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DIGITAL TRANSFORMATION: WHY TECHNOLOGY IS AN ONGOING EXPERIMENT FOR ENTERPRISE

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By Dnyanesh Kale, Advisory Director, 6point6

The key challenge to successfully execute a digital transformation is well understood. More often than not, existing business services have often been built upon a web of disconnected siloes across different departments, resulting in incomplete and ineffective architecture.

In the past, this has typically led to IT teams exploring new technologies, like RPA, to place a ‘sticking plaster’ on top of legacy systems to extend their life. However this  only results in delayed investment in building strategic IT capabilities and will cause further pain, as more money is spent just to keep the lights on. A temporary and ultimately very costly solution to what really requires a fundamental shift in mindset and practice.

Technology is an ongoing experiment as both business and technology landscapes are continuously evolving. Enterprises are continuously subjected to changing strategic as well as operational environmental pressures. With business and technology constantly evolving, the formula to future-proof any digital transformation is clear – change tact and approach it with the same ongoing and measured strategy as you would approach your business’s goals.

 

Experiment setup: Giving IT leaders a stake in the game It is a human tendency to more clearly remember one’s failures and fear the unknown. Everyone has read and feared those failed mammoth multi-year technology implementation projects. However the nature of technologies has now significantly evolved, which enables their iterative and incremental deployment for early value creation.

While  business leaders’ attitude towards new technologies is changing, there is often a reluctance to include IT leaders as a core part of business decision-making. Until now, the challenge has been to shift perception of IT leaders as ‘kids looking to play with new toys’ to a valued player, who will enable the strategic success of the enterprise.

Having a strong IT leadership can help establish a vision that identifies how to use technology to influence business outcomes. It takes a strong IT leadership, with multi-disciplinary skill set, that identifies the right fit between business outcomes that can be influenced and new technologies that can be applied. To orientate technology in tandem with a business’s strategic initiative is an approach worth taking to fully realise the benefits of digital transformation.

For enterprises the pressure to become adaptable is on. Organisations that empower IT leadership to propose new technologies and the autonomy to create a sandbox to pilot these will realise a more fine-tuned approach to targeting technology that helps incrementally meet the real needs across the business.

 

Making the experiment repeatable: FailFast

One of the most important approaches any business can undertake during a digital transformation journey is to not be afraid of failure. A fail fast strategy offers many lessons towards understanding shortcomings and what needs improvement. Most importantly, it offers employees a chance to experiment and champions their learning from mistakes.

Agile software development offers a perfect environment for Hypothesis-based learning. We have an idea, we build something, which we can rollout quickly and to a high standard with the help of Continuous Integration (CI) and automated regression testing capabilities. This gives us the opportunity to get early feedback by gathering data on the usage of the new capability and quickly address the aspects that need improvement or withdraw the ones that fail to impress the user community. To make agile software delivery fully successful IT divisions need to adopt a human-centric strategy for knowledge management while experimenting with new technologies

To speed up and simplify digital transformation initiatives, organisations that have set up centralised capabilities for experimenting with new technologies are far more likely to benefit. The need for greater user involvement throughout the adoption of new technology is vital. To enable early feedback and to fail fast, IT divisions need to have mechanisms to quickly rollout technology features in iterative and incremental way. From this, they need to invest in tooling and delivery pipelines that allow Continuous Integration/Continuous Delivery (CI/CD). This will significantly improve reliability and the success rate of the complex deployments. To further achieve consistency and standardisation, IT organisations need to invest in building a common platform providing a set of common and coherent services that are developed and managed centrally by teams called the Platform Ops. Often organisations call the teams that provide Platform Ops: a core platform team or core services.

By taking an iterative and incremental method to deliver new technology and a fail fast approach, businesses will be able to frequently measure each scale of change and its effectiveness. The key is to ensure your business has a common understanding of what powerful change looks like to avoid wasted resources and failed business services. That level of agility will hit the regular cadence in your digital transformation journey.

 

Technology

HOW ARE SPEECH RECOGNITION AND AI FIGHTING FRAUD?

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Nigel Cannings the founder of Intelligent Voice

Nigel Cannings is the founder of Intelligent Voice

 

Speech recognition and AI provide innovative methods for businesses to significantly develop and improve their fraud detection systems. With the technology and techniques used by fraudsters rapidly changing, AI can evolve and adapt to provide more comprehensive protection, assisted by the use of machine learning. The acceptance of AI as a crucial asset to fraud detection and prevention is already being recognised, with 31% of CIOs having already reported the implementation of AI systems in their business, and a furth 23% expressing intent to have the technology deployed within the next year. Crucial to the effective implementation of this technology, however, is having a basic understanding of how it functions and will assist business needs.

 

What are the roles of AI and machine learning in fraud detection and prevention?

AI can take a variety of forms, with the core systems required for anti-fraud measures being Conversational AI, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR). Automated, voice-enabled applications rely on the use of Conversational AI to allow efficient communication between technology and humans. ASR is the model tasked with translating verbal data into different formats, facilitating the recording and processing of data. The crucial bridging of the gap between the rules of human language and machine learning is carried out by NLP systems, allowing technology to process the sentiment and intent that can be derived from human interaction.

Together, these AI systems are used to both develop and augment machine learning models. The machine learning process involves the application of data from previous interactions with the intent to enable algorithms and analysis to develop and evolve alongside rapidly changing fraudulent technology and techniques. Through the collaboration between machine learning, Conversational AI, NLP, and ASR, data that would have previously been considered difficult or impractical to apply to anti-fraud measures can be repurposed. Fraud detection procedures such as checking for consistency in the details of claimant stories, identifying connections between claimants and witnesses that may be problematic, or detecting more complex behavioural indicators can be carried out more effectively, enabling a more comprehensive anti-fraud system.

 

What are the features that AI can recognise, and how does this help prevent fraud more efficiently?

Modern AI systems have the capabilities to detect a range of both speech and behavioural patterns, providing a more comprehensive analysis of the mannerisms and language features displayed in customer-facing interactions. There are several features that have been traditionally associated with fraudulent intent, with the most notable being frequent pauses in speech, hedging, delaying responses, indirectly answering questions, and displaying heightened emotional responses. AI not only has the ability to detect these traditional features of fraud, but it will also use its recorded history of confirmed fraudulent calls to continue tracking trends in behaviour and speech by fraudsters. Customers who have been identified to be displaying suspicious behaviour can be more closely monitored, and if the potential for fraud is confirmed, customer records can be updated with the necessary information and warnings concerning their claim. Currently, it is possible to also use AI systems to record a biometric voiceprint of known fraudsters, allowing their detection even when they call back with a new claim and different details. Through these measures, it can be possible to detect fraudulent intent from the first phone call.

However, it is important to be aware that these systems and tactics are not static, and constantly evolve depending on the new techniques being adopted by fraudsters to avoid detection. The most recent development in fraudulent operations is the use of “deepfake” technology, which can be used to mimic audio and mask a human voice in real-time. This allows fraudsters to create entirely new identities to recommit fraud with the same company, without being detected by biometric voiceprint technology. Traditional anti-fraud measures without the input of AI and machine learning will struggle to adapt to these new technological challenges. AI-based systems provide the flexibility and adaptability to allow businesses to keep up with these evolved techniques quickly, often with minimal human involvement.

 

How can speech recognition AI impact wider business goals?

The reach of AI is not limited to efficient fraud detection – important business goals such as the improvement of customer services also benefit significantly from the implementation of AI-based systems. Functions such as sentiment and emotion analysis now allow businesses to detect and interpret the nature of customer experiences, identifying positive and negative language and speech indicators. This enables businesses to gain a better understanding of their customer interactions and where improvements or reviews may be required. This form of analysis can also provide more detailed information about whether customers are displaying a sense of urgency, frustration, contentment, or confidence in response to their experience. Details provided by this analysis allows businesses to create more specific targets and methods to increase customer satisfaction.

Implementing wider behavioural analysis through AI systems also provides new opportunities for businesses to provide improved safeguarding for vulnerable customers. Employees can be notified when customers are displaying worrying indicators of being uncertain, confused, or concerned as a result of their interaction, and respond accordingly. These more vulnerable customers are often unemployed, young, or older adults that may require a more in-depth explanation of how the business can serve their personal needs. Follow up contact, reassurance, or in more extreme cases, welfare checks can be provided to these customers. The introduction of more thorough AI-based analysis can feel more intrusive to some customers – however, this technology also enables the provision of better customer care. The shift towards more analytical, adaptive technology increases our capabilities to care for the most vulnerable in society.

 

Nigel Cannings is the founder of Intelligent Voice, a company leading the international development of proactive compliance and technology solutions for various forms of media. His experience in both technology and law provides a unique insight into the future of these technologies and the legalities surrounding them.

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IS THERE A CASE FOR APPOINTING A DIGITAL TRANSFORMATION OFFICER?

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By

Stefano Maifreni, founder of Eggcelerate

Stefano Maifreni, founder of Eggcelerate

 

You can tell the business world’s direction by the new roles that start to pop up in the C-Suite. With titles like chief transformation officer, chief change officer, and digital transformation officer, it’s clear what’s on a business leader’s mind these days. Each of these roles is a response to rapidly changing market and customer trends and an avalanche of technological advances that are causing businesses to rethink the way they operate.

In this article, however, we’re going to take a closer look at the Digital Transformation Officer (DTO). This professional is specifically focused on an organisation’s digital strategy and transformation.

Not every company needs to hire someone for this position, however. Sometimes it’s better to get every senior officer involved and lead the company’s digital transformation as a team.

To help you decide whether to bring in a DTO, we’ll offer some points to consider below.

 

What is a DTO, and What Do They Do?

Today, an organisation’s success and long-term sustainability heavily depend on its digital infrastructure combined with a data-driven culture. Smart, automated IT systems enable critical modern business capabilities such as personalisation, adaptability, and innovation. A data-driven culture then leverages the business and market data captured and analysed by these systems to guide business decision making at all levels of the organisation.

Even technology-based businesses, such as fintech companies and SaaS providers, don’t always have the most effective and efficient digital infrastructure in place. Moreover, establishing a well-oiled data-driven culture takes much time, research, and consideration.

It is where a DTO comes in. A DTO is a member of a company’s senior management team. They are responsible for leading a business’s digital transformation in response to the emergence of new critical technologies, significant shifts in the market, or the development of new business products or services, among other events. These professionals work closely with the CEO and the C-suite, but at the same time, they must also collaborate with employees at virtually every level of the company.

Having a strong, focused senior leader at the helm of such an undertaking is thus critical to its success.

 

Three Necessary Qualities to Look for in a DTO

If you decide to hire a DTO, the person you choose for the role must possess several essential qualities to steer your company through a successful digital transformation. These qualities are in addition to technical expertise and general industry knowledge:

 

They can see the big picture.

A DTO must take in the whole picture of your company and determine the critical areas where people, data, and infrastructure interact. They can then leverage that knowledge to implement digital initiatives for every significant business process’s strategic innovation and business transformation.

They must be able to consider the customer’s experience and needs as the agent and driver for change, on the one hand, while understanding the unique needs and culture of your business on the other. It includes understanding how people and departments interact and how third-party vendors and service providers fit into the system. The goal is to balance the company-wide need for change and adaptation with consideration for employee needs for consistency, stability, and clarity.

 

They possess good communication and collaboration skills.

While the role requires a wide range of soft skills, abilities in communication and collaboration are at the top of the list. A DTO must speak to a broad spectrum of people at every level of the company. It is crucial not only for gathering information and feedback but for encouraging employee buy-in in response to change. It (almost) goes without saying that a big part of communication is not just speaking to others but listening to what is being said or reported honestly.

 

They are humble and able to learn from others.

This role involves gathering a lot of input from employees at the lowest ranks of a company, particularly those directly involved with customers, such as sales and customer service representatives. At the same time, DTOs must report to the CEO and collaborate with other C-Suite executives. To do this properly, a good DTO must be open and willing to learn from others, consider different ideas and opinions, and be ready to course-correct when an initiative goes off track.

 

Not Every Company Needs a DTO

While many companies may want to pass the responsibilities of a DTO on to a specific individual, so they are free to focus on other areas of the business, it may not be the best decision. Here are a few reasons why:

  • Unlike traditional C-Suite roles, the DTO is transient by design. It means that companies usually employ a DTO to get a digital transformation off the ground. Once the change is fully integrated into the company’s operations, the DTO is no longer needed. With the proper infrastructure and data-driven culture in place, digital transformation and innovation self-perpetuates.
  • Another issue is that companies typically hire an outsider to fill the role of DTO. While this may give the company access to a fresh perspective, an outsider working in a transitory position may not be willing or able to reach the level of understanding– whether of the company or the market it serves– needed to make the most effective decisions.
  • Because digital infrastructure and adaptability are key business competencies, direct involvement in the digital transformation process helps senior leaders improve their strategic decisions overall.

While companies that rely on analogue processes may benefit from a dedicated DTO, in many cases, appointing a DTO is not in the best interest of an already digitally aligned company. It may be much more effective to create a company-wide commitment with a team of senior leaders responsible for the digital transformation, rather than passing the details and management of the transition off to one individual.

 

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