March 2025

Artificial Intelligence (AI) is reshaping the very fabric of modern business. From revolutionizing customer service to accelerating code modernization and automating workflows, AI-powered business with hybrid cloud—particularly generative AI—is unlocking new levels of productivity and innovation. But the true power of AI can only be harnessed when it is paired with the right infrastructure: a hybrid cloud strategy and a trusted technology architecture.

In this blog, we explore valuable insights from industry leaders to help you prepare for an AI-powered business with hybrid cloud. We’ll look at how organizations are adopting generative AI in business across key functions and why a hybrid cloud strategy is essential to scaling these capabilities confidently.

Generative AI: Redefining Possibilities in Business

Generative AI in business, including GPT and diffusion models, enables businesses to automate and create like never before. According to recent reports Spending on artificial intelligence (AI) in India is expected to grow at a compound annual growth rate of 31.5 per cent from 2023 and might reach $5.1 billion by 2027, according to a joint study released on Tuesday by Intel and International Data Corporation (IDC)., especially in high-impact areas such as:

1. Customer Service Transformation

AI-powered chatbots and virtual assistants are no longer just nice-to-haves. They’re becoming central to customer engagement strategies. Generative AI in business allows these assistants to understand context, sentiment, and intent—creating natural, human-like conversations. By automating common queries, guiding users through complex processes, and even predicting customer needs, AI can free up human agents to focus on higher-value tasks.

Insight: Leading companies are combining Generative AI in business with customer data to create hyper-personalised support experiences that drive satisfaction and loyalty. For example, by leveraging hybrid cloud strategy, organizations can securely access customer data wherever it resides, ensuring consistent and intelligent service delivery across all channels.

2. Code Modernisation and Developer Productivity

Generative AI in business is also helping developers by writing, debugging, and refactoring code. With tools like GitHub Copilot and IBM watsonx Code Assistant, businesses are significantly reducing development cycles and improving code quality.

Insight: Enterprises are integrating AI and cloud integration in business into DevOps pipelines, allowing for faster innovation without compromising security or governance. hybrid cloud strategy, enable this by supporting legacy systems while providing access to modern cloud-native environments, enabling a seamless transition toward modern architecture.

3. Workflow Automation for Operational Excellence

Routine business processes—from document processing to supply chain coordination—can now be fully automated using AI. AI-powered automation enhances this by interpreting unstructured data, generating summaries, and making intelligent decisions in real-time.

Insight: Businesses that adopt AI-powered automation for workflow automation report faster cycle times and reduced operational costs. With a hybrid cloud strategy, these automation solutions can be deployed consistently across on-prem and cloud environments, ensuring scalability and compliance in highly regulated industries.

Why Hybrid Cloud is Key to Scaling AI

While AI-powered automation provides the engine for transformation, the AI and cloud integration acts as the highway—allowing you to deliver these capabilities at scale, with flexibility, security, and control.

1. Data Gravity and Sovereignty

AI models thrive on data. But for many organisations, data is fragmented across environments. A hybrid-by-design cloud architecture allows you to keep sensitive data on-premises while using public cloud resources for AI training and inference—ensuring compliance with local regulations and reducing data movement costs.

Industry Insight: Financial institutions and healthcare providers are adopting this approach to ensure data sovereignty while still reaping the benefits of AI and cloud integration.

2. Optimised Resource Allocation

AI workloads require enormous computational power. AI and cloud integration lets you elastically scale infrastructure based on demand. You can train models on high-performance GPU clusters in the public cloud while running inference at the edge or on-prem, close to where data is generated.

3. Built-in Security and Trust

AI introduces new risks—bias, hallucinations, data leakage. A trusted hybrid cloud strategy embeds security, compliance, and governance into every layer. Platforms like IBM’s watsonx, combined with Red Hat OpenShift, allow organizations to manage and monitor AI models across environments, ensuring responsible and ethical AI usage.

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Building the Right Foundation for AI

To prepare for the AI-powered business with hybrid cloud, consider the following steps:

1. Establish a Clear AI Strategy

Align your AI initiatives with business goals. Start with high-impact use cases like customer support, code automation, and process optimization. Build cross-functional teams to ensure alignment between IT, data science, and business stakeholders.

2. Invest in a Hybrid Cloud Platform

Select a hybrid cloud strategy that provides flexibility to run workloads across multiple environments. Look for solutions that offer containerization, orchestration, and support for open standards to ensure portability and interoperability.

3. Modernise Your Data Architecture

AI is only as good as the data it learns from. Invest in data integration, governance, and quality tools. Use data fabric or data mesh approaches to make data discoverable, accessible, and trustworthy across your AI and cloud integration.

4. Prioritise Trust and Governance

Ensure that your AI systems are transparent, explainable, and accountable. Implement guardrails using AI governance frameworks to monitor model performance, detect bias, and manage risk across the lifecycle.

Looking Ahead: AI and Hybrid Cloud as the Innovation Engine 

The convergence of AI-powered automation and hybrid cloud strategy is ushering in a new era of intelligent enterprise. It’s enabling organizations to innovate faster, serve customers better, and optimize operations at scale. But to harness its full potential, businesses must build the right foundation—starting with a hybrid-by-design infrastructure and a trusted technology stack.

Whether you’re modernizing legacy systems, automating workflows, or delivering next-gen customer experiences, the path to success lies in a hybrid cloud strategy that brings AI and cloud integration closer to your data, your people, and your business goals.

In an era where innovation cycles are accelerating and client demands are ever-changing, the ability to pivot, create agile solutions, and deliver impactful results has become a crucial differentiator in the consulting and technology space.  A recent discussion led by Gregory Hodgkinson, accompanied by key players like Konrad Konarski, highlighted how Prolifics can outpace Tier 1 competitors by combining AI-powered prototyping with creativity and nimble teams. This approach enables them to set a vision early, gain deep client insights, and continuously pivot toward the highest-value outcomes—delivering results faster and more effectively than larger, slower firms.

Outpacing the Giants: The Need for Speed and Agility in Innovation

One critical point emerged during the conversation—large consulting firms often rely on lengthy, resource-heavy engagements to generate solutions. As Konarski mentioned, despite their reputation as industry leaders, these firms frequently turn to smaller, more agile organizations when they need cutting-edge innovation. This presents an opportunity for firms like Prolifics to redefine the narrative by accelerating the delivery of tailored, high-impact solutions.

Vision-Led Prototyping: The Key to Early Alignment and Rapid Innovation 

One of the key takeaways from Hodgkinson’s insights was the emphasis on the role of tools—specifically, AI-driven prototyping techniques that enable early vision-setting. More than just technical execution, these capabilities help teams visually articulate ideas, validate assumptions in real time, and keep solutions aligned with evolving client needs. This approach empowers Prolifics to pivot rapidly, ensuring that innovation stays focused on the highest-value opportunities. Specifically, this methodology enables teams to:

1. Quickly bring ideas to life, aligning solutions with real client challenges from the start.
2. Test, refine, and pivot in real time—iterating toward the highest-value outcome.
3. Give clients a tangible early vision of solutions, driving deeper engagement and alignment with business objectives.

Beyond Technology: Why Human Expertise is the True Differentiator 

Hodgkinson emphasized that despite technological advancements, the human element remains indispensable. He mentioned that even the most advanced tools cannot replace the creativity and expertise required to interpret client requirements and craft a compelling vision. This is where Prolifics’ team excels—translating client needs into actionable solutions that deliver measurable results.

Key Points of Differentiation:

1. Agility: Prolifics focus on quickly creating an initial impact by identifying high-value areas for clients. Rather than waiting for months-long engagements, they aim to deliver rapid prototypes and actionable strategies.
2. Vision Communication: The ability to effectively communicate a solution’s value is essential. Hodgkinson highlighted that vision-setting involves not just proposing what’s possible but demonstrating how the client can achieve specific outcomes.
3. Iterative Innovation: Prolifics’ approach involves constant refinement, allowing teams to pivot as client needs to evolve. This agility ensures that proposals remain relevant and impactful.

Winning Against Tier 1 Giants: A Strategy Fueled by AI and Agility

Konrad Konarski highlighted a key distinction between smaller, agile firms and large consulting behemoths. While Tier 1 firms have more resources, their scale often slows down their ability to innovate and pivot. By aligning tools with strategic vision, Prolifics can outpace competitors in key areas such as:

1. Vision-Led engagements: Using AI-powered rapid prototyping to bring concepts to life early, making complex solutions tangible, securing buy-in faster, and ensuring a clear path to value.
2. Industry-specific customization: Delivering tailored solutions that address unique challenges within verticals.
3. Collaborative problem-solving: Engaging clients as partners, not just customers, to co-create value.

The Road Ahead: Sustaining AI-Driven Innovation into 2025 and Beyond 

The session concluded with Hodgkinson’s call to action—keeping the innovation momentum alive throughout 2025. He reiterated the importance of leveraging both human and technological assets to maintain competitive advantage.  AI-powered tools will keep evolving, but real success lies in how organizations use them—to set a vision, adapt dynamically, and drive value faster than the competition. The firms that master this balance of AI-driven innovation and human expertise will define the future.

Winning the Innovation Race: AI, Vision, and Human Expertise

As Hodgkinson aptly put it: “The tool will help us, but it’s our creativity and expertise that will differentiate us.” This philosophy is at the core of Prolifics’ approach—understanding client needs, formulating compelling visions, and using AI-driven innovation to deliver results faster than larger, less nimble competitors. As organizations navigate an increasingly complex digital landscape, those that combine speed, agility, and human expertise will be the ones that lead the charge into the future.

Prolifics’ continued commitment to innovation—driven by a blend of human insight and technological enablement—sets a blueprint for success, ensuring they remain at the forefront of delivering transformative client outcomes.

Stay ahead of the curve—uncover how AI is reshaping industries with cutting-edge innovations and strategic insights. Don’t miss out on expert recommendations to fuel your transformation.

Turn your ideas into reality with AI-driven innovation. Discover how AI-powered prototyping accelerates product development and enhances decision-making.

The pharmaceutical industry stands on the edge of a major transformation—driven by Intelligent Automation in Pharma.
With growing complexities in drug discovery, regulatory compliance, and supply chain operations, pharma companies are turning to automation technologies to accelerate innovation, reduce costs, and enhance efficiency.

Intelligent Automation in Pharma combines artificial intelligence (AI) and robotic process automation (RPA) to revolutionize research, manufacturing, and patient care. By leveraging these technologies, organizations can streamline operations, improve accuracy, and make data-driven decisions—leading to faster drug development and safer outcomes.

Intelligent Automation Technologies for Pharma

Pharmaceutical companies are integrating a diverse set of Intelligent Automation in Pharma tools to enhance operational efficiency and drive innovation. Below are key technologies that are transforming the industry:

1. Robotic Process Automation (RPA)

Pharma RPA automates repetitive, rule-based tasks such as data entry, clinical trial management, and compliance reporting.
Key benefits:

• Frees teams to focus on strategic work
• Improves accuracy and reduces errors
• Eliminates manual effort

2. Low-Code/No-Code Platforms

Low-code and no-code platforms empower non-technical users to build and deploy automation solutions with minimal programming.
These platforms help pharma organizations:

• Automate workflows and research processes
• Build custom applications quickly
• Reduce dependency on IT teams
  This approach accelerates Pharma Digital Transformation across departments.

3. Intelligent Document Processing (IDP)

Pharma companies handle massive volumes of structured and unstructured data—such as research reports, filings, and patient records.
Intelligent Document Processing (IDP) uses AI, machine learning (ML), and natural language processing (NLP) to extract and process this information efficiently.
Benefits include:

• Real-time data accuracy and insights
• Faster regulatory approvals
• Enhanced compliance and decision-making

4. Virtual Assistants

Virtual assistants are redefining patient engagement and internal operations. They:

• Respond to patient queries
• Support clinical trials
• Share real-time updates with teams

By improving collaboration and accessibility, virtual assistants play a vital role in Pharma Digital Transformation.

5. Process Mining/Task Mining

These technologies analyze process data to uncover inefficiencies and automation opportunities.
Outcomes:

• Improved compliance
• Enhanced productivity
• Continuous process improvement

Together, they help pharma organizations operate smarter and more efficiently.

How to Maximise Value with Intelligent Automation 

The integration of Intelligent Automation in Pharma is delivering significant benefits. Here’s how pharma companies are maximizing value:

1. Accelerating Drug Discovery and Development

By leveraging AI in Pharma and automation, pharma companies can analyze vast datasets, identify potential drug candidates faster, and streamline clinical trials. AI-driven predictive modeling accelerates the Drug Discovery Automation process, reducing the time and cost associated with R&D.

2. Enhancing Regulatory Compliance

Regulatory compliance is a critical aspect of the pharma industry. Pharma Compliance Automation ensures adherence to stringent regulations by automating documentation, reporting, and audit processes.

Automated compliance solutions reduce human errors and ensure real-time tracking of regulatory changes, minimizing risks associated with non-compliance.

3. Optimizing Supply Chain Operations

Pharma supply chains are complex, involving multiple stakeholders, from manufacturers to distributors and healthcare providers. Intelligent Automation in Pharma optimizes supply chain operations by predicting demand fluctuations, automating inventory management, and ensuring real-time tracking of pharmaceutical products.

This enhances efficiency and reduces costs associated with stockouts and overproduction.

4. Improving Patient Experience

Patient-centricity is becoming a key focus for pharmaceutical companies. IA-driven solutions, such as AI-powered chatbots and personalized treatment recommendations, enhance patient engagement and adherence to medication plans. AI in Pharma also facilitates telehealth services, enabling remote patient monitoring and improving healthcare accessibility.

5. Reducing Operational Costs

By automating repetitive tasks and optimizing workflows, pharma companies can significantly reduce operational costs. Pharma RPA enhances efficiency in administrative processes, allowing organizations to allocate resources to high-value initiatives such as drug innovation and personalized medicine.

GenAI and IA: The Future of Pharma Innovation

The convergence of Generative AI (GenAI) and intelligent automation is set to redefine pharma innovation. GenAI, which leverages deep learning and NLP, can generate new molecular structures, predict drug interactions, and optimize clinical trial designs. When combined with IA, it creates a powerful ecosystem for accelerated Drug Discovery Automation and personalized medicine.

AI-Powered Drug Discovery

GenAI is revolutionizing Drug Discovery Automation by predicting molecular properties and designing new drug candidates.

This reduces the time and cost associated with traditional trial-and-error methods, leading to faster development of breakthrough therapies.

AI-Driven Personalized Medicine

By analyzing patient data, GenAI can generate personalized treatment plans based on genetic profiles, lifestyle, and medical history. This ensures more effective and targeted therapies, improving patient outcomes and reducing adverse reactions.

Automated Clinical Trials

Pharma RPA is streamlining clinical trials by automating patient recruitment, data collection, and analysis. AI-driven simulations help in designing optimal trial protocols, increasing the chances of successful drug approvals.

Predictive Pharmacovigilance

Ensuring drug safety is paramount in the pharmaceutical industry. GenAI and IA enable predictive pharmacovigilance by analyzing vast datasets to detect potential adverse drug reactions (ADRs) early. This proactive approach minimizes risks and enhances patient safety.

Smart Manufacturing

AI-driven manufacturing enables:

• Predictive maintenance
• Automated quality control
• Real-time production monitoring

These improvements enhance consistency, reduce costs, and ensure compliance across the production lifecycle.

Conclusion 

Intelligent Automation in Pharma is revolutionizing how pharmaceutical companies innovate, operate, and deliver patient care.
By leveraging technologies like Pharma RPA, low-code platforms, IDP, virtual assistants, and process mining, organizations can optimize efficiency, accelerate research, and enhance compliance.

The rise of Generative AI further amplifies these benefits—driving breakthroughs in drug discovery, personalized medicine, and smart manufacturing.

As the industry evolves, pharma leaders embracing AI in Pharma and Intelligent Automation will unlock new opportunities for innovation, regulatory excellence, and sustainable growth—ultimately delivering safer and more effective treatments worldwide.

Gen AI in Retail is revolutionizing the retail industry in ways we never imagined. As sustainability, technological advancements, and shifting consumer expectations reshape the market, retailers must stay ahead of the curve to avoid disruption.

Today’s consumers wield more influence than ever, demanding transparency, personalization, and even the co-creation of products and services. However, while customer empowerment is key, brands must strike a balance—ensuring engagement without losing control of their narrative. By focusing on the core pillars of empathy—seeing, hearing, thinking, feeling, saying, and doing—retailers can craft meaningful and highly personalized shopping experiences.

At the forefront of this transformation, Gen AI in Retail enables global retailers to tailor experiences individually, redefining customer engagement and driving innovation across the retail and consumer goods sectors.

IDC FutureScape: Worldwide Retail 2025 Predictions 

Recent study by the IDC FutureScape: Worldwide Retail 2025 Predictions highlights the increasing role of Gen AI in Retail:

1. Prediction 1: By 2026, 90% of retail tools will embed AI algorithms, with over 30% of these algorithms using standalone AI or modular, agnostic AI models that can be swapped out for suitable retail-specific models.  

1. Prediction 2: By 2028, 30% of retailers will drive 10% IT costs out of stores by utilizing distributed edge with onboard AI chips, reducing latency and improving IT operations performance.  

1. Prediction 3: By 2026, 70% of retailers will implement AI-driven loyalty apps, improving contextualized offers by 40% and boosting customer engagement to drive up to a 25% increase in customer retention rates.  

1. Prediction 4: By 2028, secondary inventory management benefits realized from technology adopted to be compliant with regulations will spur a 30% increase in supply chain adoption of track and trace capabilities.  

1. Prediction 5: By 2025, major retailers will boost IT spending by 10%+ to enhance cybersecurity to better battle the rising tide of new, more sophisticated data breaches and other cyberthreats retailers now face.  

1. Prediction 6: By 2027, 40% of retailers will leverage Gen AI in Retail for content creation and delivery for dynamic product content, boosting conversion rates and cutting content management costs by 30%.  

1. Prediction 7: By 2026, 50% of retailers will have reduced workforce costs by 2% with embedded Gen AI in Retail in fundamental business processes and tasks across business operations at corporate and in stores.  

1. Prediction 8: By 2027, over 45% of major retailers will apply hyper-localization for store-specific assortment planning, selection, allocation, and replenishment that leverages AI and data-driven decision-making.  

1. Prediction 9: By 2029, grocery retailers will harness quality data at scale from interoperable unified supply networks for AI-based decision intelligence to see a 6% increase in profits and 18% reduction in waste.  
2. Prediction 10: By 2026, 70% of major retailers will invest in loss prevention tech applying AI to integrated data sets with multiple sources (RFID, camera, traffic, and inventory data) to reduce in-store theft by 25%. 

Common Gen AI in Retail Use Cases

1. Product Development
AI accelerates product ideation and design by analyzing market trends, consumer preferences, and competitor insights. Gen AI in Retail enables brands to create virtual prototypes, predict product success, and tailor offerings to evolving demands.

2. Inventory Management
Retailers can leverage AI-driven forecasting to optimize stock levels, prevent overstocking or shortages, and reduce operational costs. Automated insights help businesses maintain the right balance between supply and demand.

3. Customer Service
Chatbots and AI-driven virtual assistants provide 24/7 support, reducing response times and enhancing customer satisfaction. Gen AI in Retail can handle inquiries, process returns, and personalize interactions based on past behavior.

4. Price Optimization
Retailers can use AI-powered pricing models to analyze competitor pricing, consumer demand, and external factors like economic shifts, ensuring dynamic and competitive pricing strategies.

5. Visual Merchandising
Gen AI in Retail tools create optimized store layouts and digital experiences, predicting which displays and product placements will drive the most engagement and conversions.

6. Content Creation
Retailers can leverage AI-generated content, from product descriptions to social media ads, ensuring consistent, high-quality messaging while saving time and resources.

7. Personalized Marketing
AI-driven personalization tailors promotions, recommendations, and campaigns to individual consumer behaviors, increasing engagement and driving conversions.

8. Market Demand and Forecasting
By analyzing vast data sets, Gen AI predicts consumer trends, seasonal fluctuations, and market shifts, allowing retailers to make data-driven business decisions.

How Gen AI is Changing Retail and Consumer Goods

Speeding Product Development
By automating ideation, prototyping, and testing, retailers can bring products to market faster. AI-driven insights eliminate guesswork and refine offerings before launch.

Creating High-Value Experiences
From AI-powered styling assistants to immersive virtual shopping experiences, retailers are using Gen AI to deliver unique, value-driven consumer interactions.

Optimizing Supply Chain Operations
AI streamlines logistics by predicting disruptions, automating procurement, and ensuring optimal warehouse management, resulting in a more resilient supply chain.

Automating Procurement
Retailers can leverage AI-driven procurement solutions to optimize sourcing, reduce costs, and ensure the timely acquisition of high-demand products.

Redefining Customer Service
Beyond chatbots, AI enables sentiment analysis, predictive customer support, and hyper-personalized recommendations, enhancing customer loyalty and retention.

Best Practices for Gen AI in Retail

Engage and Inform Employees – AI adoption isn’t just about technology—it’s about people. Training employees to work alongside AI fosters collaboration and maximizes efficiency.

Innovate, Don’t Stagnate – Retailers must continuously experiment with AI-driven solutions to stay ahead of the competition. Embracing innovation ensures long-term relevance.

Build Confidence with Transparency – Consumers value ethical AI. Retailers should be transparent about AI-driven personalization and decision-making, ensuring trust and compliance.

Meet the Needs of AI-Enabled Consumers – Today’s shoppers expect hyper-personalized, seamless experiences. Retailers must align AI strategies with evolving consumer expectations to drive loyalty and engagement.

Conclusion 

Gen AI is revolutionizing retail, offering unprecedented opportunities for innovation, efficiency, and customer engagement. Retailers that embrace AI-driven transformation will not only stay competitive but thrive in an increasingly digital world. By leveraging AI for product development, personalized marketing, supply chain optimization, and customer service, businesses can create a future-ready retail ecosystem that meets the needs of modern consumers. 

Transform customer experiences, optimize supply chains, and drive sales with AI-powered automation. Discover how Prolifics helps retailers harness Generative AI for smarter decision-making and personalized engagement.