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Month: June 2019

02 Jun 2019
Why are so many companies struggling with digital transformation?

Why are so many companies struggling with digital transformation?

It’s become all but impossible to have a conversation about technology or business strategy without the words “digital transformation” cropping up. While the phrase has become ubiquitous at board level however, there will often be a large gap in understanding and engagement between business decision makers and their employees.

This divide was highlighted in our recent YouGov research, which looked at the attitude towards digital transformation and other common technical developments among employees of 500 UK businesses. The majority (57 percent) of respondents either admitted they had no idea what digital transformation actually was, or else misinterpreted it, with many assuming it meant moving to a paperless office.

Lagging on the global stage

Our research was in sync with the recent Dell Digital Transformation Index, which placed the UK 17th in its adoption of digital transformation. By comparison, developing nations such as India and Brazil featured much further up the list.

Most UK workers also did not consider their own companies to be innovative in our own research. When asked if they considered their employer to be a “digital innovator,” just nine percent of respondents agreed, with most saying they only adopted technology once it had become mainstream.

The UK may not be faring particularly well at the moment due to political and economic uncertainty. Companies are often averse to change at the best of times, and the current state of flux has exacerbated this trait and caused many to hold off on investing in new strategies and technology.

Failure to truly embrace digital transformation is the result of several factors. However, established companies often struggle with digitalisation more than newcomers as they must tackle years of legacy systems, while digital natives can skip the bureaucracy from day one and embrace the latest technology immediately.

The work-life divide

The struggle for companies to adopt digital transformation is particularly apparent when you compare the average workplace experience with technology to our increasingly digital home lives. Consumers have access to increasingly sophisticated and easy to use technology that provides a level of efficiency and control many companies can only dream of. For example, a parent could use an app like Apple Screen Time to manage how their children access digital media, approving or denying request for more bandwidth in real time.

By comparison, requisitioning additional IT resources is often still a slow and painful process in the workplace. Many workers must endure long waits and being passed back and forth between different departments before they finally have their request fulfilled. The main culprits here are inefficient processes coupled with a siloed approach that means different departments do not communicate clearly.

Poor processes will waste time for both the employee making the requisition, and the departments fulfilling it, impacting both morale and productivity. When the same issues are encountered by customers, the company’s customer satisfaction and retention will suffer, impacting its profits and growth.

Making digital transformation work

One of the most common issues holding back digital transformation efforts is a tendency to focus on technology rather than business objectives and user demand. Firms can often feel pressured to be seen embarking on digital projects, particularly in order to keep up with competitors. However, implementing new technology without properly considering its impact can easily cause more problems than it solves. As Bill Gates famously put it, “automation applied to an inefficient operation will magnify the inefficiency”.

Organisations should start any digitalisation project with a clearly defined set of objectives outlining what problems the new solution will solve and what needs it will meet. It’s particularly important to keep the user experience – whether it’s the internal workforce or external customers – in mind at all times.  

As part of this process, digital transformation projects should be inclusive projects that involve all relevant stakeholders, rather than being relegated to a niche IT concern. Highlighting this, our research found that 42 percent of employees feel their businesses do not integrate data and processes across departments well. Digitalisation affects the whole business, so there should be a cross-departmental team involved to represent various interests and experiences. A more inclusive, united approach will allow various elements of the workforce to have a say in shaping the project and will also help to break down the interdepartmental siloes that so often cause inefficiencies.

The final element vital for making digital transformation work is strong leadership. The board and other senior decision makers have a vital role to play in both driving forward digital projects, and also overseeing the required changes in business culture. A concerted effort must be made to get the wider workforce aware of changes being planned, and actively engaged in the process.

Senior executives have the power and responsibility to ensure that not only is digital transformation a leading business objective, but that their employees are aware of what it means and how it will benefit them. With decisive leadership from the top and an inclusive approach at all levels of the business, organisations can demystify digital transformation and continue to develop and grow.

Source: https://www.gigabitmagazine.com/big-data/cherwell-why-are-so-many-companies-struggling-digital-transformation

01 Jun 2019
Why Businesses Keep Failing to Make the Most of AI

Why Businesses Keep Failing to Make the Most of AI

According to a PricewaterhouseCoopers study, 20 percent of executives plan to incorporate AI across their enterprises in 2019. Over the past year, countless organizations and Fortune 500 companies have boasted about their AI strategies. When it came time to put those strategies into practice, however, they realized that what they called a “strategy” was little more than tools without guidance.

Businesses today have the resources, knowledge and incentive to create effective strategies behind their AI implementations. Despite these capabilities, few companies take the time to do so. They acquire the physical tools to practice AI, but they often fail to put the same effort into learning why it’s valuable and what challenges AI poses.

By purchasing new technology before designing a strategy to make the most of it, companies attempting to get ahead of their industries ironically set themselves back. To correct this misguided approach, businesses must design real, actionable strategies before they let AI take the wheel.

A driverless car without a motor.

Imagine a company in the 1980s that saw the IT revolution coming but decided to build an IT strategy purely on mainframes. Even if that company’s leaders had the right general idea, the flawed execution would not have helped the business grow.

The same thing is happening today in AI. Companies need both the tools and the wisdom to use them properly. Leaders who want to stop relying on tech vendors have the good of their organizations in mind, but a lack of strategy means their initiatives amount to purchase orders.

Without consideration for use cases and applications, businesses that think AI will fix their problems risk burning out on some incredibly promising tools. To avoid that fate and design a strategy that gets the most out of the AI revolution, keep these three concepts in mind:

1. The application must fulfill a specific need.

A company’s infrastructure layer determines how AI technologies integrate with existing systems. The application layer determines how those technologies benefit your business.

IBM’s Watson is a powerful machine, but Watson itself is an infrastructure tool. Watson’s various domain arms (financial, healthcare, etc.) represent the applications of the AI. In the banking world, Watson’s robotic intellect helps bankers sniff out false positives in money laundering, reducing customer service times in the process. That’s a specific use case — a perfect example of strategic application.

Successful AI strategies tend to be niche-specific. Rather than seek AI empowerment throughout your company, identify a few key areas that could benefit from AI tools before finding the tools that fit those needs. Ensure your infrastructure can handle the integrations, filling in any gaps of your application layer.

2. The organization must understand microservices.

Think about how AI innovation works across different layers within your company. In the infrastructure layer, containerization (also known as modularity or microservices) helps companies implement tools in specific ways without needing to adopt an entirely new infrastructure.

IBM’s Open Banking Platform acts as a plug-and-play option for existing financial institutions to integrate microservices into their operations. Such a solution lets participating banks leverage microservices as cloud APIs to nurture fintech collaboration, streamline processes and build new revenue streams.

AI does not operate like other tech tools. Don’t look at the existing system and say, “Any AI that comes in must be able to work with this system.” Instead, look to the market with a system-agnostic approach. Find opportunities for new tools to come in and fix specific problems within your organization.

3. The tools must be real AI, not data scientists.

Data is everywhere in today’s market. Fifty-seven percent of respondents to a MicroStrategy study say they streamline their decision-making via data. Real AI uses data, but it doesn’t need a trial period and access to your company’s databases to prove its worth.

If a vendor comes forward and asks for access to data and a week (or a month) to generate insights based on that information, that vendor is not a true AI vendor. In reality, groups like these are just data consultants doing professional services work.

Good AI vendors empower their clients with general infrastructure and niche applications. They don’t care where the data comes from. Quality vendors should have no problem normalizing, unifying and using data to deliver actionable information.

Just because a company uses AI does not mean it benefits from those advanced tools. True AI empowerment arises from an action-specific strategy rather than the purchase of a tool that claims to do it all. Consider the long-term, real-world ramifications of AI investments before making the leap.