Winning in 2021 with Cloud and AI Adoption and Digitizing Your Business to Thrive

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Click to learn more about author Asha Saxena.

The coronavirus pandemic has changed consumer behaviors, business models, and how people collaborate and work together. It has also accelerated the adoption of the digital economy to a level that is creating winners and losers in many industries. There are some macro trends that are emerging from the post-COVID-19 recovery that may have lasting impacts on the death of malls, retail, and mom-and-pop stores and the increased adoption of online shopping and digital experiences. As this Think With Google article points out, “The increasing use of technology to work, play, and stay connected have shaped new digital habits.” We can’t underestimate the power of habits. Once formed, they are difficult to reverse, especially when they make life easier. 

Were traditional brick-and-mortar retailers prepared for this dramatically accelerated new omni-channel digital shopping reality? Many were not. There isn’t really infrastructure for that yet (that isn’t Amazon, Target, or Walmart). E-commerce isn’t the only area that has become more digitally focused for consumers throughout lockdown. Video conferencing tools such as Zoom have provided a replacement for in-person meetings. The use of in-home exercise machines and virtual experiences such as Peloton have exploded in user adoption. Real estate platform Zillow even began rolling out new 3D interactive walk-throughs and floor plans for agents presenting virtual home listings to improve the customer browsing experience. Whether for transactional, educational, or personal purposes, it is clear that people have turned to digital channels for many aspects of their lives.  

Source: McKinsey & Company

I believe that many of the trends that have been embraced during this pandemic will have lasting effects and the use of digital technologies in all aspects of people’s lives will only continue, so companies (regardless of vertical or size) need to accelerate their digital adoption and transform their operations to meet consumers where they are now – and give them the experience they expect. Businesses must embrace digital transformation to keep up.

COVID-19 Has Created an Urgent Need for Organizations to Accelerate Their Digitization Efforts

In June of 2020, a Gartner Board of Directors Survey stated that 69% of boards of directors say that the effects of the pandemic crisis, the economic crisis, and the social crisis are accelerating digital business initiatives. According to the survey, “before the pandemic, most organizations moved their digital strategies forward at a steady pace. Leaders either wanted proof of success and didn’t feel a great urgency to invest more in digital or the organizational culture seemed resistant.” It also pointed out that when the COVID-19 pandemic hit, it forced employees to work remotely and businesses ramped up digital engagement with customers. According to the survey, many corporate directors increased the development of digital products and services to maintain and accelerate their customer engagement, and help them reach their revenue growth targets. According to a recent McKinsey & Co. article, “to stay competitive in this new business and economic environment requires new strategies and practices.” 

To win in 2021 businesses will need to continue to invest in diverse talent, data, AI, and digital cloud technologies to compete in the new remote hybrid work office world.    

 Digital Transformations Start with the Cloud

New research from an IBM Institute for Business Value report Covid-19 and Future of Business found that the COVID-19 pandemic has accelerated digital transformation at 59% of surveyed organizations. According to the executives surveyed, before the pandemic, many organizations seemingly distrusted their company’s technological capabilities and doubted the skills of their own workforces. But as companies were forced to move employees remotely and use technology tools, many found that those fears proved largely unfounded. They also found that the reliance on tech platforms became more acute, and those platforms – along with the corporate teams who use them – delivered real results. The COVID-19 pandemic has forever altered how organizations around the world operate. Some 55% of respondents say the pandemic has resulted in “permanent changes to our organizational strategy.”

An even larger 60% say COVID-19 has “accelerated process automation,” with 64% acknowledging a “shift to more cloud-based business activities.”  

Source: IBM Institute for Business Value

Benefits of a Hybrid Cloud Architecture and AI Adoption

In December 2020, Deloitte Insights published a Cloud Migration Trends and Forecast report stated that “COVID-19, lockdowns, and work from anywhere (WFA) has increased demand, and we predict that revenue growth will remain at or above 2019 levels (that is, greater than 30%) for 2021 through 2025 as companies move to cloud to save money, become more agile, and drive innovation.”

The Deloitte report went on to say that “when viewed at the total company level, very few systems will be only on-premises, only public cloud, or only private cloud. Most deployments will likely use a combination of a public cloud and a private environment that remain distinct entities but are bound together, an approach known as a hybrid cloud.” The report mentions new opportunities of unlocking value, which the cloud can support with benefits that include collaboration, automation, scale, innovation, and agility.

Last October Oxford Economics and IBM conducted a study in which they asked 6,000 CIOs, CTOs, and senior IT leaders in six industries (from organizations that are using cloud services in some capacity and at least experimenting with AI) about the importance of cloud architecture and AI in enabling their businesses to adapt and thrive. One high-level trend that emerged from the data was that those taking a strategic approach to adopting cloud were outperforming their peers in key metrics. The analysis of the survey data, which covered six industries and 26 countries, shows that data strategies, AI, and cloud – especially hybrid cloud architecture – are increasingly effective and intertwined. that’s because in a hybrid cloud all of your organization’s data can be sourced and managed from any cloud while limiting risk from migrating, loss, or downtime.  

Key findings from the study include: 

  • Cloud is becoming a foundational technology for the emerging AI era. Modernizing the business, automating decisions and workflows, and improving customer experiences are top motivators for AI adoption.
  • AI depends on data, and that data is increasingly complex. For 77% of respondents, a unified platform for cloud, data, and AI is seen as critical to long-term success, and a similar number say cloud is a critical foundation for Data Management and AI.
  • An integrated strategy for cloud and AI can deliver benefits in several different areas. Participants were asked to name the biggest advantages of using the cloud to build, modernize, and host applications that incorporate artificial intelligence, key areas including external business drivers such as better customer experiences, better-quality products or services, and more flexibility.
Source: IBM and Oxford Economics study

Assessing Cloud AI Maturity 

In the IBM and Oxford Economics study, respondents who lead their peers in terms of progress toward cloud adoption or cloud and AI adoption met the following criteria: 

Qualification Criteria for Cloud and AI Unifiers 

(highest maturity) n=809 respondents, 13.5% of respondents  

  •  Must have had at least 20% of applications in the cloud two years ago 
  •  Must have at least 40% of applications in the cloud today 
  •  21% or more of new apps incorporate AI 
  •  Must use cloud in combination with AI
  •  “Agree” or “Strongly agree” that a unified platform for cloud, data, and AI is critical to their organization’s success in the long term 

An Emphasis on AI Adoption 

Adoption rates for artificial intelligence are steadily growing, and the technology’s move into the mainstream is well underway, with early adopters seeing measurable results from their investments. The number of new applications that incorporate AI is relatively high – about 20% on average across survey respondents – with a small subset of that group incorporating AI into more than 30% of new applications. 

Respondents are also investing in specific AI-powered applications or other technologies that might incorporate or depend on AI. 

A majority of respondents are investing in: 

  • Predictive analytics (60%) 
  • IoT (61%) 
  • Machine learning (61%) 
  • Adoption rates are also high for virtual assistants (51%), 
  • Robotic process automation (RPA) (48%), and other areas 
Source: IBM and Oxford Economics study

Decision-makers across industries are looking to AI to address a variety of business issues, with deployment furthest along in the customer service function, along with IT and business operations. 

Unlocking Big Data and New Business Models 

hybrid cloud delivers the scalability and reliability of the public cloud with the security and customization of the private cloud. If basic cloud computing enables businesses to develop products faster and make full use of their data, then a hybrid cloud is the logical next step forward. 

Another IBM report stated that “a hybrid cloud approach helps enterprises quickly pivot their IT resources to match shifts in business strategy. It also sets an ideal foundation to harness artificial intelligence (AI) and data analytics.” 

The IBM report explains that to embrace the hybrid model, organizations must understand how their established IT components will connect with the two clouds and how this new approach will underpin all business applications. It’s a tricky process that will go smoother with expert help.  

For example, the hybrid model allows companies to rapidly spin up environments on either the public or private cloud at no additional cost to infrastructure. Scalability is now essential, and it will continue to be crucial in the future as digital-driven commerce increases the demand for new products in rapid time. A hybrid model taps unlimited cloud resources that can be scaled down as workflows change. It’s an approach that fully supports an enterprise’s commitment to innovation and speed.  

A hybrid cloud architecture also helps unlock the potential of a business’s greatest digital asset: data. Insight from data will create new business opportunities and ways to gauge the true pulse of the market, competition, and customer base. But for some enterprises, their current IT infrastructure might not be able to handle the computational resources needed to collect, sort, clean, and analyze high volumes of data continuously. Many AI-supported technologies are constantly gathering information from many data points and turning that information into real-time, actionable insight.

Overcoming Barriers to AI That Go Beyond the IT Department 

Unlocking the business use cases and adopting AI into the organization is not a simple process, and there are several different factors that organizations need to overcome. The Oxford Economics and IBM study highlighted some of these challenges across the 6,000 surveyed (from highest to least): 

  • Difficulty managing change
  • Difficulty creating and deploying an adoption plan 
  • Difficulty determining where data/application should be hosted    
  • Data Governance challenges 
  • Difficulty building and managing models with multiple AI providers 
  • Security and compliance issues 
  • Difficulty curating relevant data to leverage AI   
  • Operational challenges leveraging data across multiple clouds 
  • Immaturity of technology on the market 
  • Lack of available data 
  • Budget issues 
  • Lack of workforce skills 
  • Lack of support from employees 
  • Lack of support from the C-Suite 

Data Is the Essential Fuel 

Unifying cloud and AI development requires a solid foundation of Master Data Management.  “You can only have good AI or good results if you have tons of data, and the cloud providers are the ones with the most data for sure,” says Gus Shahin, CIO at Flex, a $25 billion electronics manufacturer with global operations. He further explains, “So they’re going to take the lead in coming up with better intelligence. There’s no doubt because they’re sitting on tons and tons of data.”  

Although certain kinds of data will still typically reside in on-premise or private clouds – including sensitive information around financial reporting and intellectual property –  these types of data still need to integrate with others to support advanced analytics, automation, and collaboration across functions. 

Availability of data is no longer a major problem for most companies – in the Oxford Economics and IBM study, only 17% cited it as a barrier to AI adoption – but managing it is an issue.   

Data Governance and difficulty curating relevant data to leverage AI are each cited by 29% of respondents as a barrier to AI adoption, and around a quarter say similar challenges hinder their cloud adoption. Having a unified platform for data, cloud, and AI is crucial to efforts in facilitating data-sharing and enabling analytics/machine learning efforts. 

For some organizations figuring out data, strategies go beyond their internal operations – they also pertain to difficulties in data-sharing (difficulty accessing and securing data) with external partners. And the data sorting and cleansing process could be made easier. According to a recent Fortune article, that is why Google and Microsoft’s venture arms are betting on a startup called Incorta, which delivers data to enterprise users directly without costly systems and processes like data warehousing and ETL severely limiting speed and agility. 

In a Medium article titled “A Practical Framework for AI Adoption” Pradeep Menon points out that, in general, more data to train implies a more usable model. In the past, the ability to train an AI/ML model was constrained. Storage and computing capabilities were limited. Over the last few years, cloud computing platforms are innovating. Storage is cheap. Computation is affordable. Data processing and model training at scale is possible at an acceptable cost. The old limitations are now obliterated.

Conclusion

Organizations now find themselves in a position where they must use data and AI to responsibly fuel innovation, business models, and partnerships. Funding digital strategy is a must in the post-COVID era.  

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