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The Next Corporate Technological Revolution | AI, Cloud and Blockchain

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Connected Intelligence
  • Sustainable Growth and Tech Trends
  • Cloud
  • AI
  • Emerging Technologies


Publication | Update: Sep 2020

The rapid evolution of technology makes it confusing to explore, test and implement technology that is relevant to the business with the greatest chances of success.

To help get an expert perspective on the matter, Savannah Group recently hosted an event for a selection of CIOs, CTOs and CDOs and invited Dr Jai Menon, Chief Scientist at Cloudistics to lead a discussion on the major technological changes driving change in organisations.

FACTORS DRIVING THE TECHNOLOGICAL REVOLUTION: AI

Data Labelling Remains A Key Challenge

AI is changing the way that businesses undertake tasks. Menon outlined a fundamental shift in paradigm in working with IT. In the past, IT professionals programmed computers, but AI has driven a very important shift towards teaching computers instead. This has been dubbed Software 2.0, with Software 1.0 representing the programming ways of the past. In Software 1.0, computing problems were broken down into manageable chunks to resolve difficult problems. Following that, programming was undertaken to deliver the desired result. Software 2.0 tackles problems very differently. Instead, data is fed into the computer and the machine learns what is important and why. It figures out what matters for itself, and optimises results accordingly.

Implementing AI and machine learning is, unsurprisingly, not straightforward. It’s important to remember that these technologies are still in their infancy so there are still many challenges to overcome which will get easier as understanding grows. One of the problems that companies are commonly running into at the moment is the effort needed for the labeling of data needed during the training phase when the AI is being taught how to sort and categorise the data it has access to. As Jai explains, “It takes a lot of human effort to label the data to the computer in the first place so that the machine can learn.” There is a massive volume of data required to help machines learn effectively, and getting that data in shape can require significant human resource. An active area of ongoing AI research is how to entirely eliminate the need for training the AI, or for how to achieve training with only a small amount of labelled data.

Real World AI Use-Cases Are Becoming More Common

They are also seen in healthcare for reading medical images and for drug development. In agriculture they are being utilised for making decisions on where to plant, when to plant and what to plant. When combined with the Internet of Things, AI can be used to detect where maintenance is required, such as with leaks in oil and gas pipelines too. All of these types of helpful applications can increase competitive advantage, and companies not using AI can find themselves under significant competitive pressures. Menon also provides an example of the use of AI within the Technology space. As he explains of Google, “They’re using AI in a massive way across all of their data centres and have reduced the amount of energy that they’re consuming by around 15% in their data centres.” If Google can achieve these types of savings within their datacentres it is likely other businesses will be able to save money on their IT infrastructure in a similar way.

An approximation is that six percent of his customers are already deploying AI solutions while sixty percent are gathering information to pinpoint ways in which AI and machine learning may be of use to them. Change is being driven in many cases by product teams, engineering teams and service and support departments rather than higher up in the organisational echelons.

Organisational Clarity Around The Problems AI Should Be Solving Remains Elusive

The discussion highlighted that one of the most difficult challenges is finding the right problems to apply AI and machine learning to in the first place. Without clearly defined goals, AI may not deliver anything of value to the business and may be a waste of time and money. Attracting talent to deliver AI and machine learning advancements can also be an issue, given that Goliaths like Google and Facebook attract the best and brightest in this field. The discussion highlighted that if done well, however, AI can at a minimum help organisations process dull tasks, and there is likely to be applicability to most industries in some way or another.

CLOUD

Cloud technology is not new but is still in a state of evolution, and the latest cloud technology offers businesses the opportunity to both increase efficiency and to cut its IT infrastructure costs. At the same time, cloud technologies have opened up a tremendous amount of storage capacity for both individuals and corporations, allowing previously inconceivable business innovations to be implemented in a cost-effective manner. Critically, cloud facilitates big data developments and use.

New paradigms such as Serverless and containers are Emerging

Serverless technology is a new way to develop applications for the cloud. Jai states, “I see the cloud providers putting a lot of energy and effort into those technologies. I think serverless will be big.” The name is a bit of a misnomer as serverless technology does indeed use servers. It can be otherwise described as “event-driven programming”. When a specific event happens, this triggers a certain piece of code to run. The benefit of this for organisations is that they need not concern themselves with how many servers are needed or capacity, or even which servers to run. While it is early days for serverless, this cloud application is predicted to be big. That said, thought needs to go into its use so that it is optimised effectively. The major benefit from a cost perspective however is that companies only pay for capacity when events are triggered.

The use of container technology (a method where applications are packaged so that it can be run, with its dependencies, isolated from other processes) is also advancing rapidly.

Hybrid Cloud May Be The End Result

There is also an evolution relating to the different types of cloud, such as the public cloud (storing your data on Amazon or Googles servers) and the private cloud (storing data on your own cloud servers), with a recent trend away from the public cloud in some quarters. Factors impacting on the use of these different types of cloud include cost and security as well as control. There has been concern among IT functions due to a lack of control and around having sufficient capacity – issues which have also influenced changes in this area. Security is a particularly big concern, particularly around the public cloud.

Analysis from 451 Research indicates that by 2020 38% of apps will be in the private cloud and 30% in the public cloud. There is a lot of movement between each. As yet unpublished analysis from IDC indicates that 80% of enterprises that moved to the public cloud relocated at least one application back to the private cloud. Major technology players are also responding to these needs. For example, Microsoft’s Azure proposition may be considered a private cloud. Menon says “I really believe that some form of a hybrid cloud is where the world will get to.” This is likely to include a mix of different cloud solutions.

While cloud is the most advanced of the technologies that are driving a revolution in corporations, there is no doubt that change is still to come in this area as the technology and its uses continue to evolve.

BLOCKCHAIN 

Blockchain too is having an impact on corporations, but this is in the earlier stages of inciting a technological revolution. While slower in bringing forth applications that will transform organisations and the way that people do business, it is highlighted as a “technology of tomorrow”. There has been a lot of hype around blockchain, and this is problematic as it makes it more challenging to see its genuinely useful applications for business. Menon predicts that 90% of the hype will not come to fruition.

Blockchain Brings Trust and Security Advantages

Blockchain is a type of database with specific, unusual properties. One such property is the fact it is immutable. Nothing can be changed or deleted later. Transactions can be reversed, but what is in the blockchain cannot be removed. This has tremendous benefits because it makes fraud impossible. Further, it allows trust to be developed between different entities. As Jai puts it, “the thing that makes it really interesting, is that it lets you create trust between different entities that don’t have trust amongst each other, without the need for any central authority anywhere.” There is simply no way that a transaction came from someone other than indicated in the blockchain and this removes the need for a central authority to validate transactions.

The Financial Sector Is Leading the Way But It Is Early Days

Given the fact that there is no way to counterfeit transactions in blockchain, for obvious reasons many early applications of this technology have been advanced in the financial sector. Menon observed that in 2017, 80% of applications could be seen in financial industries. Yet already by 2018, this figure was closer to 50%, indicating the speed of advancement of take-up in other industries. As Menon puts it, “Blockchain is still in its early days. 1% say they have adopted it. One per cent. Eight per cent say they are in active experimentation, and 80% express no interest today.”

Aside from in the financial industry, other applications of this emerging technology include the tracking of records such as in the real estate industry or within the supply chain. It is also being put to use in retail, and even in the jewellery industry. Menon highlights the case of De Beers, the diamond company using blockchain to track where the jewel was mined through to where it is right now. This reduces the time spent on this type of tracking from days down to minutes.

Applying Blockchain Appropriately Will Drive Results

As with the other ground breaking technologies highlighted, a word of warning is issued around its use. There is little point jumping on the bandwagon and implementing blockchain just for the sake of it. It is most likely to be of use where immutability is critical. There have been numerous inappropriate uses of blockchain where immutability is not important. This may mean that the technology is not being applied in the most helpful ways by some organisations and in some cases, this can lead to poor performance and a lack of results.

The best investments in this revolutionary technology are likely to be well thought out and focused on the benefits that blockchain can bring for specific use cases, based on its unique strengths. Implementing blockchain because it is “cool” probably does not make good business sense. As with anything, common sense good business practices should be applied to selecting new technologies like this for resolving corporate problems.

Summary 

The most advanced technology, cloud, is still transforming due to security concerns and is most likely to evolve into hybrid cloud solutions which might include private and public cloud as well as serverless. The least developed, blockchain, is subject to a lot of hype, and fewer practical applications have been developed for the real world. Yet this technology has tremendous potential in the finance sector, and for governance and trust. Meanwhile, AI is fast advancing with plenty of uses identified, but challenges with data labelling slowing the revolution.

Jai Menon, Chief Scientist at Cloudistics.

Dr. Jai Menon is a global technology leader with over three decades of experience in enterprise and internet technologies across IT, telecom and media industries. Jai’s distinguished career has seen him serve as CTO of some of the largest systems businesses in the world including IBM and Dell. He is recognised as an IBM Fellow and was a pioneer behind the creation of RAID technology – now a $ 20 billion industry.

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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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