Be it large organizations or SMBs; AI has the potential to alter the infrastructure of changing businesses dramatically. 

The business impact of AI is ramping up globally, and the most benefited countries will be China (with a 26% boost in GDP) and North America (with a 14.5% boost in GDP) by 2030. 

AI in Organizational Culture

Increasingly, organizational leaders recognize that AI is used to discern performance drivers that cannot be identified by experience alone. AI provides opportunities and assumptions for re-examining the corporate culture & effectiveness. This guides the team behavior and enterprise goals central to organizations’ culture. In a report of the 5th annual survey by MIT Sloan Management Review & Boston Consulting Group conducted on 29 industries and 111 countries, 64% of companies said they had integrated AI into their processes, improving performance KPIs. 

AI-based solutions expand the team’s vision of work in novel ways and mutate the company’s cultural DNA. When teams become efficient and improve their decision-making, it results into collaborative learning, morale, and clarity of roles. Let’s figure out how AI affects cultural aspects at the team level.

Top 6 Cultural Benefits of AI in Enterprise

Organizations are under the gun to demonstrate positive results from their considerable investments in AI. Hence, many businesses have turned to AI to revolutionize their work culture. Here are a few points that demonstrate how AI brings positive employee experience.

1. Enhanced Decision-making Enhances Team Confidence: Scaling the use of AI across organizations enhances decision-making, ultimately enhancing teams’ confidence. This, in turn, inspires the team to clarify individual roles, making it easier for teams to collaborate on a human level.
The outcome is greater trust and doubled investment in AI.

2. Expediting Business Processes & Financial Benefits Increases Learning Opportunities: When AI leads to improving business processes, the technology creates further learning opportunities for employees and boosts employee morale. AI acts as a cultural catalyst and leads the charge into new analytical endeavors for employees, creating better learning opportunities. AI-powered tools monitor employees’ performances and enhance their improvement areas. It also helps in foretelling future improvements by studying behavioral patterns.

3. Deploying AI With Innovation Creates Value in Organization: AI brings novel ways of working and boosts employee confidence which ultimately helps in translating business benefits into values like greater resilience and competitive advantage among employees. When employees are burdened with excessive work, the organization fails to leverage the potential of collaborative efforts. AI-powered tools assist leaders & managers with employee data and help them form better teams with complementing capabilities.

4. Effective Use of AI Clarifies Team Responsibility: AI-derived knowledge helps experts improve their skills. For instance, in the healthcare industry, AI helps call center employees handle customers effectively by studying the behavior of customer queries and other parameters. Enhanced operational efficiency defines team roles and responsibilities clearly. 

5.Improved Decision from AI Enhances Collaboration: The team’s decision quality enhances collaboration. The new age of AI communication has enhanced cooperation for the remote workforce in diverse locations with different time zones. Digital collaboration in today’s remote work culture has enhanced employees’ desire for improved connectivity. 

6.Innovative AI Improves Competitiveness: Employees who use AI to explore new ways of creating values can compete in better ways and are capable of facing challenges. 

AI enables tasks and shifts the data-related strategies in real-time and builds up new test data. This can ultimately help in sales promotions and promote competitiveness. 

Executives in different organizations are implementing AI to develop and refine strategies related to employee performance. Five or ten years later, new organizations will have AI toolkits to communicate better and add value to work. 

Conclusion:

AI will Encourage Employees to Outperform KPIs 

Employee experience is just one of the many avenues that AI has revolutionized. Tools empowered by AI/ML are enabling organizations to offer better collaboration and targeted learning opportunities. AI tools are developed to understand employee behavior and mindset which will help businesses enhance their employee happiness index .

Through repeated application & managerial attention, the virtuous cycle between organizational culture & AI use cases will result in a more cohesive organization. In the future, employees will be expected to outperform their existing KPIs.

To find the right AI-solution for your business, connect with our experts.

The post Top 6 Cultural Benefits of Using AI in Enterprise appeared first on Techment.

Artificial intelligence (AI)  is changing the way businesses operate fundamentally today, from the way they communicate with their customers through virtual assistants, to the automation and even the management of network security. The past year saw a number of new strategic priorities for companies that needed to work to meet the needs of their customers while finding ways to be more profitable, more responsive, and make decisions faster. Companies that can overcome barriers to adoption and distribution and leverage AI and automation tools to address these challenges will be able to deliver value to AI in 2022.

While many companies have tried new ways to achieve promising results in AI model development, these practices hold great promise: 

• The integration of product development into IT operations (commonly referred to as DevOps) 
• The focus is on continuous delivery. Both focus on automation, continuous monitoring, and sharing of information and processes throughout product development and IT operations.

DevOps is the combination of processes and culture that brings the development and operation teams together for a faster software development process. As development and testing run parallel, it makes the process smooth, thus improving the scope of updating and delivering the application at a faster pace. 

DevOps never concludes, it’s an infinite loop of constant activity of improvement!!!

With DevOps (How DevOps is changing application infrastructure landscape?) enabling a shorter development phase, lesser deployment failures, improved efficiency, and collaboration, other technologies need such implementation to scale their benefits for the furtherance of adoption by organizations. DevOps is now a de-facto standard that focuses on processes that support applications. Overnight adoption of change and innovation are often repelled by people and systems and this is more intense in the case of intelligent technologies. 

According to a survey of IBM in partnership with Morning Consultant, “From Roadblock to Scale: The Global Sprint Towards AI”, 78% of respondents find difficulty in AI deployment.

Companies looking forward to the next wave of advancement with AI- ML need DevOps to facilitate continuous delivery, deployment, and monitoring. It has the potential to stabilize and streamline the AI model release process through continuous integration (CI) and continuous deployment (CD) principles for better operationalization from design to production, faster implementation, and automation of these technologies.

How does DevOps facilitate AI?

For the right artificial intelligence (AI) development processes in place and for the correct time of execution, companies implementing DevOps in AI will observe the following advantages:

• Speed-Up Process: For many companies, AI development is still new and for such a new operation, a testing environment needs to be created. A time-consuming process of deploying code to software and then testing becomes cumbersome. DevOps eliminates such time-consuming tasks, hence faster time-to-market.
• Enhances Quality: AI heavily depends on the quality of data processed by them. Training AI models on bad data lead to skewed responses resulting in a bad outcome. When unstructured data appears in the AI development process, the DevOps process helps in cleaning datasets and improves model quality. 
• Scales AI: As AI has too many roles and processes, scaling it is a challenge. DevOps unburdens the AI with faster delivery and eliminates rework and allows team members to focus on the next step.
• Brings Stability in AI: Continuous integration is one process in DevOps that prevents the release of products in case any fault appears. Hence, support the release of an error-free model, which is more reliable and stable.

How  Executions from DevOps Culture will Improve AI Performance?

AI has transformed many business processes and developed many but still faces challenges as it requires a large number of human efforts and technologies are emerging to address them. Obtaining a dataset, training, cleaning, and predicting seems more complex. Building a smooth generalized training pattern i.e., bringing a particular technique from one circumstance to another, is a different challenge. 

For more striking results businesses should bring change in the operational processes like DevOps culture which produces an efficient development, deployment, and operation pipeline. Make AI operations adaptable with DevOps culture in these stages:

1. AI Data Preparation: Dataset preparation is the process of transforming raw and running through machine learning algorithms and converting them into insightful ones to make predictions. Since the data preparation steps vary according to industry, some basic steps involved are collecting, cleaning, transforming, and storing data which consumes the maximum time of data scientists. To incorporate DevOps into data processing, automating the process is the only way to handle this pipeline. This is also known as “DevOps for Data” or “DataOps”. 

DataOps uses technology to automate the design, deployment, and management of data delivery. DevOps brings team support and streamlines the delivery of work. 

2. AI Model Development: The difficult as well as a prominent part in developing AI/ ML model is its development and deployment and keeping the environment operational and supportable. The team leading the development process should automate in development pipeline through processes for parallel development, parallel testing, and model versioning. As AI/ ML (How do AI/ ML benefit small and large businesses?) projects revolve around real-world use cases in real-time, frequent and small iterations must be used during the development process and then put into production. 

This implies CI/ CD approach for AI/ ML and here are some ways how it works:

• As AI/ ML is based on experiments and iteration of models, it takes ample time to build, train and test the model. So carve out a separate workflow and accommodate different timelines for building and testing.
• This isn’t a one-time construction model but the consistent improving model that can deliver value without compromising. So collaborating with the team to consistently improve the practice, error check will improve the model lifecycle and its evolution.

3. AI Model Deployment: DevOps methods make AI models portable and modular to handle incoming streams of data in real-time on highly scalable and distributed platforms. Such architecture boosts the AI operation. As enterprises are involved in the production of AI several challenges emerge like:

• Maintaining traceability, 
• Recording experiments,
• Searchability of models, 
• Visualizing model performances, etc. 

For this DevOps and IT teams heavily need collaboration i.e., they need central store model artifacts, ML engineers need to rearchitect the production model. Hence, seamless teamwork between data scientists, IT, and DevOps teams becomes important. In brief:

• DevOps team needs to monitor the system for health checks.
• Data scientists need to monitor model degradation, testing, etc and collaborate with the DevOps team.

MLOps, or machine learning operations, is another way of the culmination of people, processes, practices, and underlying technologies that automate the implementation, monitoring, and management of AI/ML models in production in a scalable and fully governed manner. Creating an MLOps foundation enables data development and production teams to work collaboratively and leverage automation to deploy, monitor, manage and govern services and machine learning initiatives within an organization, process, and culture.

4. AI Model Learning and Monitoring: DevOps is a well-known and widely used practice in software development. It’s proven and allows teams to shorten development cycles and make releases smoother and faster. AI / ML models can produce predictive results that change, or “drift” from the original parameters that were defined during the training period. The identification of the drift category will determine the corrective actions necessary to bring the forecast performance back to an acceptable level. This brings DevOps with the concept of continuous learning to monitor drift and accuracy to stay relevant for a longer time. 

Continuous improvement in DevOps requires an organizational commitment to continuous learning and mastery at the most advanced level. Skills are required to include the implementation and operation of advanced operating practices, advanced continuous testing, and observability. 

To conduct continuous improvement and learning consider the following practices:

• Conduct continuous feedback from data scientists.
• Identify and conduct training goals for each role in AI application.
• Set training goals for data scientists, DevOps teams, and IT leaders and make sure access to tools and resources are available to teams.

When you launch AI, you use automation in a meaningful way that covers all business operations. To deliver maximum value, any model development process must be both accessible and extensible. Truly exceptional solutions not only democratize access but are also proven to be robust and flexible enough to support the business goals. 

Continuous Integration will Speed Up the AI Modeling
For most businesses, software development and implementation involve an iteration period in which all changes are stopped, i.e. development is no longer allowed, and a period of time when all changes are stopped. At the same time, a separate team must set up the supporting technology infrastructure, a process that can only take a  few weeks. The version should not be distributed until all of these steps are completed.

The problem with many companies is that they abandon their AI development process, so they lag behind those who realize the scalability of technology and cultural practices. The expansion and creation of a fully automated AI model need a combination of DevOps culture and technologies. Focusing on the most profitable automation opportunities is the most productive way forward. To support these opportunities, developers must integrate sophisticated automated testing into their IT architectures. While companies can often prepare to change their AI development processes, continuous delivery is therefore required. Continuous deployment (CD) increases the speed of companies to market high-quality products and services.

Development teams can learn and make decisions quickly based on data that affect development and performance. At Techment Technology, we focus on DevOps culture and facilitate the development and modeling process for clients.

The post How DevOps Practices will Expedite AI Adoption in 2022? appeared first on Techment.

Before COVID-19, the workplace saw employees at the workplace and was the most favorable factor to signify the engagement of employees and their presence. The norm changed after the pandemic where everyone had to confine themselves at their accommodations and work from home culture gained significance. The utilities and medical care facilities too transformed towards a digital-first strategy to emphasize digital technologies at the workplace, whenever and wherever possible. 

The remote work or work from home culture hyped the extensive use of videoconferencing, cloud infrastructure operations and management for remote accessibility of data, cloud talent management, self-service tools for improved efficiency and transparency of employees, etc. For enterprises, it became significant to control costs and mitigate uncertainty which forced them to adopt similar virtual strategies in the new normal. 

Most companies had already started their digital transformation (Digital transformation strategies for sustainable success in 2022) journey before the pandemic had also seen a shift in workplace technologies, also giving them a chance to transform their work culture (A new culture with employee belief and organizational value) in the next frontier of 2022. A considerable transition in new normal 2021 was from tech giant Facebook, Inc. to Meta Platforms, Inc. or simply Meta which focuses on products for creators and communities that would work to realize the vision of creating a 3D experience using extended reality, challenging workplace culture, and setting forth new expectations for advanced technologies in 2022 and beyond.

Which 6 Workplace Technologies will Dominate in 2022 and Beyond? 

There may be long-term changes in the current and future workplace technologies, these new technologies will help an organization remain resilient and better equipped to adapt to future crises. The preferred technologies that will build resiliency and make the workplace better are:

1.Conversational AI: For the digital-first workplace, companies need to provide employees with digitally engaging tools. The new-age workplace culture needs automated interfaces for interactions which are known as conversational AI or chatbot, which are potent tools. Apart from supporting the customer support team, this will also help HR leaders by automating the bulk instructions and also the in hiring process by utilizing recruitment automation bots.
According to Kindly, a leading company in conversational AI service provider, the conversational AI market will reach $ 77.6 billion in 2022, and the size of the global chatbot market will surpass $ 1.25 billion by the year 2025.
Perhaps the biggest takeaway this year is that conversational AI adoption has continued to increase, especially in the customer service industry, for both chatbots and voice bots. We’ve seen several organizations develop their chatbot technologies. Google has come a long way from Google Translate to Google Assistant. 

2. 5G and Edge Computing: The major concerns in remote work are IT infrastructure and in-person collaboration, employee awareness, and telecommunication. With new workplace culture and digitalization, 2022 will see high adoption of 5G and edge computing (How 5G will bring transformation in edge computing?). This will enable AR-based meetings, private network access, cloud-based applications from mobile devices and corporate laptops.
5G coupled with edge computing (aka “last mile”) which sits between cloud data centers in the core of the internet and billions of devices in home and offices in mobile networks, will reduce the latency, bring higher throughput, and more connectedness among people. It is expected to transform the way consumers interact and the way businesses interact with consumers, employees, partners, and suppliers.

3. Low-Code Development Platforms (LCDP): These platforms have become popular as it provides a chance for non-developers to develop apps of varying complexity to meet business demands for development and automate the process. Software developers have acknowledged its flexibility and saved half of the development workload. These custom software development platforms will push companies towards digital transformation as citizen developers will be able to perform, which was in a state of stagnation since the pandemic hit. Low-Code development (Which Low-Code trend will work for your company?) platforms will hit these areas:

• Will accelerate legacy modernization.
• Empower the workforce to develop applications with confidence and push hyper automation across the workplace.
  Gartner forecasts the worldwide Low-Code development technologies market to grow 23% in 2021.
  Low-Code has already joined the race of hyper automation and will drive digital transformation in 2022 as this will bring innovation in application development and integration.

4. Hyper Automation: The business-driven and disciplined approach rapidly identifies and automates the business and IT processes and involves the orchestrated use of multiple technologies, tools, or platforms which includes:

• Artificial Intelligence (AI)/ Machine Learning (ML)
• Robotic Process Automation (RPA)
• Business process management (BPM) and intelligent business process management suites (iBPMS)
• Integration Platform-as-a-Service (iPaaS)
• Low-Code/ No-Code tools
• Packaged software and others

Hyper automation is generating interest among organizations for adoption in new normal as it spans the whole spectrum of operations, using digital tools to simplify many time-consuming tasks. This is already giving results to financial service providers in data management and production.

5. Blockchain: Also known as a “trustless” network (because partners don’t have to trust), blockchain uses a shared and immutable ledger that can be only accessed by members with permission. It has significantly reduced costs and errors and eliminated the need for a third party to verify transactions. When merged with enterprise resource planning (ERP) which centralizes all business data, controls the inner processes, and helps in making informed choices in the future.
Blockchain (Why blockchain is integral to healthcare’s transformation?) and ERP have an important part in common. ERP works on a single data modification system. Blockchain also controls a single data table that is shared by millions of buyers across the web. The information is available for each member node of the blockchain, but no one can tamper with or modify the report without any agreement.
This characteristic of distributed ledger variation is the most critical point in integrating ERP with blockchain, and this type of coordination can be effective in building trust between different organizations and teams within the organization.

6. Extended Reality and Metaverse: The new term “Metaverse” popularly known across the globe after Facebook, Inc. changed its name to Metaverse Platforms, Inc. and entered into the new phase of extended reality is a three-dimensional internet powered by augmented reality (AR) and virtual reality (VR). Its 4 important features well define what it offers in the new normal and what it will look like in 2022. It is persistent, real-time, infinite, self-sustaining, and interoperable. 

This comes with the biggest advantage of enhancing visibility and telecommunication among employees. Apart from this, it enhances productivity as you can add an extension of the resources required. 

Working in the metaverse requires a combination of artificial intelligence (Why startups must adopt AI), powerful cloud connectivity, which could take several years to develop. There are security concerns that are persistent metaverse that would need to capture and store user data to provide an intuitive experience.

The past year has seen the world change rapidly. Heading into 2022 challenges organizations to create a balance between a technology-powered future where constant evolution with hyper automation and smart technologies will dominate and here the future of work lies. 

Infusing Extended Reality will be the Next Frontier of New Digital Workplace

The future workplace demands more automation for monotonous tasks and will be in high demand as the need for such processes will grow strongly. This can free higher-value tasks in human resources and others. This depends on companies incorporating artificial intelligence (AI) and paves the path for incredible innovation and growth exponentially. It would be fair to say that emerging technologies like artificial intelligence, IoT, 5G, cloud, etc. are yet to be unlocked for full potential which is a positive sign for innovation in 2022. 

A better and more natural way to interact with distant colleagues is necessary if businesses and employees are to thrive in the new normal. The commercial use of extended reality has evolved over time, and as hardware and software have improved, companies are now looking for these tools to help employees connect and collaborate better. This will be possible when the requirement of employees, efficiency at the workplace, and flexibility are kept at the forefront.

Techment Technology is confident about the adoption of these technologies in 2022 and empowers the team with more advanced technologies. For more information on advanced technologies and their implementation or any guidance on how to step into creating a better workplace, contact us.

The post Which 6 Workplace Technologies will Commit to Digitalization of Workplace in New Normal? appeared first on Techment.

The customer experience has always been crucial in business innovation and digital transformation with several digital touchpoints. The end-to-end customer experience improvement is expected each time companies aim to scale their business and companies do try to anticipate the needs of customers and provide a personalized experience. In the era of automated technologies becoming an organization with great customer experience isn’t novel. Companies are now trying to step into a business of Experience (BX) with shifting consumer behavior. The new reality pushes businesses to step into a new phase to make their customer experience their brands in depth.

A great experience is achieved when customers’ experience (CX) touches the desired outcome where companies can’t sit on the excuse of changing consumer expectations. A mature step of Business of Experience (BX) in the upcoming year 2022, will be a more holistic approach where all stakeholders will contribute to developing the exceptional customer experience. 

When companies provide the best customer experience, they start creating a positive financial impact as well. So organizations are now trying to step into technologies like AI and ML, that serve a ubiquitous purpose of CX. Customer experience has covered miles in terms of digital touchpoints and the internet and evolution in intelligent technologies has made it compulsory to march towards improving CX.

6 AI Strategies to Uplift Customer Experience 

In the age of AI, it’s time that customer segmentation strategies are more actionable on a personalized front. AI has the power to improve the customer experience journey at every step. As technology experience grows, customers too find that digital services can be improved across a variety of sectors. Provisioning of the desired level of specificity is possible through collaborative, elastic, and responsible AI at an enterprise scale. 

According to IDC’s survey, leading organizations that are well-positioned in their technology and data capabilities are in a better position to capitalize and pivot for opportunities arising from pandemics. 

Here are some strategies of leveraging AI for better CX:

1.Customer 360 (C360) Through AI: What customer experience demands are complete insights about customers that can serve customer satisfaction (CSAT) and personalize the experience with them. Customer 360-degree view  (C360) refers to information about every interaction from inquiry to product purchase made by them. The following steps would determine what customer 360-degree insight you get:

• Predicting Survey Scores for Better CX from CX: Instead of taking reports from customers, take reports from survey reports to detect the anomalies and try resolving them. 
• Engage Employees in Streaming Analytics: AI has the capability to provide microlearning so employees need to practice customer-centric behavior in real-time which enhances customer satisfaction (CSAT). 
• Watch on NPS for Better CSAT: Net Promoter Score (NPS) is a core KPI within organizations, the higher the score, the more satisfied the customer you have. AI directly impacts NPS and implementing it can resolve NPS issues for organizations. Some ways AI can improve NPS score:
• Companies can develop chatbots for a more effective self-service environment for customers or any AI for customers to do transactions or account changes.
• Using AI, an intelligent routing system can be developed to guide customers to the fastest solution to problems.

2. Personalization Engine: A personalization engine measures the optimum experience of individual customers based on their past interaction, current practices, and predicted intent, which enhances the marketer’s knowledge about customers. 

• Customer Intelligence Platform: Adding AI to this platform engine brings the digital experience of customers to the surface like layout, menubar, CTA buttons, etc., for any channel according to the visitor’s persona. This uniquely does:
• Optimize personalization in real-time based on clicks and purchases.
• Deliver targeted and dynamic content that unifies disparate sources of data in real-time.
• Provide content-rich individual digital experience.

Watson Assistant by IBM is a full-service AI chatbot that integrates your CRM system to automate tasks and guide the marketers to the information needed to resolve customer queries. 

• Product Intelligence Platform: AI-powered product intelligence platform provides a 360-degree view of your product positioning and performance. This provides data about product availability across the siloed system, service lines, and geographies. Also enhances the product design by incorporating customer feedback.

3. Machine Learning for Automated Learning: Machine learning (ML) ) provides systems the ability to learn automatically so that they can respond according to data given to them. When used with a chatbot, it will not only learn about specific responses but also determine when should the conversation be handed over to the human agent. This definitely is a step ahead in conversational AI to enhance customer experience. 

In this direction, Uber has its Customer Obsession Ticket Assistant (COTA) empowered by ML providing a most accurate solution to the thousands of tickets surfacing daily on the platforms. 

In a forward approach, companies must proactively use ML to engage customers at the right moment rather than waiting for customers to ask for new products and provide reviews. 

• Start with core journey dataset and build to improve accuracy: customer-level data, operational data, financial data
• Combine with customer interaction data

Predicting Customer Churn Rate with ML:  This rate is basically the health indicator of business which tells the percentage of customers who abandon the product. With the use of ML companies can find out which customers aren’t fully satisfied with the service, this is Effective Churn Modelling. With this effective customer retention actions can be made.

4. Use Data to Align with Business Objectives: After customer profile what organizations need is to figure out what insight they want to generate. Syn the tech, data, and human agenda for achieving bigger objectives and reimagine business objectives and operating model. With advances in customer expectation, enabling customer-eccentricity at a greater scale to integrate tools, technologies, data, and processes is imperative. This will build and maintain the business of experience (BX).
When it’s about data, building agile technology with cloud keeping in view the technology stack of companies will take it to the next level in 2022. With cloud capabilities, companies will reinvest in data powered by AI to drive performance.

5. Marketing Attribution Platform: The process of evaluating touchpoints on the path of the customer journey is called marketing attribution. Marketing attribution software allows marketers to determine which channels, campaign landing pages had the greatest impact on revenue. The manual process of attribute collection does not capture use engagement adequately.
Automating and scaling the painstaking process of the attribution process will reduce the manual process and introduce more automation by integrating CRM (customer relationship management). This has perfect space for AI to incorporate vast amount of data from various sources in a scalable way down to a granular level.

• Build Recommendation Engines: This is a data filtering tool that uses ML to recommend the most relevant item to a particular customer. It operates on the principle of finding patterns in consumer behavior data and improving business across industries. The more data it collects, the more efficient and effective it will generate suggestions. 

6. More Focus on Use Case to Drive Value: It is important to have a clear vision of how the information will be applied and to focus on some specific use cases that will create immediate feedback. As a simple structure, organizations can look at key sources of opportunity, weak spots, or both across customer journeys and think about how a predictive system might create new or improve existing solutions that might have. a direct impact on customer behavior and sale. Leaders need to ask which use cases present a clear opportunity to drive value so they can build momentum and gain support.

Overall, companies need to be more thoughtful about where to focus their efforts, as current regulatory and economic scenarios require a reassessment of long-standing practices. As leaders strive to get a more complete picture of customer preferences and behaviors, they continue to rely on outdated surveys that have been the backbone of CX for decades and this needs to be changed.

Transforming Customer Data into Action to Expedite Business of Experience (BX)

In an era of digital interaction, AI has brought pace in various sectors of life and changed various performance parameters, especially in customer-centric businesses. Companies approaching towards providing greater customer satisfaction (CSAT) will need automated and more personalized plans, adjust their marketing strategies, and need to find or develop more use cases that will drive business value and flourish in the business of experience (BX). Importantly, AI and ML have reached several touchpoints with data-driven and analytical approaches like never before and continuously improving customer experience over time.

The context of using AI for a customer-centric approach aligns more with withdrawing relevant data and how it can be improved whether, through customer 360-degree approach, personalized engines or to develop use cases with help of data, and all processes are continuously evolving. The next step of disruption will lie in the business of experience (BX) where companies are heading now.  The winning plan will be successful for companies when they will be aware of what customers will look forward to i.e., take relevant customer data, make experience innovation a regular habit in the organization, and synchronize tech, data, and human agenda with company strategy. 

Techment Technology plans to implement a more customer-centric approach to enhance the entire customer journey and improve CSAT. With our offering of design, marketing and content we are trying to improve CX for the future and finding new ways to win customers.

The post AI to Fuel Customer Experience (CX) in 2022 appeared first on Techment.

Now that we have set the tone of the change we are dealing with, let’s understand why. 

IT has evolved from a cost center into an inextricably embedded, valued business asset that supports a company’s core operations and value delivery. 

In the digital-led world, an IT department is unable to keep pace with rapid business scaling, snowballing complexity, and disruptive innovation because —IT personnel cannot grow at the same rate as the complexity and disruption. In a world where most operations will be via the cloud, it is important to switch to a better alternative – AIOps.

AIOps, Artificial Intelligence for IT Operations, is platforms and software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”

The Present Situation and Challenges 

Switching to AIops has become essential because of the massive move towards the cloud. Here are a few stats that clear the picture:

• Public cloud computing market will top massive $266 billion by 2020, Gartner predicts
• 67% of enterprise infrastructure will be cloud-based by 2020, Sys Group
• 85% of businesses worldwide are already making use of cloud technology to store information, Sys Group
• 37% of the global IT budget for 2019 was on Cloud Computing, Tech Radar

Cloud adoption stats for 2020 report that software as a service (SaaS) takes up 48% of the cloud computing budget, while infrastructure as a service (IaaS) accounts for 30%. The remaining 22% is claimed by the platform as a service (PaaS).

• 94% of the internet workload will be processed in the cloud by 2021, Network World

The adoption of cloud has many advantages, but as the business grows and develops, it becomes increasingly challenging to manage it while troubleshooting 24*7. It is not possible to do this manually without significant disruption and large costs associated with the workforce. 

AIOps: Present Status

The use of AIOps is fairly limited but is picking pace. In 2017, it was just 5%, but Gartner expected it to rise to 40% by 2021 (“Market Guide for AIOps Platforms,” Gartner, August 2017). The pandemic may have given it a nudge for better growth. The AIOps Market is expected to grow at a CAGR of 26.2% during the forecast period 2021 to 2026. Mordor Intelligence.

Behind the Rise of AIOps

There are a plethora of reasons why interest in AIOps is rising. 

AIOps can help transform IT operations into a service-oriented model with diverse benefits such as profound and real-time insights into: 

Artificial intelligence (AI) can be used to apply algorithms to IT operations and enhance current data analytics capabilities to improve outcomes. ML can autonomously analyze and process the massive quantity of data being generated by today’s IT operations — learning patterns for faster correlation and root-cause analysis.

The AIOps deliver value in terms of these desirable Outcomes:

• Prevention of future issues and downtime
• Faster triage to reach the root cause
• Use cognitive pattern technology to reduce event noise
• Optimized processes for hassle-free scaling and managing complexity
• Unprecedented agility to keep up with technological change 
• Early intimation to business associates with better risk assessment
• Superior Operational Efficiency
• Improve productivity with real-time visibility of business services

9 Unique AIOps Advantage for Businesses 

The adoption to multi-cloud IT environments demands better management, and AIOps just deliver that as it helps:

• Automate processes
• Drive faster resolution time
• Provide deep service visibility
• Ease of operations management at scale
• Simplified and unified IT operations management
• Deployment of proactive indicators
• Execute remedial action autonomously
• Cognitive pattern capabilities
• Access to accurate data for business collaboration 

Three Pillars of AIOps Value Creation

The core value created with AIOps can be subsumed into three areas: 

Health Check, Monitoring, and Course Correction:

• Advanced and predictive problem identification and remediation     
• Use of Historical data for early problem detection 
• Reducing event noise with ML and provide a real-time health status

Holistic Service Views: 

• Manage, collect and correlate incident, and event data
• Service maps: Precise and updated
• Cognitive pattern tracing 

Analysis for business services

• Data Analysis at scale
• Proactive recommendations for potential problems
• Rapid root cause analysis
• Prioritize resolution by business service

KPIs for AIOps

Measurement of outcomes is as important as the creation of the solution. Here are simple KPIs (Key performance indicators) that can be applied for AIOps for best outcomes. :

• Service and support ticket reduction 
• Faster Mean Time to Repair (MTTR)
• Reduction of P1 (major) incidents 
• Service availability improvement 

The Future is AIOps

Everyone is switching to the cloud, the data generated is massive, and with continuous updates, it is simply untenable to be effective with old-style management of IT operations. Also, software development has become so rapid that it is increasingly becoming impossible to monitor and remediate the service issues in the vast cloud environment. Not only would cost, but the damage of downtime and lengthy corrective measures necessitate that firms switch to AIOps for hassle-free management of their IT operations. 

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A lot has changed in one year; AI has slowly turned from a costly and complex option to a necessity. Here is why:

• It is the only option for Accessible, Affordable, and Quality healthcare 
• It can catalyze the shift from – physician-centric to more patient-centric care
• Shortage of care workers is a concerning issue
• Medical Error reduction and efficiency 

According to McKinsey, AI and automation technologies can add value to nursing activities by 10% by 2030, while Accenture estimates that AI will engage with 20% of unmet demand by 2026. The estimates may vary, but AI is rising to be an integral part of the healthcare system.  

Four Pillars of AI Applications in Healthcare

AI is adding value in many areas, but four areas remain the mainstay. 

Patient Care 

• Health Assistants and Personal Trainers

AI-based chatbots are being used as health assistants and personal trainers. Some of the use cases of chatbots in healthcare include scheduling doctor appointments, providing medication reminders, and identifying the condition based on symptoms. Start-ups like Babylon Health and 

• Automated and Assisted Diagnosis & Treatment

AI-enabled chatbots are getting good at helping patients with self-diagnosis as well as in assisting doctors for the same based on symptoms and condition of the patient. Today, they function in an assistive capacity, but in the future, as acceptance grows, they will do more.

• Pregnancy Management

Monitor the progress and stay worry-free with early diagnosis. 

• Real-time Patient Prioritization and Triage

Prescriptive analytics on patient data to enable accurate real-time case prioritization and triage.

Empowered Physician 

• Personalized Medications and Care

AI uses patient data to identify the best treatment plans thereby lowering cost, improving cure effectiveness, and reducing time to recovery. 

• Surgical Robots

Today, robotics has improved enough to assist surgeons, with rapid leaps in AI, their role would become increasingly integral to surgeries as they excel in repetitive and tedious tasks while helping surgeons increase the precision of surgery. 

• Prescription Auditing

AI audit systems are getting good at lowering prescription errors.

• Clinical Trials

AI algorithms easily medical data and help in the identification of patients’ responses to treatments based on their characteristics, thereby, helping deliver personalized medicine and treatment at scale.

• Alternate Diagnosis

Not only digital, but microorganisms also evolve fast, and so is the research in the field. It is not possible to know every development and with the assistance of AI, physicians can get the benefit of an alternate diagnosis that can prevent error. 

Medical Imaging and Diagnostic 

• Error Free Diagnostic results

Be loose over millions of people every year due to misdiagnosis, and AI can help change that. Its accuracy in producing diagnosis results eliminates human error, thus saving lives and costs.

Intelligent Symptom Analysis

• Predictive and Early Diagnostics

Be it cancer, or tumor, or any other disease AI has incredible predictive power that analyzes the data t, o predict a condition at the earliest. 

• Radiology Assistant

AI-enabled assistants produce the report of the scan with high accuracy. 

• Diagnosis via Medical Imaging

Skin cancer, oxygen level, and many other conditions are now diagnosed by AI-powered medical imaging. The speed of results and their accuracy both are game-changing advancements. 

Research and Development 

• Data Mining

AI is currently being deployed in the data mining of medical records. Cognitive technologies are helping healthcare organizations leverage vast data trove to power diagnosis.

• Drug Discovery

AI-startups such as Helix respond to verbal questions and requests and help in augmenting efficiency, better lab safety, manage inventory while staying updated on recent research.

• Drug Design

AI is now powering and automating drug design and compound selection by predicting protein characteristics which enable researchers to simplify protein design, streamline production issues, and discover new protein features.

• Pandemic Detection 

Applications such as BlueDot provide early warning systems for identifying pandemics by scanning hundreds and thousands of media sources worldwide in all the languages daily to foretell outbreaks in real-time. 

• Genetic Engineering 

AI helps companies produce better genetic material and identify the disease and its treatment. 

Taking the Digital Leap with AI

AI is a rapidly evolving need for medical institutions. Yes, regulations, cybersecurity, and Privacy issues loom large, but it is the matter of addressing them and not of questioning the suitability of AI.

The playing field in AI is getting even by day and calls for companies of all shapes and sizes to accept AI and take its help in serving the patients better.

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Advantages of Microservices:

• Easier Development
• Shorter Cycle Times 
• Development Technological Autonomy for developers to choose best tech, language for each unit
• Faster to scale, even with new features
• Easy and independent Upgradations

Unlike a monolith application, microservices comes with the added responsibility of monitoring the application because failure of one dependent service has significant upstream performance consequences. In recent years the need for monitoring and DevOps has increased, partly due to the increased usage of microservice infrastructures.

In the last decade, the rise of microservices enabled via containerization and Docker has been astounding. According to IDC, by 2022, around 75% of global organizations will be running containerized applications in production. But as the world prepares to shift more and more applications to the cloud, via microservies, new challenges are emerging and there seems only one probable, effective and feasible solution – Anomaly Detection via AI.

Read about Microservices, containers and Docker 

The Rising Challenges with Microservices 

The exponential shift to microservices architecture has made the systems more distributed with east-west traffic, resulting in increasing difficulty in detecting threats and other performance issues.

Millions of applications are running across the world, with each having hundreds of microservices held in containers which are accessed from any part of the world, making their management a living albatross for enterprises to ensure uninterrupted services with reliable quality. Today, it is incumbent on enterprises how they best monitor thousands of services and maintain optimal performance and reliability. 

• The increasing and ever expanding cloud expansion is making it hard to achieve the stability of the cloud ecosystem. 
•  Unexpected quality issues like slow access to data, web pages frustrate users; application crashes and data loss pose serious threat. 
• Numerous issues make identification of critical issues impossible in real time
• The identification of critical defects among hundreds of issues in real time is now the holy grail of microservices architecture. 

The Need for Automated Anomaly Detection 

Anomaly detection is the domain of identification of events that deviate from the expected or permissible outcomes and are undetected by manual human supervision. 

These  anomalies are critical as they contain hidden significant information that is hard to find. For example, anomalous readings from different sensors could mean faulty road or weather conditions that could lead  to  road  accidents  or  abnormal  points  from  MRI  images  that  could  indicate  the  presence  of  malignant tumors.

Challenges in Anomaly Detection

The challenge with microservices is that the performance of the end microservice depends on the series of services before it, hence the delay in the initial ones causing a cascade effect. When more microservices are used, the response time increases causing performance issues in a large distributed system. The issues are diverse and so are their symptoms such as:

When KPIs like Error rate go undetected result in system failures which impact user experience. The problem gets further daunting as each instance (copy of service, and there are many to prevent outages) of microservice needs to be monitored, and analysis of aggregated metric is also challenging, when this is done for multiple services. 

Types of Anomalies

To identify an anomaly is not a simple task, this would become clear with understanding the types of anomalies: 

• Point  Anomalies:  A  single  isolated case of deviance 
• Contextual  Anomalies:  Deviance visible only when given a context. Ex. high CPU usage with no users.
• Collective Anomalies: Deviation which becomes one when other instances are put together. They generally indicate anomalous system behavior. 

The Biggest Challenge with Anomaly Detection 

Subjectivity and dynamics pose great challenges; a point anomaly can become a contextual or a collective anomaly, the dynamics keeps on changing. Though it is tempting to put fixed error thresholds on what is not an anomaly and what is, but because everything is so fluid and complex, organizations must avoid using fixed approaches and instead follow a dynamic one. 

Automating Anomaly Detection with AI and ML

The microservices system has become incredibly vast and overwhelming. Manual supervision can not keep pace with it. The need of the hour is AI enabled anomaly detection coupled with automated corrective action. 

There are three types of monitoring modes: 

• Supervised
• Semi-Supervised
• Unsupervised 

AI Enabled Automation is the Future 

The health of a growing microservices system depends on a large part of its flawless functioning which is key to user experience. With numerous upgrades and features, complexity increases over time, manual monitoring falls much short. 

The future lies with AI-enabled supervision, under any mode, but it is crucial to prevent failures, and also that when they do occur, remedial action must be taken in real-time.  

Those organizations who want to derive the maximum benefit from microservices and cloud must consider deploying AI to keep an eye on the health of their rapidly expanding and growing microservice applications. 

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1. Data is Changing Nature of Competition

The time it takes for new technology to reach mainstream adoption is accelerating exponentially. Soon, it could take just a few years for the technology to reach over 50% penetration, juxtaposed with decades it took earlier. Change management has grave import for startups as no one can truly claim to be free of legacy systems. Business models must be continually redefined, and it is challenging for many entrepreneurs to keep up.

Willingness to continuously look at the creative destruction of existing structures and technologies is the way forward. 

As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 5000 gigabytes per hour. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy; a jet-engine needs servicing, a person is at risk of disease, etc.. Industrial giants such as GE and Siemens now sell themselves as data firms.   

2. Network Effect: The Data Warfare  

The big tech companies are leveraging data to amass incredible amounts of power. The network effect helps them as the greater the number of users they onboard, the more data they get. The data is then used to improve the product and services that then attract more customers or users. More users generate more data, and the cycle goes on; this is how the tech giants like Facebook, Tesla, Google have grown unprecedentedly.  Tesla collects the data from its customers and uses it to improve its software which has grown and developed breathtakingly. It  is the reason why Tesla is the world’s most valued auto company. Tesla is a data company, and to compete with Tesla, auto giants need a shift their mindset away from mechanical engineering towards data; that is the extent of change that has transpired. In the words of former Amazon executive John Rossman,  

“Data is the business model.”

3. The Threat of Not Investing is Very Real

In a survey by  Ernst & Young March 2020 report, finds that Cloud, AI and IoT are the top priorities of transformation leaders across the globe. The investment in technology for governance is also picking up impressively. As the pandemic has made digital transformation a matter of priority, startups must think of further expanding the technological capabilities for better competitive advantage. The other aspect of advanced technologies is that it is easy and rewarding to begin early; as organizations grow in size, it becomes exceedingly challenging to incorporate and leverage advanced tech.   


One of the interesting findings of Deloitte’s- State of Enterprise AI report 2020 is that a significant number of respondents (2727 global executives from nine countries) is that they feel AI is getting easier. The survey results indicate a buy over build preference. The lack of data scientists who can program in PyTorch or TensorFlow is also one influencing factor. But even though outsourced AI would clear one set of hurdles, other challenges remain.

The level of uncertainty after the pandemic is historic. Also, spending on digital transformation and advanced technologies is going to increase, and startups must take some technological leaps to create a competitive edge and be hyper-agile.   

There is an ever-widening chasm between those who are experimenting with AI and those who are waiting for the technology to mature, expertise to be easily available, or are adopting the “fast follower” approach. Then there are those bootstrapped or young startups which have not yet given a thought to AI.

There are three types of companies:

• Those who are experimenting and making progress with AI, ML and Big data, etc.
• Those who are “fast follower”, waiting for the technology to mature, expertise to be easily available.
• Those who see feasible, ROI driven advanced tech as distant possibility Except for experimentation, the other approaches are highly risky. 

4. The Perfect Match Startups and AI

Even though Ant Financial Services Group was spun out of Alibaba, its meteoric rise serves as an eye-opener for every startup. In 2019, just five years into operations, this company had 1 billion customers; Ten-times as many as the largest US bank with a tenth of staff. It became a company with a valuation of $ 150 billion, half that of the world’s most valued financial-services company, JPMorgan Chase. The reason is that at its core, AI runs the entire show. From loans, advice, and operations, everything is managed by AI. It is a treasure trove for startups to ignore.  Young organizations can build their company around AI or can exploit AI to the maximum, much more than the older orgs, and hence it is the perfect match.

5. Eliminate Scaling Risk

Hundreds of years after Alfred Chandler described the concepts of scaling, which were hitherto limited to labor and management with traditional IT infrastructure, we have reached a stage via AI, where scaling happens without the typical constraint of diminishing returns. The graph depicts the effect of digital operating models at scale. 


6. Incredible Value: Promising Areas of AI Applications

The area in which AI should be applied today baffles chief executives around the world, and it is even more significant for startups with tight budgets to identify these areas. Extensive research at McKinsey Global Institute reveals that around 40% of the value that analytics creates comes AI’s “deep learning” technique. 2/3rd of the entire AI opportunity lies in two areas:

• Supply-Chain Management/Manufacturing: $1.2 Trillion – $ 2 Trillion Potential Value Creation

For manufacturing the greatest value that can be created is in the area of predictive maintenance- $500-700 billion. AI’s capacity to crunch huge amounts of data, finding anomalies in audio and video to prevent breakdowns, be it aircraft or assembly line. 

AI based forecasting improves accuracy by 10-20%, which percolates into 5% reduction in inventory cost, and revenue increase by 2-3%. AI can optimize routing of delivery traffic, improving fuel efficiency and reducing delivery times. Companies have reduced as much as 15% fuel by optimization.

• Marketing and Sales: $ 1.4 Trillion – $ 2.6 Trillion Potential Value Creation

Customer service management and personalization areas are the most consequential. Speech recognition for call center management and call routing help deliver seamless service. Product recommendations are one of the most prominent areas of application. Also, Automate Voice, Email, Chat, and Social Media are also popular applications.

A lot of big providers, such as Salesforce, are beginning to offer ML predictions. You can get recommendations on related products, sales lead classification and warning you when a deal is going cold, finding alternate contracts, and even the best way to approach.  

Misconstrued Notions about AI and other Advanced Technologies

Here are a few points to be considered before entrepreneurs invest in AI and advanced tech.   The word on the street is that AI is yet to come of age, that it is highly oversold tech. This approach to AI is a bad idea and could jeopardize the initial success of startups if they do not ration for AI.  Here are a few questions that can help startups decide and strategize: 

• AI is yet not mature and thus highly risky and frugally rewarding

There are parts of AI like traditional ML, and even deep learning is old, and most importantly, their mathematical and statistical foundation is established. Hence the notion of maturity is not valid and correct. 

• The best time to adopt AI would be when competitors or leaders have shown success

This notion can be highly damaging as in the present hyper-competitive world, time is a critical asset. It takes a lot of time to develop and train AI systems as they need to be tailored to specific business needs; ML needs to be fed with large amounts of data, and for NLP it can be difficult to get the project up and running. Early experimentation is an ideal practice.  

• The Plug & Play approach with an experienced vendor will ensure success 

Even if a company has built the system, taking it from the pilot and prototyping stage to the actual production system is a highly challenging task. This would involve reengineering of business processes and also redefine the human tasks around it. If a company wants to augment customer engagement, then they need to create multiple AI applications for marketing, sales, and customer service. There is no plug-&-play when serious AI applications are concerned.  

• Advanced technology is applied for autonomous operations 

Today, AI is oriented towards augmenting human effort rather than working autonomously. New AI systems entail new human roles and retraining for new ways of working. And even if the new systems are designed to work autonomously, the phase of interaction learning is critical and unavoidable.  

• We can scale the AI Everest  

AI requires a broader governance approach as the efficacy of its algorithms decay over time. They are built over historical data, and recent changes require that algorithms be updated and monitored by subject matter experts to ensure that the machine correctly interprets and is free of bias. So AI is a journey and not a destination.   Though the incorporation of AI and other advanced tech is an uphill task, it is the only way startups can compete against tech giants, and is the only way they can continuously create competitive advantage. Begin early!  

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A deeper plunge into the world of AI

The areas which are being transformed by AI can be divided under three rubrics, Natural Language Processing, Machine Learning & Machine Perception.

• Natural Language Processing:
  Chatbots, personal/ legal/ virtual assistants are changing the way we communicate. According to a survey by Oracle, 80% of marketers have planned the use of chatbots in one form or the other to increase revenue by staying connected with their customers. BFSI sector already relies heavily on them to sort out customer queries. Irrespective of the industry a firm is in, its success depends on its ability to stay in touch with its customers & chatbots present the most efficient solution. According to a study by Forbes, the market of chatbots will reach USD 1.25 bn by 2025, an impressive number for a budding technology and highlights AI’s rapid ascent. Industries requiring constant customer interaction such as tourism, healthcare, education, etc. must incorporate chatbots in the near future, as there is none without them.
• Machine Learning
  ML is an application of AI which enables the system to learn and improve from experience without being programmed for it. When put to use, ML reduces the human effort and cost to a fraction. Prominent ML applications are:
  Medical Diagnostics: Programs such as Enlitic increase substantially a physicians diagnostic capabilities by analysing the patient’s medical past.
  Automotive: Data generated from cars is used in myriad ways such as in prediction of part life, preventive maintenance, as well as decisions involved in Autonomous vehicles.
  Agriculture: Even this sector is not left untouched; firms like John Deere have brought ML to agriculture by deploying it in robots who diagnose (in real-time) the disease afflicting crops or trees. GPS enabled accurate ploughing, sowing, irrigation etc. is transforming agriculture.
  AI Ensuring Peace: Facebook, Instagram, Twitter and others, use deep ML algorithms and neural networks to filter out content that can cause problems like communal riots, civil war etc. They also use it to better their user experience and raise advertisement revenue.
  The other prominent applications are Fraud detection, Cyber Security, Procurement Optimization, Bioinformatics and Emotional Detection. AI is also playing a crucial role in software testing, Know More.
• Machine Perception: For machines to become more intelligent and capable of doing vasts number of tasks, their data source must be varied as well as visual.
  AI finds application in Medical Imaging to identify diseases, in Manufacturing to apply robots safely in fabrication, pick and place as well as inspection applications.
  AI is changing hospitality too. Pepper, the humanoid robot of Softbank can discern facial expressions to identify emotions; it also understands languages, remembers faces and preferences. It is used to greet people in hotels, banks and offices, expect more of them in the future.
  Amazon, Dominos, etc. are using computer vision to operate drones for delivery. AI is not only shaping the digital world, it is transforming the physical as well.

  AI Advantages For Small Businesses

  AI should sound like a death knell for those firms who have no intention of incorporating AI into their business any time soon. The list of wonders AI can do to even a small business, is long; to mention a few:
  
  Marketing Edge: AI-enabled CRM tools help firms get sales pertinent data, helps better analyze customer feedback and fine-tune sales strategy in time, a stitch in time saves nine.
  Continuous Customer Engagement: Automated chatbots or digital assistants ensure that a firm is connected with its customers 24*7, and solve their queries effectively. This boosts customer engagement and helps business.
  Human Resource Leverage: AI-enabled recruitment tools expedite the entire process while concurrently increasing the probability of getting the right candidate.
  The Cost Advantage: From chatbots, CRM tools, to recruitment assisting software, AI can save cost for the firm in a big way. There is no need to train or recruit staff for interaction with customers in different time zones. The faster recruitment process saves time, and time is money. In every AI application, implicit is the operational and labour cost advantages.
  Quantification of the right time to switch and deploy AI can be debatable, but early movers do have the advantage.

  Embrace the Change

  The times are changing, and with it, the way business battles are fought. Those who stick to the old technology will perish soon, and those who switch to the aegis of Big Data & AI will have a higher chance of survival. Thus it is vital, even for small & medium enterprises, to early adopt to AI.But the transition to AI is not a day’s jump. Before firms think about using AI, they must first upgrade their digital infrastructure. “Digitalisation” is the mantra, and firms must digitalise most of their business. A well-endowed website with creative UI/UX and a corresponding, well-performing app can give companies a big boost. This has been Techment’s experience as well as advice; its customers have witnessed remarkable growth with its well-crafted software applications.

  Firms have to be prepared to embrace the new AI-enabled world and the most crucial step in its preparation is getting the present software infrastructure in good shape.

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