Tag: Generative AI

The State of Generative AI in the Retail and CPG Industry | Webinar

on-demand webinar

The State of Generative AI in the Retail and CPG Industry

In today’s competitive retail and CPG landscape, businesses face pressing challenges, from fluctuating consumer preferences to operational complexities. Generative AI (gen AI) presents a crucial solution, enabling rapid product innovation and enhanced operational efficiency. Adopting gen AI is now essential for companies aiming to stay ahead, and the conversation has shifted from “where can gen AI work” to “where is gen AI working?”

This webinar offered buyers and service providers valuable insights into real-world use cases where pilots progress to full production, key challenges, and the enterprise playbook around AI governance.

What questions has the webinar answered for the participants?

  • What are the use cases in the industry where gen AI adoption is moving from pilot to production?
  • What is the impact realized from the adoption of gen AI?
  • What are common challenges while scaling gen AI adoption?
  • What are the key business, technology, and sourcing implications of different regulations in North America and Europe around gen AI?

Who should attend?

  • CIOs, CTOs
  • IT/BPO strategy and department heads
  • Heads of outsourcing
  • Procurement managers
  • Global sourcing mangers
  • Vendor managers
  • Senior marketing executives
Partner
Mundra Abhishek 1
Practice Director

Agentic AI – Exploring its Enterprise Potential | Blog

The emergence of agentic AI represents the next frontier in AI-based automation, offering enterprises the opportunity to revolutionize work processes through increased autonomy, enhanced decision-making capabilities, and greater adaptability. But can it move from consumer applications to the enterprise environment? Read on to explore agentic AI’s potential to transform operations and the challenges of this rising technology.

Enterprises today increasingly prioritize generative AI (gen AI) to enhance individual knowledge and assist employees. However, newer models, such as agentic AI, are emerging as promising solutions that can make decisions and fulfill goals. While agentic AI shows considerable potential, can it be adapted from consumer use cases to enterprise workflows? Let’s explore this further.

What is agentic AI?

Agentic AI is a new evolution that enables end-to-end task execution through natural language-based inputs. It goes beyond retrieving information or increasing knowledge and can take action with minimal human oversight.

For example, gen AI can help employees write code for tasks. Agentic AI takes this to the next level by running, debugging, and executing the code to achieve the desired outcome. Ultimately, agentic AI promises to combine the reasoning, execution, and course correction mechanisms humans typically use to accomplish goals, as illustrated below:

Slide1

The early promise of agentic AI has been demonstrated in consumer use cases through practical tools such as Auto-GPT and innovative AI devices such as rabbit r1. These applications show that when pushed to its limit, agentic AI can significantly reduce the reliance on multiple applications/products for distinct workflows, achieving actions simply through chat interfaces. This offers a glimpse into agentic AI’s potential for transforming enterprise workflows.

Given the potential agentic AI has demonstrated in a business-to-consumer context, it is intriguing to consider how this technology could be applied in an enterprise setting. At the lower end of the realization spectrum, it could more effectively automate mundane or repetitive tasks with limited human supervision. On a larger scale, agentic AI could be leveraged to have fully autonomous agents that think through and achieve outcomes and goals communicated in natural language. Regardless of the promise eventually realized, agentic AI will likely boost employee productivity and provide greater end-to-end automation.

Agentic AI in the enterprise

Currently, agentic AI seems somewhat more elegant in a consumer use case. In an enterprise environment, automated dynamic workflow generation and execution pose risks for multiple reasons, including the cost of error (such as incorrect workflows) and enterprise technology complexity.

However, given the potential benefits, it is unsurprising that multiple tech vendors – from prominent tech providers to early-stage startups – are working on building platforms and solutions that enable agentic AI in the enterprise.

Despite workarounds and solutions that are being developed for these risks, agentic AI in an enterprise environment encounters the following obstacles:

  • Explainability: For critical processes in particular, the ability to explain why a specific workflow was generated to solve a problem is essential
  • Supervision: Fully automated execution of dynamically generated workflows may be problematic in many enterprise circumstances. Agentic AI will require human review for generating workflows and often for execution and handling exceptions
  • Ecosystem complexity: With the many products and solutions within the enterprise landscape, generating and executing workflows tailored to specific ecosystems will require specialized training and execution tools/interfaces
  • Governance and security: As is the case with any automation, agentic AI will also need robust governance, privacy, security, audit mechanisms, and other guardrails

What does this mean for existing automation tools?

In the near future, Agentic AI will likely augment existing automation tools, not replace them. Agentic AI can work with automation tools in certain situations to enable smoother execution. For instance, data and insights gained through task mining can be used to train and refine the agentic AI model for workflow generation. This model, embedded within a process orchestration tool, can then be used to build workflows that are subsequently executed using application programming interfaces (APIs), robotic process automation (RPA), intelligent document processing (IDP), and other automation tools.

What does the future hold?

Although using fully autonomous agents may appear to be a magical remedy to the software sprawl problem, it is hardly that simple. Even if users could generate and execute workflows instantaneously in an enterprise environment, mechanisms to trace actions, capture related data, and enable the right access levels to different user groups are necessary.

In the near future, it is improbable that agentic AI will threaten larger systems designed to address high-value enterprise use cases. Nonetheless, agentic AI will undoubtedly simplify and enable goal achievement and end-to-end execution, shortening the time to productivity for new users. As both core technology and guardrail mechanisms improve, multi-vendor point solutions with incremental benefits may become unnecessary due to agentic AI’s efficiency.

While still a nascent technology, agentic AI has the potential to be adopted more quickly in an enterprise environment due to its functions and benefits. As the technology and ecosystem rapidly advance over the next few years, we will monitor the progress and adoption. Follow this space for more insights, and contact Anil Vijayan at [email protected] to discuss further.

Watch the event, Distinguishing Gen AI Hype from Real Applications, to learn about practical applications shaping the future of intelligent technologies.

Everest Group Survey Reveals a Shift Towards Gen AI Outsourcing | In the News

More businesses are reportedly interested in exploring innovative strategies to enhance customer experience (CX) through generative artificial intelligence (gen AI) technology.

However, a recent survey by Everest Group, supported by TELUS International, revealed that most of these companies are looking to outsource their gen AI development due to concerns over data security, privacy, and regulatory compliance.

Read more in Outsource Accelerator.

Forecasting the Future: Key Trends Reshaping CXM Outsourcing in 2024 | Blog

After a turbulent past year, 2024 holds great promise for CXM outsourcing, with generative AI and other technologies poised to transform contact center operations. Discover five key trends that will impact the CXM industry going forward by reading on, or get in touch.

Amid the tumultuous landscape of 2022-23, the Customer Experience Management (CXM) outsourcing industry faced a barrage of challenges. Economic uncertainties, unfavorable exchange rates, and mounting financial pressures compelled many enterprises to tighten their belts, leading to reduced spending on CXM services. In the face of these adversities, the industry weathered a turbulent period, seeking resilience in the storm.

2024 has ushered in an exciting new chapter of transformation. Several technological advancements are poised to catalyze growth and reshape contact center operations. Generative AI (gen AI) holds the potential to redefine customer service offerings, automate more interactions, enhance agent performance, and provide superior customer experiences.

Advancements in accent neutralization and AI translators are expected to enhance service quality, boost workforce efficiency, and alleviate language barriers. The current surge in investments targeting various delivery geographies aims to unlock untapped talent pools. This trend is accompanied by a pressing need for agent reskilling to match the pace of evolving technologies.

Enterprises are reevaluating vendor management strategies, prioritizing providers with robust capabilities and talent, and focusing on embracing sustainability. This transformative shift is reshaping the CXM landscape and heralding a future defined by resilience and adaptability.

In this rapidly evolving environment, innovation isn’t just an option, but a prerequisite for enterprises and providers to prosper, making 2024 a pivotal year for the CXM industry.

Let’s deep dive into five key trends expected to reshape the CXM industry in the near future:

  1. Gen AI pilots to move to production: 2024 marks a pivotal moment in implementing gen AI pilots, heralding a transformative era in CX delivery. Gen AI is not merely a technological upgrade. It is a catalyst poised to elevate efficiency, redefine agent assistance capabilities, revolutionize voice bots, introduce cutting-edge self-service tools, and fundamentally reshape the overall service delivery landscape. Enterprises are proactively gearing up for the widespread deployment of gen AI across established use cases, strategically positioning themselves to harness its potential within the next two years.
  1. Accent neutralization and AI translation to redefine CX: Beyond the scope of gen AI, innovative technologies like accent-neutralization and AI translation are poised to revolutionize customer interactions. Accent-neutralization facilitates more transparent communication and promotes inclusivity, while AI translation bridges linguistic divides, enabling global interaction. Together, these advancements promise to enrich customer engagement, surpassing conventional limitations. They equip businesses to navigate language challenges, streamline processes, and cut costs significantly. These technologies simplify the complexities of multilingual support, boosting customer satisfaction and loyalty. Ultimately, they empower businesses to provide seamless, effective, and inclusive service worldwide.
  1. Evolving shoring dynamics: We anticipate a significant increase in offshoring and nearshoring activities continuing this year. This surge is propelled by factors such as escalating onshore talent costs, rapid technological advancements, and an increasing need for geographical diversification in response to the growing complexities of the business landscape. Emerging delivery locations in Africa (Ghana, Rwanda, Kenya, Senegal, Burkina Faso, and Morocco), Latin America (Suriname, Argentina, Nicaragua, Uruguay, and Caribbean countries), and Asia Pacific (Malaysia, Taiwan, Sri Lanka, and Indonesia) present compelling options for enterprises aiming to diversify their delivery capabilities and fortify their Business Continuity Planning (BCP) strategies.
  1. Strategic portfolio management: Enterprises will continue refining service provider portfolios through consolidation, rebalancing, or integrating new suppliers. This strategic portfolio management approach will be characterized by its sophistication, considering cost, risk, capabilities, and productivity factors. The overarching objective will be to achieve an optimal balance that enhances operational efficiency and positions enterprises to swiftly adapt to evolving market dynamics and shifting customer expectations. By strategically managing their portfolios, enterprises aim to gain a competitive edge by aligning their service provider relationships with their broader business objectives. This proactive approach will enable them to optimize resource allocation, mitigate risks, and capitalize on emerging opportunities.
  1. Embracing sustainability Initiatives: Sustainability initiatives are poised to play a pivotal role for enterprises and service providers. Enterprises will prioritize partnering with providers actively involved in such initiatives, aligning seamlessly with their organizational goals. By collaborating with these providers, businesses will demonstrate a commitment to ethical and socially responsible practices, contributing to the broader objectives of responsible and sustainable outsourcing.

Impact on the CXM Outsourcing Landscape

The CXM outsourcing landscape stands on the cusp of a transformative era, poised for a revolution fueled by the advent of technology and various strategies. These innovations promise remarkable productivity gains by automating more tasks and elevating customer and agent experience.

These trends signal a gradual departure from human-centric interventions to digitally-led customer experiences. As the industry moves towards a future shaped by AI and sustainability, customer service is expected to increase as a true differentiator between enterprises. The ability to personalize interactions, resolve issues quickly, and demonstrate empathy is expected to be key to success.

To gain more insights into the dynamic CXM outsourcing landscape, evolving customer requirements, and the significant impact of emerging technologies, explore our in-depth report, Strategic Keys: Unlocking the Potential of Customer Experience Management. For questions about the CXM outsourcing industry, contact [email protected] and [email protected]. You can also catch up on the latest insights with our webinar on demand, The Generative AI Advantage in Enterprise CXM Operations.

Breakdown: CX Leaders Spooked by Gen AI Data and Compliance Snafus | In the News

Decision makers in the CX space are almost entirely sold on generative AI (gen AI). Nevertheless, many can’t shake off the nightmare scenarios that the technology could bring in matters of data security and compliance.

Data security and compliance are the issues of most concern for CX leaders seeking to deploy gen AI in their organizations, according to a recent survey by Everest Group and TELUS International.

Read more in Nearshore Americas.

Navigating the Landscape: The Cost and Benefits of Generative AI Implementation | Blog

Generative AI (gen AI) can significantly benefit the BFSI industry. However, it can be an expensive investment, making it critical for enterprises to conduct a cost-benefit analysis before implementation. Explore the various costs and advantages associated with this technology in this blog, or get in touch to find out more. 

Gen AI has recently gained considerable attention in the banking, financial services, and insurance (BFSI) industry. Many use cases that go beyond creating or summarizing content are being explored throughout the value chain.

Implementing gen AI can improve the velocity of change, increasing the overall efficiency of existing tasks. This technology can streamline operational processes, automate tasks, and enhance customer experience by fostering engagement through tailored experiences. Moreover, it can potentially drive innovation to create change or transformation by generating unexplored ideas, optimizing products, and identifying new market opportunities. Ultimately, this positions enterprises for continuous evolution and success.

Navigating the Landscape The cost and benefits of Generative AI Implementation sf 1

BFSI enterprises have recognized the transformative potential of adopting gen AI, which undoubtedly can disrupt existing enterprise models. In the race to get the early advantage, enterprises face challenges as they reallocate funds from other projects and seek to secure new investments to finance new AI and gen AI initiatives.

Concurrently, cloud costs emerge as a significant concern that can potentially escalate when training AI models. However, the overall cloud cost impact from gen AI hinges on specific use cases and model architecture.

A cost-benefit analysis becomes imperative as gen AI-driven use cases are limited, and most can be explored through other AI technologies. This is particularly important because other relatively less expensive technologies can achieve comparable outcomes with similar efficiency.

While gen AI has generated a lot of hype and rapid investment, it’s not currently viable to implement the technology almost everywhere without understanding the cost implications for achieving the potential gen AI benefits. Let’s explore this further.

Exploring Cost and Generative AI Benefits

Navigating the Landscape The cost and 11of Generative AI Implementation sf 1

Below are some of the high-cost categories across the value chain to consider:

Infrastructure and compute

The computational backbone, encompassing graphic processing units (GPUs), tensor processing units (TPUs), and energy consumption, constitutes a substantial investment. Building and maintaining a powerful infrastructure is pivotal for running complex algorithms and training sophisticated models.

Model training or fine-tuning

Gen AI implementation comes with many fixed and variable costs. Training or fine-tuning gen AI models to meet specific requirements is intricate, involving significant computational resources, expert oversight, and time. These costs are substantial but also the foundation for the gen AI model’s efficacy and adaptability.

Data acquisition, preparation, and processing

Performance is heavily influenced by the data quality on which these models are trained. Collecting, cleaning, and storing data can come with high costs to acquire, prepare, and process diverse and high-quality datasets. Ensuring diverse and representative datasets while maintaining data quality standards can be challenging, ultimately impacting the accuracy and reliability of gen AI outputs. At the same time, acquiring high-quality data for training gen AI models and holistic data readiness initiatives can be expensive and require significant capital investments, especially if specialized or proprietary datasets are required.

Security measures

In a highly regulated industry like BFSI, where data and security are imperative, meticulous attention to security and regulatory compliance is critical. Implementing robust security measures cannot be compromised.

However, this adds costs for deploying cybersecurity measures, encryption protocols, and access controls to protect sensitive financial data, notwithstanding increased investments in security technologies, routine audits, and adherence to industry standards.

Considering that gen AI often relies on large datasets, managing personally identifiable information (PII) necessitates strict adherence to data privacy regulations.

Privacy-preserving techniques, anonymization processes, and implementing consent management systems to meet compliance requirements can be costly. On top of that, continuous monitoring and regular audits are essential to maintain compliance and security standards, contributing to ongoing operational expenses.

Integration and service 

Not all models run independently and often require integration with existing systems. Seamlessly integrating gen AI into existing workflows and providing continuous support have financial implications. The processes of customization, compatibility checks, and uninterrupted service provision collectively contribute to the overall expenditure.

Regulatory compliance

Operating within a highly regulated BFSI industry with standards such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and industry-specific regulations necessitates additional investments in compliance monitoring, data governance, and legal counsel.

Non-compliance with these regulations may lead to fines and legal consequences. As the regulatory laws for gen AI are still evolving, enterprises must be vigilant.

In light of the dynamic regulatory landscape, remaining flexible to accommodate incoming regulations is crucial.

Post-implementation

Following deployment, continuous monitoring and proactive maintenance of the systems are demanded to ensure gen AI’s sustained performance. Although this is an ongoing expense, these measures are pivotal for adaptability and longevity.

Talent related costs

Enterprises may incur expenses related to recruitment efforts, training programs, certification courses, and retention strategies to attract and retain top talent in the competitive gen AI landscape. As gen AI continues to evolve and play a pivotal role in digital transformation initiatives, businesses must carefully consider and budget for talent costs to ensure successful implementation and utilization of advanced AI technologies.

While investments in gen AI and related technologies are crucial, enterprises must also invest in their human capital by empowering employees with the skills and knowledge needed to thrive in today’s digital age. Effective leadership and a commitment to upskilling and reskilling will drive successful technology adoption and foster an organizational culture of innovation and agility.

The outlook for cost reduction efforts

While gen AI comes with a high cost, the landscape is evolving daily. Technology companies are substantially investing in developing proprietary AI chips and more efficient architectures, a strategic shift that aims to diminish reliance on expensive alternatives.

Enterprises can also explore a micro use case-led approach to implementing gen AI, deploying small, focused areas where gen AI can deliver clear and measurable benefits. Targeting smaller tasks allows for quicker development and deployment of gen AI solutions, leading to faster ROI (Return on Investment). Micro use cases provide opportunities to test and learn from gen AI implementations, enabling continuous improvement and informing future deployments. Smaller projects require less time and resources compared to developing a large, complex gen AI system.

Moreover, the gen AI domain is experiencing a notable training cost reduction, with some solutions claiming a remarkable 50% reduction. These advancements signal a significant stride toward enhancing AI’s capability and affordability, marking a pivotal turning point in the technology’s ongoing evolution.

While the costs associated with gen AI implementation are evident, the benefits in specific uses can significantly outweigh the expenses. Balancing financial considerations and the innovation potential is key. Enterprises must align their AI strategy with business objectives to position themselves at the forefront of innovation and competitiveness.

To discuss gen AI in BFSI, please reach out to [email protected], [email protected], and [email protected].

Looking for use cases for gen AI? Check out our LinkedIn Live on Distinguishing Gen AI Hype from Real Application, or read our latest research on generative AI and its adoption potential.

Indian IT Firms Face a Tough FY24 with All Round Pressure on Revenue & Margin | In the News

The Indian IT services industry has faced a tough financial year in FY24 with hope for a better fiscal year starting April 1. Tepid revenue growth rate, pressure on margin, low hiring, fall in headcount, disruption coming from generative AI, leadership changes at the top level, and sound deal pipeline characterized the ongoing financial year.

“Large deal pipelines are full, however, predicting the timing of their closing is becoming increasingly difficult,” Peter Bendor Samuel, CEO of Everest Group, told Bizz Buzz.

Read more in Bizz Buzz.

Cutting Through the Noise: Why Now is the Time to Invest in Next-gen Technologies | In the News

In the realm of boardroom conversations, buzzwords like generative AI (gen AI), data lakes, predictive analytics, and automation frequently surface. However, there’s a noticeable disparity between the prevalent talk on these topics and the actual scenarios business leaders face.

For example, the “chatter” might suggest that companies are already fully harnessing the potential of data lakes; however, according to the Global CFO Survey by Everest Group, investment in this technology is still in its infancy.

Read more in Nasdaq.

 

 

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