Cloud migration best practices: Optimizing your cloud migration strategy

As businesses adapt to the evolving digital landscape, cloud migration became an important step toward achieving greater efficiency, scalability and security. Cloud migration is the process of transferring data, applications and on-premises infrastructure to a cloud computing environment. This shift involves a fundamental change in the way a company operates.

Why migrate to the cloud?

There are many reasons for migrating from on-premises infrastructure to cloud. Businesses are increasingly embracing cloud infrastructure due to its scalability, flexibility and cost-effectiveness, among other benefits. Recent statistics indicate a significant rise in companies adopting cloud services to meet their operational and cost saving needs.

Cloud-based collaboration tools enable team members to work together from diverse locations, enhancing productivity and enabling remote work. Cloud migration can also help reduce a business’s carbon footprint.

Additionally, cloud providers regularly update their services, supplying access to the latest features, security patches and technology advancements. 

Types of cloud migration

The specific strategies and scenarios for cloud adoption and migration depend on the needs of the organization and its current IT infrastructure. It is important to understand the cloud migration best practices for effective execution.

Complete data center migration involves the transmission of all company data to the cloud. This migration approach is favored when businesses seek to fully use the benefits of the cloud, including scalability, flexibility and advanced features. By transferring all data and operations to the cloud, organizations can reduce operational costs and retire traditional on-premises setups. 

Hybrid cloud migration is relocating a portion of resources to the cloud while keeping some data on-premises. This approach suits scenarios where businesses have specific data compliance needs, keeping sensitive or critical data on-premises while leveraging the cloud for other operations. The hybrid model allows organizations to gradually transition to the cloud, managing risks associated with a complete migration while benefiting from cloud scalability and flexibility. 

Cloud-to-cloud migration occurs when organizations move resources from one cloud to another. This migration type is driven by the pursuit of cost efficiency or for better security. 

Multicloud migration is a strategic approach that involves utilizing services or resources from multiple cloud service providers. Multicloud environments range from leveraging software-as-a-service (SaaS) solutions for portability to orchestrating enterprise applications across various platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS) offerings from leading cloud vendors like Amazon Web services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), through a centralized management console. 

Cloud migration strategies 

There are several types of cloud migration strategies that organizations employ, based on their specific needs. These include rehosting, re-platforming, refactoring, repurchasing and retiring.

Rehosting 

Rehosting, also known as Lift and Shift, involves moving applications from an on-premises environment to the cloud without making significant changes. 

Re-platforming 

With a re-platforming migration, some adjustments or optimizations are made to the applications before moving them to the cloud.

Refactoring 

Refactoring involves modifying or redesigning applications to fully leverage cloud-native features. This migration type often involves breaking down monolithic applications into microservices, making them more scalable in the cloud environment.

Repurchasing

Repurchasing involves retiring an existing application and replacing it with a SaaS alternative. Instead of migrating the application to the cloud, businesses opt for a cloud-based SaaS solution that meets their needs.

Retiring 

Retiring involves decommissioning outdated or unused applications as part of the migration process. This helps in reducing maintenance costs and eliminating redundant resources. 

Common cloud migration challenges

Performance bottlenecks

Performance bottlenecks particularly occur during the testing phase when migrated resources are validated, which can pose a significant challenge during cloud migrations. The transition to a cloud environment can introduce differences in performance compared to on-premises setups, necessitating careful identification of bottlenecks, latency, issues and other performance-related challenges. Successfully addressing these issues during testing is essential to make sure that applications and services operate smoothly post-migration, minimizing disruptions and maintaining optimal performance for end-users. 

Cost overruns

Estimating and controlling expenses throughout a cloud migration and beyond is hard work. Although cloud services can offer long-term cost savings, the initial migration phase often incurs significant expenses related to data transfer, re-architecture and training. Additionally, without proper monitoring and optimization, ongoing cloud usage costs can escalate rapidly, leading to budget overruns and financial strain. To address this challenge, organizations must implement robust cost management strategies, leveraging cost optimization tools and continuously monitor and adjust their cloud resources to promote cost-efficiency and alignment with business objectives. 

Time and resource commitment

Achieving a successful cloud migration entails significant time and resource commitment due to the intricate nature of cloud migration projects. These endeavors demand meticulous planning, extensive testing and skilled personnel, all of which can strain an organization’s resources and divert attention from other critical business initiatives. Without adequate planning and resource allocation, businesses may face disruptions to operations and potential project failure, emphasizing the importance of careful consideration and investment in cloud migration initiatives. 

Scalability and performance

Scalability and performance present notable challenges for cloud migrations, despite the inherent scalability benefits of cloud computing. It’s critical to make sure that the selected cloud solution can effectively manage the increasing volumes of data and processing demands, both at present and as the business expands. For instance, a rapidly growing online gaming company migrating to the cloud for scalability might encounter issues if auto-scaling features are not configured optimally. This could result in subpar performance or unexpectedly high costs during peak traffic periods, highlighting the importance of thorough planning and testing for seamless scalability and performance in the cloud environment. 

Vendor lock-in

Vendor lock-in causes organizations to risk becoming dependent on a single cloud provider’s proprietary services, APIs and pricing models. This dependency can limit flexibility, hinger innovation and cause costs to increase over time. Moreover, transitioning away from a cloud provider can be complex and costly because of data transfer fees, re-architecture efforts and potential app rewrites. To mitigate this risk, you must carefully consider your cloud strategy, adopt multi-cloud or hybrid cloud architectures and implement cloud-agnostic solutions wherever possible to maintain flexibility and avoid being locked into any one cloud vendor’s ecosystem. 

Service disruption

Due to the complexity of transitioning mission-critical applications and services during a migration from on-premises to the cloud, service disruptions pose a significant challenge. Migrating workloads from on-premises data centers to the cloud often involves reconfiguring network settings, transferring large volumes of data and adapting to new cloud-native architectures. During this transition, any interruptions or downtime can lead to lost revenue, decreased productivity and damage to your organization’s reputation. You must safeguard seamless continuous service to minimize disruptions and maintain business operations throughout the migration process. 

How to avoid cloud migration challenges 

There are many benefits to cloud migration but that doesn’t mean the cloud migration process is not without its own challenges. A well-developed strategic approach is crucial for a smooth migration and to navigate potential challenges, but a strategic approach alone is not enough to avoid the associated challenges. What’s needed is a comprehensive solution that not only manages, automates and continuously optimizes your cloud environment in real-time but also helps you in planning and executing a successful cloud migration, whether you are transitioning from on-premises to the cloud or migrating between cloud providers. IBM® Turbonomic® supports your cloud provider, whether it’s private or public cloud. 

With IBM Turbonomic you can avoid many challenges associated with cloud migrations, like 

• Service disruptions: Dynamically optimizes workloads performance and resource allocation for uninterrupted operations 
• Vendor lock-in: Provides insights and recommendations for workload placement across multiple cloud providers, reducing dependency on any single vendor’s ecosystem. 
• Cost overruns: Continuously optimizes resource utilization and provides insights to help control cloud spending (during a migration and after). 
• Time and resource commitments: Automates workload placement and optimization, reducing manual effort and streamlining the migration process. 
• Performance bottlenecks: Dynamically identifying and addressing workload performance issues for smooth operation post-migration. 

IBM Turbonomic What-If Planning: cloud migration edition

IBM Turbonomic makes sure your cloud migration runs smoothly no matter what challenges are thrown your way, but what makes the platform special is its ability to run What-If planning scenarios. IBM Turbonomic has a plan specifically tailored for cloud migrations called ‘Migrate to Cloud’, offering invaluable foresight and strategic insight for a seamless transition to the cloud.

The plan simulates the migration of on premises virtual machines (VMs) to the cloud, or the migration of VMs from one cloud provider to another. This plan focuses on performance and cost optimization by selecting the most suitable cloud resources for your VMs and their associated volumes. Additionally, the plan can recommend cost-savings measures such as transitioning workloads from on-demand to discounted pricing and purchasing more discounts. 

The ‘Migrate to Cloud’ plan calculates costs based on your negotiated billing and price adjustments with your cloud provider, covering compute, services (for example, IP services) and licensing fees. Additionally, the plan factors in discounted purchases for VMs eligible for discounted pricing.

The plan results show: 

• Projected costs 
• Actions to execute your migration and optimize costs and performance 
• Optimal cloud instances to use, combining efficient purchase of resources with assured application performance 
• The cost benefit of moving workloads from on-demand to discounted pricing 
• Discounts you should purchase 

Turbonomic shows results for two migration scenarios, Lift and Shift and an optimized cloud migration. For the Lift and Shift results, Turbonomic shows the migration of your VMs to cloud instances that match their current resource allocations. As for the optimized plan, Turbonomic actively seeks opportunities to optimize both cost and performance. Through the analysis of historical VM resource utilization, Turbonomic discovers instances of over-provisioning. In an optimized migration scenario, Turbonomic will suggest transitioning these VMs to more cost-effective instances without compromising performance, showcasing the resulting cost savings. Furthermore, when examining the actions for an optimized migration, Turbonomic provides charts that plots the historical utilization data used in the analysis. 

By simulating cloud migrations, Turbonomic enables organizations to anticipate potential challenges, such as resource constraints or performance bottlenecks and proactively address them before implementation. This capability empowers businesses to optimize their migration plan and strategy, mitigate risks, reduce cloud costs and maximize the success of their cloud migration initiatives. With Turbonomic’s What-If planning, organizations can confidently navigate the complexities of cloud migration and achieve their desired outcomes with minimal disruption.

Source: ibm.com

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How to become an AI+ enterprise

We have all been witnessing the transformative power of generative artificial intelligence (AI), with the promise to reshape all aspects of human society and commerce while companies simultaneously grapple with acute business imperatives. In 2024, companies confront significant disruption, requiring them to redefine labor productivity to prevent unrealized revenue, safeguard the software supply chain from attacks, and embed sustainability into operations to maintain competitiveness.    

AI is at a turning point, driving exponential advancements in an organization’s prosperity and growth. Generative AI (gen AI) introduces transformative innovation to all aspects of a business; from the front to the back office, through ongoing technology modernization, and into new product and service development.

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. This requires a holistic enterprise transformation. We refer to this transformation as becoming an AI+ enterprise.

Become an AI+ enterprise

An AI+ enterprise innovates with AI as the primary focus, understands that AI is fundamental to the entire business, and recognizes that AI impacts all aspects of the business: product innovation, business operations, technical operations, as well as people and culture. 

Figure 1: Transforming into an AI+ enterprise is at the core of what our team at IBM does

An AI+ enterprise integrates AI as a first-class function across the business. They understand that if one area of the business adopts AI while others lag or resist it (due to valid concerns), this exacerbates issues like Shadow AI, making it challenging to implement a holistic strategy.

Benefits of being an AI+ enterprise

The vast business opportunity with AI, forecasted by Gartner to bring USD 3 to 4 trillion in economic benefits to the global economy across industries, prompts companies to recognize the investment required to use AI effectively, and are demanding a dramatic return on investment (ROI) before investing in an AI use case.

By becoming an AI+ enterprise, clients can realize the ROI not only for the AI use case but also for improving the related business and technical capabilities required to deliver AI use cases into production at scale.

Figure 2: ROI potential by transforming into an AI+ enterprise

Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 times higher ROI.

IBM has developed AI+ Enterprise Transformation to equip clients with the business and technical strategy, architectures, roadmaps and hands-on experience to become an AI+ enterprise.

AI+ Enterprise Transformation

With IBM’s depth in AI and hybrid cloud, we have discovered that companies becoming an AI+ enterprise leads to faster realization of business results. What’s exciting is that many clients we work with are already excelling in AI, and by adopting AI+ Enterprise Transformation, they uncover activities that accelerate their business growth through running AI in production at scale.

Figure 3: AI+ enterprise domain overview

Figure 3 summarizes AI+ Enterprise Transformation, highlighting the multiple domains across the organization that an AI+ enterprise needs to address to bring AI to production at scale: 

• Key use cases that enhance business performance
• Responsible AI technology to implement these use cases
• A well-designed data foundation to fuel AI initiatives
• Application innovation to deliver AI experiences, and application modernization to handle AI requests
• Hybrid cloud platform, including integrations, to run AI, data and applications as required
• Building pipelines for continuous updates, enhancements and fixes of apps, data and AI, with deployment protection through scanning and guardrails
• Day-2 operations using AI to predict (and repair) failures before they happen, fostering a culture where employees embrace AI’s value instead of fearing replacement.
• Security, governance, risk and compliance mechanisms are essential not only for governing AI but also for managing the IT estate running AI, providing evidence for regulatory compliance.

Start with the use cases

The most important step for an AI+ enterprise is identifying transformative use cases. After experimenting with various options, the enterprise selects high-value use cases that show faster ROI. It then delivers them into production across the IT landscape, laying the groundwork for additional use cases and fostering ongoing innovation.

Figure 4 illustrates the AI+ use case funnel that an AI+ enterprise adopts to systematically and rigorously transform use cases into broad-reaching AI enterprise solutions that deliver high ROI, aligned across delivery, operations, security and governance.  

Figure 4: AI+ use case funnel to deliver AI solutions to production at scale

Harness the right AI technology

After identifying use cases, the next step for an AI+ enterprise is choosing the appropriate AI technology and architecture. Often, this decision is made too quickly. It should be approached thoughtfully to help ensure suitability.

Consider the following:

• Do you need a public foundation model?
• Should you build your own? If so, where will it run?
• Should you use a retrieval augmented generation (RAG) model by pairing your data with a public foundation model?
• Do you use gen AI out of the box? How can you master prompt engineering? When should you prompt-tune or fine-tune?
• Which approach requires on-premises GPUs?
• Where do you harness gen AI vs. predictive AI vs. AI orchestration? For instance, when automating password change requests, do you need a 175 billion parameter public foundation model, a fine-tuned smaller model, or AI orchestration to call APIs?

As you pinpoint your AI technology, your decision impacts the other domains of AI+ Enterprise Transformation. For more insights, keep reading.

Deliver a strong data foundation

AI relies fundamentally on data. An AI+ enterprise ensures that the data used for AI is trustworthy, transparent, and has clear lineage and efficacy. Otherwise, the risks become too significant. We have all seen examples of companies delivering AI built on weak data foundations, leading to undesirable outcomes. These outcomes typically fall into one of three categories, none of which are desirable:

• Not useful: Customers remain unimpressed with your results. For example, stale data, hallucinations and more.
• Embarrassing: Offensive output emerges based on the data used in AI. For example, hate, abuse, profanity and bias.
• Financial/criminal: Violations of existing and emerging data and AI regulations. For example, copyright laws, the European Union’s Artificial Intelligence act, Digital Operational Resilience Act (DORA), data sovereignty laws and more.

An AI+ enterprise empowers architects to confidently source, prepare, transform, protect and deliver data to the required locations for AI. 

Innovate and modernize applications

Innovating with new AI-based applications to deliver outstanding experiences is essential. It’s also crucial to modernize existing applications that interact with AI. if an AI-powered human resources assistant offers to perform actions for employees, it is vital to ensure that the application being called can handle increased traffic. Frequently, these actions involve calling APIs to legacy applications running on architectures unfit for handling the sudden demands of the AI assistant. This often leads to a disappointing experience due to slow response times.

An AI+ enterprise excels in delivering innovative AI applications to its customers and modernizing existing applications to meet the new demands AI presents.

Hybrid cloud platform

Once AI, data and applications are understood, the discussion naturally shifts to “Where do we run this solution?” In our experience, the answer depends on many factors, which can change over time, requiring a flexible platform.

Adopting an open technologies-based hybrid cloud platform enables an AI+ enterprise to make informed decisions without limiting its business.

Figure 5: AI+ enterprise hybrid cloud architecture

As shown in figure 5, a hybrid cloud architecture enhances the entire business in various ways:

• Flexibility in where to train and tune large models
• Flexibility in where to train and tune smaller models
• Where to perform inferencing on-premises, in private clouds or even on edge devices
• Applications using RAG architectures experience less latency when running close to the models
• Data sovereignty laws limit data relocation, so having the ability to move AI and applications to the data is essential
• Creating an AI+ fabric that provides interconnectivity across the IT and business landscape

Continuously build and enhance apps, data and AI

When AI, data and applications run across a well-designed hybrid cloud platform, an AI+ enterprise builds pipelines and toolchains to continuously enhance and deliver with full automation. For example:

• Platform pipelines provision and update infrastructure and the software running on them using Terraform and Ansible
• Application pipelines integrate and deliver code updates for both innovative applications delivering AI experiences and modernized applications being acted upon by AI digital workers
• Data pipelines process incoming data to help ensure that the data sources used by AI are current and valid

AI pipelines pull in data, retrain and augment as needed based on metrics such as drift and accuracy

An AI+ enterprise knows how to continually enhance applications, data and AI models throughout their lifecycle, helping to ensure that only trusted and approved AI functions go live.

Operations

Incidents occur, even in an AI-first world. However, an AI+ enterprise uses AI not only to delight customers but also to solve IT problems. With the right tools, an AI+ enterprise can significantly increase employee productivity. Examples include:

• Detecting and correcting when an application is being constrained and automating capacity increases
• Providing visibility and insight across the enterprise to enable higher levels of automation and achieve predictive maintenance
• Reducing security threats by closing security gaps before they arise. For example, by using compliance control scanning of terraform templates to fail provisioning if controls are not met.

An AI+ enterprise also recognizes that alongside the necessary tools, fostering a culture that embraces AI and trains talent is crucial. This culture encourages experimentation and expertise growth. It requires people trained to harness, evaluate, and accelerate AI, rather than fearing it.

Secure and govern AI on the hybrid cloud platform

To deploy AI, particularly gen AI, at scale in production, organizations must establish a secure and governed environment. The scale and impact of next-generation AI emphasize the importance of governance and risk controls. An AI+ enterprise mitigates potential harm by implementing robust measures to secure, monitor and explain AI models, as well as monitoring governance, risk and compliance controls across the hybrid cloud environment.

Pairing existing cloud governance with new AI governance controls is essential, requiring continual focus to comply with emerging regulatory changes, such as NIST AI Risk Management Framework, the European Union’s Artificial Intelligence act, ISO/IEC 42001 AI Management, and ISO/IEC 23894 AI Risk Management.

Get started today

IBM wants to work with you to become an AI+ enterprise, providing impactful use cases, strategies, architectures and hands-on experiences to:

• Understand your current trajectory
• Shape your AI+ enterprise strategy with points of view and models
• Co-create target state solutions and architectures
• Specify governance and risk posture
• Customize a business value case
• Co-develop key near-term engineering sprints to prove the value of your AI+ enterprise strategy

Future articles will delve deeper into each AI+ domain, showcasing IBM’s perspective through architectures, demos and strategies.

Source: ibm.com

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Empowering the digital-first business professional in the foundation model era

In the fast-paced digital age, business professionals constantly seek innovative ways to streamline processes, enhance productivity and drive growth. Today’s professionals, regardless of their fields, must fluently use advanced artificial intelligence (AI) tools. This is especially important given the application of foundation models and large language models (LLMs) in Open AI’s ChatGPT and IBM’s advances with IBM watsonx.

 

Professionals must keep up with rapid technological changes such as cloud computing and AI, recognizing the integrative power of foundation models, which are increasingly central to AI-based automation. The transition to the foundation model era signifies a substantial change in how professionals use technology to enhance their digital strategies. By using cutting-edge technology, professionals can optimize decision-making processes and enhance operational efficiency. 

For instance, business analysts now play a crucial role in bridging the gap between business and IT but also in integrating these foundational AI models into business strategies, further augmenting and optimizing operations. They translate business needs into solution requirements and propose ways to optimize business operations. 

The next leap: Beyond low-code platforms 

While the rise of low-code platforms has marked a significant evolution in bridging business requirements with IT implementation, the current market trend is veering toward more intuitive, AI-driven solutions. Foundation models urge businesses to look beyond conventional limitations, with their inherent ability to understand, generate and process human-like text, allowing non-technical professionals to interact and build applications by using natural language, marking a shift from conventional programming. By transcending the constraints of low-code platforms, businesses can build more robust, tailored solutions that align closely with their evolving digital strategies. 

Deploying LLMs effectively, like any tool, requires professionals to understand their capabilities and potential biases. Blending creativity and domain-specific expertise with AI’s computational prowess helps ensure technologically sound and contextually relevant solutions. 

From digital assistants to AI assistants

The narrative surrounding digital assistants is evolving. While computer-aided instructions or computer-assisted instructions represented a previous breakthrough, AI platforms like watsonx are elevating the concept. Instead of mere assistants, these AI-based automation platforms act as collaborators, offering insights, handling routine tasks with precision and accuracy, and enhancing decision-making processes for knowledge workers. 

The distinction between traditional robotic process automation (RPA) robots and AI-driven digital collaborators is paramount. The latter not only automates but also comprehends, reasons and learns, providing richer, more dynamic interactions. More importantly, they enable systems and tools to conform to the needs of the users, responding intelligently to users’ natural language requests.  

IBM’s vision: watsonx, watsonx Orchestrate, foundation models and beyond 

IBM strategically innovates by venturing into the world of foundation models with watsonx, demonstrating their dedication to revolutionizing businesses through AI. Their powerful IBM watsonx™ Orchestrate platform equips digital assistants with essential tools to deliver unparalleled value. Simultaneously, IBM complements this ecosystem with its RPA and Process Mining tools, offering a low-code interface for business analysts to unearth and enhance business processes. 

In essence, IBM’s comprehensive suite, centered on watsonx, aims to usher in a new era by empowering businesses to use foundation models. This supercharges their operations, helping to ensure a harmonized dance between human expertise and AI-driven automation. 

Source: ibm.com

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Building trust in the government with responsible generative AI implementation

At the end of 2023, a survey conducted by the IBM Institute for Business Value (IBV) found that respondents believe government leaders often overestimate the public’s trust in them. They also found that, while the public is still wary about new technologies like artificial intelligence (AI), most people are in favor of government adoption of generative AI.

The IBV surveyed a diverse group of more than 13,000 adults across nine countries including the US, Canada, the UK, Australia and Japan. All respondents had at least a basic understanding of AI and generative AI.

The survey was designed to gain an understanding of individual perspectives on generative AI and its use by companies and governments, along with their expectations and intent in using this technology at work and in their personal lives. Respondents answered questions about their levels of trust in governments and their views on governments adopting and leveraging generative AI to deliver government services.

These findings reveal the complex nature of public trust in institutions and provide key insights for government decision-makers as they adopt generative AI on a global scale.

An overestimation of public trust: Discrepancies in perception

Trust is one of the main pillars of public institutions. According to Cristina Caballe Fuguet, Global Government Leader at IBM Consulting, “Trust is at the core of the government’s ability to perform their duties effectively. Citizens’ trust in governments, from local representatives to the highest posts in the national government, depends on multiple elements, including the delivery of public services.”

Trust is essential as governments take the lead on critical issues like climate change, public health and the safe and ethical integration of emerging technologies into societies. The current digital age demands more integrity, openness, trust and security as key pillars to building trust.

According to another recent study by the IBV, the IBM Institute for the Business of Government and the National Academy of Public Administration (NAPA), most government leaders understand building trust requires focus and commitment to collaboration, transparency and competence in execution. However, the most recent IBV research indicates trust in governments among constituents is in decline.

Respondents indicate their trust in federal and central governments has declined most since the start of the pandemic, with 39% of respondents indicating that their level of trust in their country’s government organizations is very low or extremely low, compared to 29% prior to the pandemic.

This contrasts with the perceptions of surveyed government leaders in the same study, as they indicate they are confident they have established and effectively grown trust in their organizations among constituents since the COVID-19 pandemic. This discrepancy in the perception of trust indicates that government leaders must find a way to better understand their constituents and reconcile their views on how the public sector institutions are performing with how they are perceived by constituents.

The study also found that building trust in AI-powered tools and citizen services will be a challenge for governments. Nearly half of respondents indicate that they trust more traditional human-assisted services, and only about 1 in 5 indicate they trust AI-powered services more.

Open and transparent AI implementation is the key to trust

This year, more than 60 countries and the EU (representing almost half of the population of the world) will head to the polls to elect their representatives. Governments leaders face myriad challenges, including ensuring that technologies work for—and not against—democratic principles, institutions and societies. 

According to David Zaharchuck, Research Director, Thought Leadership for the IBV, “Ensuring the safe and ethical integration of AI into our societies and the global economy will be one of the greatest challenges and opportunities for governments over the next quarter century.”

Most surveyed individuals indicate they have concerns about the potential negative impacts of generative AI. This shows that most of the public is still wrapping their mind around this technology and how it can be designed and deployed by organizations in a trusted a responsible way, adhering to strict security and regulatory requirements.

The IBV study revealed that people still have a level of concern when it comes to the adoption of this emerging technology and the impact that it can have on issues like decision-making, privacy and data security or job security.

Despite their general lack of trust in the government and in emerging technologies, most surveyed individuals agree with government use of generative AI for customer service and believe the rate of adoption for generative AI by governments is appropriate. Less than 30% of those surveyed believe the pace of adoption in the public and private sectors is too fast. Most believe it is just right, and some even think it is too slow.

When it comes to specific use cases of generative AI, survey respondents have mixed views about using generative AI for various citizen services; however, a majority agree with governments using generative AI for customer service, tax and legal advisory services, and for educational purposes.

These finding show that citizens see the value in governments leveraging AI and generative AI. However, trust is an issue. If citizens don’t trust governments now, they certainly won’t if governments make mistakes as they adopt AI. Implementing generative AI in an open and transparent ways enables governments to build trust and capability at the same time.

According to Casey Wreth, Global Government Industry Leader at IBM Technology, “The future of generative AI in the public sector is promising, but the technology brings new complexities and risks that must be proactively addressed. Government leaders need to implement AI governance to manage risks, support their compliance programs and most importantly gain public trust on its wider use.”

Integrated AI governance helps ensure trustworthy AI

“As the adoption of generative AI continues to increase this year, it’s vital that citizens have access to transparent and explainable AI workflows that bring light to the black box of what’s generated using AI with tools like watsonx.governance. In this way, governments can be stewards of the responsible implementation of this groundbreaking technology,” says Wreth.

IBM watsonx™, an integrated AI, data and governance platform, embodies five fundamental pillars to help ensure trustworthy AI: fairness, privacy, explainability, transparency and robustness.

This platform offers a seamless, efficient and responsible approach to AI development across various environments. More specifically, the recent launch of IBM watsonx.governance helps public sector teams automate and address these areas, enabling them to direct, manage and monitor their organization’s AI activities.

In essence, this tool opens the black box about where and how any AI model gets the information for its outputs, similar to the function of a nutrition label, facilitating government transparency. This tool also facilitates clear processes so organizations can proactively detect and mitigate risks while supporting their compliance programs for internal AI policies and industry standards.

As the public sector continues to embrace AI and automation to solve problems and improve efficiency, it is crucial to maintain trust and transparency in any AI solution. Governments must comprehend and manage the full AI lifecycle effectively, and leaders should be able to easily explain what data was used to train and fine-tune models, as well as how the models reached their outcomes. Proactively adopting responsible AI practices is an opportunity for all of us to improve, and it is an opportunity for governments to lead with transparency as they harness AI for good.

Source: ibm.com

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The difference between ALIAS and CNAME and when to use them

The chief difference between a CNAME record and an ALIAS record is not in the result—both point to another DNS record—but in how they resolve the target DNS record when queried. As a result of this difference, one is safe to use at the zone apex (for example, naked domain such as example.com), while the other is not.

Let’s start with the CNAME record type. It simply points a DNS name, like www.example.com, at another DNS name, like lb.example.net.  This tells the resolver to look up the answer at the reference name for all DNS types (for example, A, AAAA, MX, NS, SOA, and others). This introduces a performance penalty, since at least one additional DNS lookup must be performed to resolve the target (lb.example.net). In the case of neither record ever having been queried before by your recursive resolver, it’s even more expensive timewise, as the full DNS hierarchy may be traversed for both records:

1. You as the DNS client (or stub resolver) query your recursive resolver for www.example.com.
2. Your recursive resolver queries the root name server for www.example.com.
3. The root name server refers your recursive resolver to the .com Top-Level Domain (TLD) authoritative server.
4. Your recursive resolver queries the .com TLD authoritative server for www.example.com.
5. The .com TLD authoritative server refers your recursive server to the authoritative servers for example.com.
6. Your recursive resolver queries the authoritative servers for www.example.com and receives lb.example.net as the answer.
7. Your recursive resolver caches the answer and returns it to you.
8. You now issue a second query to your recursive resolver for lb.example.net.
9. Your recursive resolver queries the root name server for lb.example.net.
10. The root name server refers your recursive resolver to the .net Top-Level Domain (TLD) authoritative server.
11. Your recursive resolver queries the .net TLD authoritative server for lb.example.net.
12. The .net TLD authoritative server refers your recursive server to the authoritative servers for example.net.
13. Your recursive resolver queries the authoritative servers for lb.example.net and receives an IP address as the answer.
14. Your recursive resolver caches the answer and returns it to you.

Each of these steps consumes at least several milliseconds, often more, depending on network conditions. This can add up to a considerable amount of time that you spend waiting for the final, actionable answer of an IP address.

In the case of an ALIAS record, all the same actions are taken as with the CNAME, except the authoritative server for example.com performs steps six through thirteen for you and returns the final answer as both an IPv4 and IPv6 address. This offers two advantages and one significant drawback:

Advantages

Faster final answer resolution speed

In most cases, the authoritative servers for example.com will have the answer cached and thus can return the answer very quickly.

The alias response will be A and AAAA records. Since an ALIAS record returns the answer that comprises one or more IP addresses, it can be used anywhere an A or AAAA record can be used—including the zone apex. This makes it more flexible than a CNAME, which cannot be used at the zone apex.  The flexibility of the Alias record is needed when your site is posted on some of the most popular CDNs that require the use of CNAME records if you want your users to be able to access it via the naked domain such as example.com.

Disadvantages

Geotargeting information is lost

Since it is the authoritative server for example.com that is issuing the queries for lb.example.net, then any intelligent routing functionality on the lb.example.net record will act upon the location of the authoritative server, not on your location. The EDNS0 edns-client-subnet option does not apply here. This means that you may be potentially mis-routed: for example, if you are in New York and the authoritative server for example.com is in California, then lb.example.com will believe you to be in California and will return an answer that is distinctly sub-optimal for you in New York.  However, if you are using a DNS provider with worldwide pops, then it is likely that the authoritative DNS server will be located in your region, thus mitigating this issue.

One important thing to note is that NS1 collapses CNAME records, provided that they all fall within the NS1 system. NS1’s nameservers are authoritative for both the CNAME and the target record. Collapsing simply means that the NS1 nameserver will return the full chain of records, from CNAME to final answer, in a single response. This eliminates all the additional lookup steps and allows you to use CNAME records, even in a nested configuration, without any performance penalty.

And even better, NS1 supports a unique record type called a Linked Record. This is basically a symbolic link within our platform that acts as an ALIAS record might, except with sub-microsecond resolution speed. To use a Linked Record, simply create the target record as you usually would (it can be of any type) and then create a second record to point to it and select the Linked Record option. Note that Linked Records can cross domain (zone) boundaries and even account boundaries within NS1 and offer a powerful way to organize and optimize your DNS record structure.

CNAME, ALIAS and Linked Record Reference Chart

	CNAME	ALIAS	Linked Record
Use at Apex?	No	Yes	Yes (only to other NS1 zones)
Relative Speed (TTFB)	Fast	Faster	Faster
Collapses Responses	Yes (NS1 Connect exclusive feature)	Yes	Yes

Source: ibm.com

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6 benefits of data lineage for financial services

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. How can banks, credit unions, and financial advisors keep up with demanding regulations while battling restricted budgets and higher employee turnover?

The answer is data lineage. We’ve compiled six key reasons why financial organizations are turning to lineage platforms like Manta to get control of their data.

1. Automated impact analysis

In business, every decision contributes to the bottom line. That’s why impact analysis is crucial—it predicts the consequences of a decision. How will one decision affect customers? Stakeholders? Sales?

Data lineage helps during these investigations. Because lineage creates an environment where reports and data can be trusted, teams can make more informed decisions. Data lineage provides that reliability—and more.

One often-overlooked area of impact analysis is IT resilience. This blind spot became apparent in March of 2021 when CNA Financial was hit by a ransomware attack that caused widespread network disruption. The company’s email was hacked, consumers panicked, and CNA Financial was forced to pay a record-breaking $40 million in ransom. This is where lineage-supported impact analysis is needed. If you experience a threat, you will want to be prepared to combat it, and know exactly how much of your business will be affected.

IT resilience is also threatened by natural disasters, user error, infrastructure failure, cloud transitions, and more. In fact, 76% of organizations experienced an incident during the past two years that required an IT disaster-recovery plan.

Most organizations struggle with impact analysis as it requires significant resources when done manually. But with automated lineage from Manta, financial organizations have seen as much as a 40% increase in engineering teams’ productivity after adopting lineage.

2. Increased data pipeline observability

As discussed above, there are countless threats to your organization’s bottom line. Whether it is a successful ransomware attack or a poorly planned cloud migration, catching the problem before it can wreak havoc is always less expensive.

That’s why data pipeline observability is so important. It not only protects your organization but also your customers who trust you with their money.

Data lineage expands the scope of your data observability to include data processing infrastructure or data pipelines, in addition to the data itself. With this expanded observability, incidents can be prevented in the design phase or identified in the implementation and testing phase to reduce maintenance costs and achieve higher productivity.

Manta customers who have created complete lineage have been able to trace data-related issues back to the source 90% faster compared to their previous manual approach. This means the teams responsible for particular systems can fix any issue in a matter of minutes, according to Manta research.

3. Regulatory compliance

The financial space is highly regulated. Institutions must comply with regulations like Basel III, SOC 2, FACT, BSA/AML and CECL.

All of these regulations require accurate tracking of data. Your organization must be able to answer:

• Where does it come from?
• How did it get there?
• Are we capable of proving it with up-to-date evidence whenever necessary?
• Do we need weeks or months to complete a report?
• Is that report even entirely reliable?

Data lineage helps you answer these questions by creating highly detailed visualizations of your data flows. You can use these reports to accurately track and report your data to ensure regulatory compliance.

4. Efficient cloud migrations

McKinsey predicts that $8 out of every $10 for IT hosting will go toward the cloud by 2024. In the financial space, 40% of banks and 41% of credit unions have already deployed cloud technologies.

However, if you have ever been involved in the migration of a data system, you know how complex the process is. Approximately $100 billion of cloud funding is expected to be wasted over the next three years—and most enterprises cite the costs around migration as a major inhibitor to adopting the cloud. The process is so complex (and expensive) because every system consists of thousands or millions of interconnected parts, and it is impossible to migrate everything in a single step.

Dividing the system into smaller chunks of objects (reports, tables, workflows, etc.) can make it more manageable, but poses another challenge—how to migrate one part without breaking another. How do you know what pieces can be grouped to minimize the number of external dependencies?

With data lineage, every object in the migrated system is mapped and dependencies are documented. Manta customers have used data lineage to complete their migration projects 40% faster with 30% fewer resources.

5. Improved workflow & IT retention

Data engineers, developers, and data scientists continue to be fast-growing and hard-to-fill roles in tech. The shortage of data engineering talent has ballooned from a problem to a crisis, made worse by the increasing complexity of data systems. The last thing you want is to continually overstretch your valuable data engineers with routine, manual (and frustrating) tasks like chasing data incidents, assessing the impacts of planned changes, or answering the same questions about the origins of data records again and again.

Data lineage can help to automate routine tasks and enable self-service wherever possible, allowing data scientists and other stakeholders to retrieve up-to-date lineage and data origin information on their own, whenever they need it. A detailed data lineage map also enables faster onboarding of data engineers to integrate new or less-experienced engineers into the role without impacting the stability and reliability of the data environment.

6. Trust and data governance

Data governance isn’t new, especially in the financial world. The Basel Committee released BCBS 239 as far back as 2013. The regulation was meant to strengthen banks’ risk-related data-aggregation and reporting capabilities—enhancing trust in data.

Report developers, data scientists, and data citizens need data they can trust for accurate, timely, and confident decision-making. But in today’s complex data environment, you’re dealing with dispersed servers and infrastructure, resulting in disparate sources of data and countless data dependencies. You need a complete overview of all your data sources to see how it moves through your organization, understand all touch-points, and how they interact with one another. You can only completely trust your data when you have a complete understanding of it.

Data lineage provides a comprehensive overview of all your data flows, sources, transformations, and dependencies. You’ll ensure accurate reporting, see how crucial calculations were derived, and gain confidence in your data management framework and strategy.

Why Manta is the right fit for data lineage in financial services

Manta has helped dozens of customers in the financial space realize the benefits of data lineage. We bring intelligence to metadata management by providing an automated solution that helps you drive productivity, gain trust in your data, and accelerate digital transformation.

The Manta platform includes unique features to make the most value out of your lineage, with more than 40 out-of-the-box, fully automated scanners. In addition, Manta works alongside the most popular data catalogs; our platform integrates with catalogs like Collibra, Informatica, Alation and more.

Don’t wait. Realize the benefits of automated data lineage today.

Source: ibm.com

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6 ways to elevate the Salesforce experience for your users

Customers and partners that interact with your business, as well as the employees who engage them, all expect a modern, digital experience. According to the Salesforce Report, nearly 90% Of buyers say the experience a company provides matters as much as products or services. Whether using Experience Cloud, Sales Cloud, or Service Cloud, your Salesforce user experience should be seamless, personalized and hyper-relevant, reflecting all the right context behind every interaction.

At the same time, Salesforce is a big investment, and you need to show return on that investment as quickly as possible. Ensuring maximum user adoption and proficiency is key. The more useful and relevant the experience is, the more effective users will be on the platform—and the more frequently they will return to it.

Here are six ways you can elevate your Salesforce experience for customers, partners and employees.

1. Continuously inform and engage your users.

Keep users abreast of everything they need to know about your business, and share valuable, engaging content related to their needs and interests. Deliver timely information and critical alerts through tailored announcements. Keep your audience informed and engaged with virtual and in-person events and targeted news, blogs or other articles. Manage and surface all of this within Salesforce to minimize context switching and to keep users coming back to the platform.

2. Personalize the user experience for hyper-relevance.

Infuse context and personalized content to enrich the entire experience and make it more relevant to individual customers. Don’t make employees struggle with out-of-the-box search and list views; dynamically present what they need in the flow of work, so they don’t have to leave the current task to find it. Whether it is location mapping, embedded video, targeted news and events, assigned learning, or recommended products and knowledge articles, strive to give users the information they need when they need it.

3. Escape the confines of the typical Salesforce look and feel.

Break away from limiting, out-of-the-box layouts, view, and UI components to give users the beautiful, modern experience they expect. Follow current UX design principles and ensure that every touchpoint represents your unique branding look and feel, rather than just looking like any other Salesforce implementation.

4. Accelerate platform adoption and mastery.

Develop a plan to thoroughly onboard users and get them proficient with the platform as quickly as possible to start realizing value. Streamline and automate the onboarding process. Gathe data to drive users to the site or platform, personalize the experience, and equip them with the knowledge and resources they need for success. Then, go deeper and give your employees, partners and customers an immersive digital learning experience tailored to their specific needs. A highly skilled ecosystem is a loyal and effective one, and educated customers are advocates for the brand.

5. Enable users to serve themselves and each other.

Give your customers, partners and employees the ability to serve themselves 24/7, whether researching products, making purchases, managing accounts or troubleshooting and solving issues. This means making your product information, knowledge articles and other content easily accessible, searchable and filterable. Deflect cases by giving customers access to the same content your service employees use via the knowledge base or a chatbot.

6. Empower your users to be your advocates.

An effective way to get your brand and messaging in front of as many potential customers as possible is to give your users ways to advocate for you. Organically expand the reach and influence of your brand by enabling users to share, contribute to and interact with your content. Enable partners and employees to contribute blogs and articles, empower customers to share your content in their social networks, and enable users to rate and review products, services and other records. Use this active user base to crowdsource the best ideas for improving your business and your Salesforce implementation.

Achieve an elevated experience with IBM Accelerators for Salesforce

You can achieve this elevated experience with IBM Accelerators for Salesforce. Its library of pre-built components can be used to quickly implement dozens of common use cases in Salesforce with clicks, not code. You can drag, drop, configure and customize components to create engaging, hyper-relevant experiences for your employees, partners and customers on Sales Cloud, Service Cloud, and Experience Cloud. Accelerators like Announcements, Experience Components, News, Ideas, Learning Adventure, Onboarding, and many more enable you to create a highly relevant and personalized experience.

IBM developed these accelerators with the expertise we gained through thousands of successful IBM Salesforce Services engagements. Now, these same products are available to purchase and use in your projects! Unleash the power of our pre-built components to reduce customization efforts, empower administrators and speed the ROI of your Salesforce implementation.

Source: ibm.com

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Reducing defects and downtime with AI-enabled automated inspections

A large, multinational automobile manufacturer responsible for producing millions of vehicles annually, engaged with IBM to streamline their manufacturing processes with seamless, automated inspections driven by real-time data and artificial intelligence (AI).

As an automobile manufacturer, our client has an inherent duty to provide high-quality products. Ideally, they need to discover and fix any defects well before the automobile reaches the consumer. These defects are often expensive, difficult to identify and present a myriad of significant risks to customer satisfaction.

Quality control and early defect detection are paramount to uphold standards, enhance operational efficiency, reduce costs and deliver vehicles that meet or exceed customer expectations while safeguarding the reputation of the manufacturer.

How IBM helped the client better detect and correct defects during manufacturing

IBM worked with the client’s technical experts to deploy IBM Inspection Suite solutions to help them reduce defects and downtime while enabling quick action and issue resolution. The solutions deployed include fixed-mounted inspections (IBM Maximo Visual Inspection Mobile) and handheld inspections (IBM Inspector Portable). Hands-free wearable inspections (IBM Inspector Wearable) were also made available for situations that required a head-mounted display.

While computer vision for quality has existed in more primitive states for the last 30 years, the lightweight and portable nature of IBM’s solution, which is based on a standard iPhone and uses readily available hardware, really got our client’s attention. The client loved the fact that the solution can be used anywhere, at any time, by any of their employees—even while objects are in motion.

Scaling to 30 million inspections for an immediate and significant reduction in defects

The IBM Inspection Suite improved the client’s quality inspection process without requiring coding. The client found the system to be simple to train and deploy, without the need for data scientists. The system learned quickly from images of acceptable and defective work products, which enabled the solution to be up and running within a matter of weeks. The implementation costs were also lower than those of viable alternatives.

The ability to deliver AI-enabled automation by using an intuitive process in their plants allowed this client to scale this user-friendly technology rapidly across numerous other facilities where it aided in over 30 million inspections. The customer almost immediately realized measurable success due to the significant reduction in defects.

Voted on by the leaders of the client’s technical community, the client awarded their annual IT Innovation award to IBM for the technology they believed delivered the greatest value-driving innovation to their company. In presenting the award, the client’s executives declared that a discussion with IBM about transformation led to a focus on improving manufacturing quality with AI automation.

The Inspection Suite supported the client’s quality initiatives with in-station process control and quality remediation at the point of assembly or installation. The solution also provided continuous process improvement that is helping the client lower repair and warranty costs, while improving their customer satisfaction.

Transparency and trust in AI

By bringing the power of IBM’s deep AI capabilities, deployable on cost-effective edge infrastructure, across the client’s plants, IBM Inspection Suite enabled the client to deliver higher quality vehicles to their customers. The client is now expanding to additional plants and use cases thanks to their collaboration and innovation with IBM.

All the team members at IBM were honored that this client recognized them for their business and technical achievements. We believe that this recognition reflects the IBM values of client dedication and innovation that matters. It is a direct acknowledgment of the value IBM Inspection Suite provides to clients.

IBM’s mission is to harness the power of data and AI to drive real-time, predictive business insights that help clients make intelligent decisions.

Source: ibm.com

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Unveiling the transformative AI technology behind watsonx Orders

You’re headed to your favorite drive-thru to grab fries and a cheeseburger. It’s a simple order and as you pull in you notice there isn’t much of a line. What could possibly go wrong? Plenty.

The restaurant is near a busy freeway with roaring traffic noise and airplanes fly low overhead as they approach the nearby airport. It’s windy. The stereo is blasting in the car behind you and the customer in the next lane is trying to order at the same time as you. The cacophony would challenge even the most experienced human order taker.

With IBM® watsonx Orders, we have created an AI-powered voice agent to take drive-thru orders without human intervention. The product uses bleeding edge technology to isolate and understand the human voice in noisy conditions while simultaneously supporting a natural, free-flowing conversation between the customer placing the order and the voice agent.

Watsonx Orders understands speech and delivers orders

IBM watsonx Orders begins the process when it detects a vehicle pulling up to the speaker post. It greets customers and asks what they’d like to order. It then listens to process incoming audio and isolates the human voice. From that, it detects the order and the items, then shows the customer what it heard on the digital menu board. If the customer says everything looks right, watsonx Orders sends the order to the point of sale and the kitchen. Finally, the kitchen prepares the food. The full ordering process is shown in the figure below:

There are three parts to understanding a customer order. The first part is isolating the human voice and ignoring conflicting environmental sounds. The second part is then understanding speech, including the complexity of accents, colloquialisms, emotions and misstatements. Finally, the third part is translating speech data into an action that reflects customer intent.

Isolating the human voice

When you call your bank or utilities company, a voice agent chatbot probably answers the call first to ask why you’re calling. That chatbot is expecting relatively quiet audio from a phone with little to no background noise.

In the drive-thru, there will always be background noise. No matter how good the audio hardware is, human voices can be drowned out by loud noises, such as a passing train horn.

As watsonx Orders captures audio in real time, it uses machine-learning techniques to perform digital noise and echo cancellation. It ignores noises from wind, rain, highway traffic and airports. Other noise challenges include unexpected background noise and cross-talk, where people are talking in the background during an order.  Watsonx Orders uses advanced techniques to minimize these disruptions.

Understanding speech

Most voice chatbots began as text chatbots. Traditional voice agents first turn spoken words into written text, then they analyze the written sentence to figure out what the speaker wants.

This is computationally slow and wasteful. Instead of first trying to transcribe sounds into words and sentences, watsonx Orders turns speech into phonemes (the smallest units of sound in speech that convey a distinct meaning). For example, when you say “shake,” watsonx Orders parses that word into “sh,” “ay,” and hard “k.” Converting speech into phonemes, instead of full English text, also increases accuracy over different accents and actively supports a real-time conversation flow by reducing intra-dialog latency.

Translating understanding into action

Next, watsonx Orders identifies intent, such as “I want” or “cancel that.”. It then identifies the items that pertain to the commands like “cheeseburger” or “apple pie.”

There are several machine learning techniques for intent recognition. The latest technique uses foundation and large language models, which theoretically can understand any question and respond with an appropriate answer. This is too slow and computationally expensive for hardware-restrained use cases. While it might be impressive for a drive-thru voice agent to answer, “Why is the sky blue?”, it would slow the drive thru, frustrating the people in line and decreasing revenue.

Watsonx Orders uses a highly specific model that is optimized to understand the hundreds of millions of ways that you can order a cheeseburger, such as “No onions, light on the special sauce, or extra tomatoes.” The model also allows customers to modify the menu mid-order: “Actually, no tomatoes on that burger.”

In production, watsonx Orders can complete more than 90% of orders by itself without any human intervention. It’s worth noting that other vendors in this space use contact centers with human operators to take over when the AI agent gets stuck and they count the interaction as “automated.” By our IBM watsonx Orders standards, “automated” means handling an order end-to-end without any humans involved.

Real-world implementation drives profits

During peak times, watsonx Orders can handle more than 150 cars per hour in a dual-lane restaurant, which is better than most human order takers. More cars per hour means more revenue and profit, so our engineering and modeling approaches are constantly optimizing for this metric.

Watsonx Orders has taken 60 million real-world orders in dozens of restaurants, even with challenging noise, cross-talk and order complexity. We built the platform to easily adapt to new menus, restaurant technology stacks and centralized menu management systems in hopes that we can work with every quick-serve restaurant chain across the globe.

Source: ibm.com

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The customer experience evolution: Today’s data-driven, real-time discipline

An evolution of customer experience (CX) was to be expected. Throughout modern history, organizations have encountered internal and external challenges that changed how they interact with customers and how customers view those organizations.

Read More: C1000-132: IBM Maximo Manage v8.0 Implementation

Advancements in technology mean customers can order virtually any product and receive it in less than a week. For software solutions, they can get access immediately and often in a seamless experience.

Across arguably every industry, business leaders view a great customer experience strategy as a key differentiator. Brand loyalty, already on the wane, became even weaker due to the pandemic. For example, McKinsey found that 50% of consumers reported they would switch brands if their preferred brand was unavailable due to shortages.

Customer needs changed. Customer retention is difficult to keep high. Today, providing a positive customer experience is more of a challenge, or more of a critical need, for companies. To achieve this, more organizations must prioritize being customer-centric.

How we arrived at this CX environment

Early days of retail

Before mass media, it was harder to know what other products were available outside of the ones offered by the local store. Before globalization, it was more difficult to purchase products from far-flung locations. Many customers were limited to the goods and services in their near vicinity. And if something went wrong with a product that could be fixed, they would go to a local mechanic. They likely had strong ties to their local merchants and trusted their opinions on which products they should buy. They were much less likely to have any meaningful relationship with the product manufacturer unless those products were made and sold locally.

As a result, brand loyalty was stronger and customer preferences changed less frequently. Today’s customers, however, have a wide range of options and are less loyal due to several external circumstances. It is harder to maintain customer satisfaction and increase customer loyalty.

The Internet and rise of e-commerce

It is no overstatement to say the Internet changed everything about business. Consumers could learn about new products and services without leaving their homes or turning on the TV. They could start shopping online and buying products directly without leaving their homes. For product manufacturers, this is perhaps the biggest leap forward for customer experience. Previously, their direct customers were mostly retailers or resellers, who sold to the end users in store.

Being able to sell directly to customers meant many of these companies had, for the first time, direct relationships with those end users. They could more directly influence customer loyalty beyond the quality and price of their solutions. They were more directly responsible for offering memorable experiences and providing customer support. And thanks to online metrics, specific customer feedback, and data analytics, these retailers had more information about their customers than ever before.

Increasingly organizations expanded what they offered. Look at Amazon, which started with books and moved into virtually everything else. Long-standing UK pharmacy Boots saw its website visitors rise from 7,000 people a minute to 19,000 during the pandemic, so it needed upgrade its entire infrastructure and tools. It turned to IBM to provide the solution, enabling Boots to easily handle, at peak, over 27,000 visitors without an issue.

Customer journey mapping

The customer journey is more complicated. Today, a customer could be influenced by one channel (e.g., out of home) and make their purchase through another channel (e.g., a mobile app). This omnichannel revolution means organizations must monitor multiple customer touchpoints and better understand which mediums feature more customer interactions. This means organizations need to devote more resources to improving their SEO, mobile apps and social media presences. They need to determine where they should spend their advertising dollars to reach the most persuadable prospects and create an organic inbound engine to capture them.

Consumer advocacy

The rise of social media platforms, chatrooms and message boards gave consumers a voice. They became more willing to express their interests and frustrations with brands, creating a scenario where those organizations needed to monitor conversations and triage responses based on the issue and the influence of the consumer. In some respects, this has given organizations valuable insights from real-time market research and customer feedback loops. But it also raises the bar for what organizations need to do to meet customer expectations. That extended the responsibilities of contact centers and social media or PR teams to respond in real-time.

Segmentation

The rise of cookies, digital media and third-party tracking created personalization and segmentation. This enabled organizations to send individual customers messages that felt tailored to them. Now organizations try to improve the user experience on their website by organizing information based on individual consumer preferences. Social ads can target specific users based on demographics. And they can segment which audiences they target based on purchasing power. A recent IBM Institute for Business Value survey found 57 percent of respondents said meeting customer demands for more personalized experiences was their top reason for adopting AI.

The next wave of technology driven CX

We’re entering a new age of customer experience driven by digital transformation. New technologies like artificial intelligence (AI) and machine learning (ML) will drive automation and further enhance the CX suite. Chatbots using generative AI and natural language processing will encourage more customers to use self-service tools for their simplest problems. That frees up human customer service representatives to focus on the biggest issues that can create happier and more loyal customers.

AI will power predictive analytics that will help organizations understand better when customers may have an issue or when it would be an opportune time to reach out to them. Organizations that can create compelling customer experiences can use virtual reality (VR) and augmented reality (AR) to show potential customers a facsimile of their services.

Customer experience will continue to evolve

The future of customer experience is bright. Providing a positive customer experience can become a competitive advantage. IBM can help enterprises apply trusted AI in this space for more than a decade. Generative AI has further potential to significantly transform customer and field service with the ability to understand complex inquiries and generate more human-like, conversational responses.

IBM puts customer experience strategy at the center of your business, helping you position it as a competitive advantage. With deep expertise in customer journey mapping and design, platform implementation, and data and AI consulting, IBM can help you harness best-in-class technologies to drive transformation across the customer lifecycle.

Source: ibm.com

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