish Lego firm, decided to engage in a major qualitative research project. Children in five major global cities were studied to help Lego better understand the emotional needs of children in relation to legos. After evaluating hours of video recordings of children playing with legos, a pattern emerged. Children were passionate about the “play experience” and the process of playing. Rather than the instant gratification of toys like action figures, children valued the experience of imagining and creating. The results were clear; Lego needed to go back to marketing its traditional building blocks and focus less on action figures and toys. Today, Lego is once again a successful company, and thick data proved to be its savior.

While it’s impossible to read the minds of customers, thick data allows us to be closer than ever to predicting the quirks of human behavior. The problem with big data is that companies can get too caught up in numbers and charts and forget the humanistic reality of their customers’ lives. By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.

The Perils of Big Data Exceptionalism

As the concept of “Big Data” has become mainstream, many practitioners and experts have cautioned organizations to adopt Big Data in a balanced way. Many qualitative researchers from Genevieve Bell to Kate Crawford and danah boyd have written essays on the limitations of Big Data from the perspective of Big Data as a person, algorithmic illusion, data fundamentalism, and privacy concerns respectively. Journalists have also added to the conversation.  Inside organizations Big Data can be dangerous.  People are getting caught up on the quantity side of the equation rather than the quality of the business insights that analytics can unearth. More numbers do not necessarily produce more insights.

Another problem is that Big Data tends to place a huge value on quantitative results, while devaluing the importance of qualitative results. This leads to the dangerous idea that statistically normalized and standardized data is more useful and objective than qualitative data, reinforcing the notion that qualitative data is small data.

These two problems, in combination, reinforce and empower decades of corporate management decision-making based on quantitative data alone. Corporate management consultants have long been working with quantitative data to create more efficient and profitable companies.

Without a counterbalance the risk in a Big Data world is that organizations and individuals start making decisions and optimizing performance for metrics — metrics that are derived from algorithms. And in this whole optimization process, people, stories, actual experiences, are all but forgotten. By taking human decision-making out of the equation, we’re slowly stripping away deliberation — moments where we reflect on the morality of our actions.

Where does Thick Data come from ?

Harvard Business Review (HBR) defines thick data as a tool for developing ‘hypotheses’ about ‘why people behave’ in certain ways. While big data can indicate trends in behavior that allow marketers to form hypotheses, thick data can fill in the gaps and allow marketers to understand why their customers are likely to take certain actions.

While ‘thick data’ is recently receiving a great deal of attention among big data thought leaders, it’s not a new concept. There’s little difference between ‘thick’ data and ‘prescriptive analytics,’ both of which represent advanced maturity in marketing big data. By shifting your focus from predictive big data to forming and testing hypotheses, marketers can better understand how their buyers will act in the future.

Historically, big data has been transactional, while thick data has been qualitative. For data-driven brands of years past, insights into consumer behavior were typically derived from behavioral observation, voice of the customer (VOC) or Net Promoter Score (NPS) surveying, focus groups, or other time-intensive research methods.

Today, insights into consumer behavior can come from a variety of sources. Thanks to social media, internet of things technologies and other drivers of big data, marketers can gain insight into why humans act the way they do with data sources such as:

• Online or Mobile Behavior
• User-generated social media content
• 3rd-party transactional data

Studies indicate that currently, 95% of brand research into consumer preferences is performed manually, using methods such as surveying or focus groups. However, in an era where consumers produce thousands of insights each day from mobile usage, online shopping and social media updates, the insights are easy to obtain.

Finally, will Thick Data take over Big Data ?

This is not to say big data is useless. It is a powerful and helpful tool companies should invest in. However, companies should also invest in gathering and analyzing thick data to uncover the deeper, more human meaning of big data. Together, thick data and big data give you an incredibly insightful advantage.