The Risks of Using Chinese DeepSeek AI in Indian Government Offices: A Data Security Perspective

Introduction

Artificial Intelligence is transforming governance, enhancing efficiency, and automating decision-making. However, when deploying AI solutions, especially from foreign entities, national security and data privacy must be top priorities. The recent rise of Chinese AI models, such as #DeepSeek, raises significant concerns if deployed within Indian government offices.

 

Understanding DeepSeek AI

#DeepSeek AI, developed by Chinese firms, is an advanced generative AI model comparable to OpenAI's ChatGPT or Google Gemini. While it offers powerful language processing, the core issue is data sovereignty—who owns, accesses, and controls the data that flows through these systems.

Key Data Leak Concerns

1. Data Storage and Transmission Risks

Many AI models rely on cloud-based processing, meaning data entered into #DeepSeek AI might be stored on servers outside India. If hosted in China, it could fall under Chinese Cybersecurity Laws, which mandate that all data stored on Chinese servers be accessible to their government. This creates a high risk of unauthorized access to sensitive Indian government data.

2. AI Model Training and Retention of Sensitive Information

DeepSeek AI, like other generative AI models, continuously improves by learning from user inputs. If government officials unknowingly enter classified information, the model could retain and use this data in future responses. This creates a leakage pathway for confidential communications, defense strategies, and policy decisions.

3. Potential for AI-Based Espionage

China has been accused of using AI-driven data collection to support cyber espionage. If DeepSeek AI is embedded into Indian government operations, it could potentially be leveraged to:

 

Monitor government discussions

Analyze sensitive trends in policymaking

Extract metadata about officials, agencies, and strategies

Such risks make it untenable for a foreign AI system, especially from a geopolitical rival, to be integrated into government workflows.

Real-World Example: How a Data Leak Could Happen

Scenario: A Government Employee Uses DeepSeek AI to Draft a Report

Imagine an officer in the Ministry of Defence (MoD) is tasked with preparing a classified report on India's border security strategies in Arunachal Pradesh. To speed up the process, they enter sensitive details into DeepSeek AI, asking it to refine and format the document.

What Happens Next?

1. Data Sent to Foreign Servers:

DeepSeek AI processes the request on its servers, which may be located in China or other foreign jurisdictions. The model may store or analyze this sensitive input for further training.

2. Hidden Data Trails in PDF Files:

The AI-generated report is downloaded as a PDF and shared internally within the ministry. However, AI-generated PDFs often contain metadata, such as input prompts, IP addresses, timestamps, and even hidden AI-generated summaries of user interactions. If a cyberattack targets the ministry, these documents could reveal what was asked from the AI, including confidential border troop movements, defense procurement plans, and diplomatic strategies.

3. Potential Cyber Espionage via AI Logs:

If DeepSeek retains logs of AI interactions, Chinese intelligence agencies could access fragments of sensitive information that were input by multiple Indian government users. Over time, even seemingly harmless prompts could help adversaries piece together critical insights about India's defense and economic policies.

Another Example: Finance Ministry & Budget Leaks

A Finance Ministry officer drafts an early version of India's Union Budget using DeepSeek AI to refine tax policy announcements.  The AI processes tax adjustments, subsidies, and proposed infrastructure allocations. If this data is retained or intercepted, it could provide foreign entities an unfair advantage in financial markets, potentially leading to stock market manipulation before the budget is officially announced.

4. Compliance with Indian Data Protection Laws

India's Digital Personal Data Protection Act (DPDP), 2023, mandates strict controls over cross-border data transfers. If DeepSeek AI processes government data outside India, it could violate these regulations, leading to legal repercussions and national security concerns.

Government Action Needed

1. Ban on Foreign AI in Sensitive Departments

India should restrict foreign AI tools from being used in government offices, especially in defense, law enforcement, and strategic sectors.

2. Development of Indigenous AI

Instead of relying on Chinese AI, India should focus on strengthening its own AI ecosystem through initiatives like Bhashini, IndiaAI, and partnerships with Indian tech firms.

3. Security Audits and Whitelisting of AI Tools

The government must enforce strict AI security audits and only approve AI models that meet data sovereignty and privacy standards.

Conclusion

While AI can revolutionize governance, national security should never be compromised. Allowing Chinese DeepSeek AI into Indian government offices could create serious data leak vulnerabilities. India must take a proactive stance by investing in indigenous AI solutions and enforcing stringent data security measures to safeguard its digital future.

The Impact of Data Quality on AI Output

 

The Influence of Data on AI: A Student's Social Circle

Imagine a student who spends most of their time with well-mannered, knowledgeable, and
disciplined friends. They discuss meaningful topics, share insightful ideas, and encourage each
other to learn and grow. Over time, this student absorbs their habits, refines their thinking, and
becomes articulate, wise, and well-informed.
Now, compare this with a student who hangs out with spoiled, irresponsible friends who engage in
gossip, misinformation, and reckless behavior. This student is constantly exposed to bad habits,
incorrect facts, and unstructured thinking. Eventually, their ability to reason, communicate, and make
informed decisions deteriorates.

How This Relates to Large Language Models (LLMs)

LLMs are like students-they learn from the data they are trained on.
- High-quality data (cultured friends): If an LLM is trained on well-curated, factual, and diverse data,
it develops a strong ability to generate accurate, coherent, and helpful responses.
- Low-quality data (spoiled friends): If an LLM is trained on misleading, biased, or low-quality data,
its output becomes unreliable, incorrect, and possibly harmful.

Key Aspects of Data Quality and Their Impact on AI Output

1. Accuracy - Incorrect data leads to hallucinations, misinformation, and unreliable AI responses.
2. Completeness - Missing data causes AI to generate incomplete or one-sided answers.
3. Consistency - Inconsistent data results in contradicting outputs, reducing AI reliability.
4. Bias and Fairness - Biased data reinforces stereotypes, leading to unethical and discriminatory AI
responses.
5. Relevance - Outdated or irrelevant data weakens AI's ability to provide timely and useful insights.
6. Diversity - Lack of diverse training data limits AI's ability to understand multiple perspectives and
contexts.
7. Security and Privacy - Poorly sourced data may contain sensitive information, leading to ethical
and legal concerns.

 

Conclusion: Garbage In, Garbage Out

Just as a student's intellectual and moral development depends on their environment, an AI model's
performance depends on the quality of the data it learns from. The better the data, the more
trustworthy and effective the AI becomes. Ensuring high-quality data in AI training is essential to
creating responsible and beneficial AI systems.

Understanding Large Language Models (LLMs) - Ajay

 Overview

There is a new discussion on India developing its own Large Language Models (LLMs) and some politician even planned to deploy #DeepSeek in India to be used by government offices. I have received many  have revolutionized artificial intelligence, enabling machines to
understand, generate, and interact with human language in a way that was once thought impossible. These models power applications like chatbots, translation services, content generation, and more. But what exactly are LLMs, and
how do they work?

What Are Large Language Models?

LLMs are deep learning models trained on vast amounts of text data. They use neural
networks-specifically, transformer architectures-to process and generate human-like text. Some
well-known LLMs include OpenAI's GPT series, Google's BERT, and Meta's LLaMA.
### Key Features of LLMs:
- **Massive Training Data**: These models are trained on billions of words from books, articles, and
web content.
- **Deep Neural Networks**: They use multi-layered neural networks to learn language patterns.
- **Self-Attention Mechanism**: Transformers allow models to focus on different parts of the input to
generate contextually relevant responses.

How LLMs Work

1. Training Phase
During training, LLMs ingest large datasets, learning patterns, grammar, context, and even factual
information. This phase involves:
- **Tokenization**: Breaking text into smaller pieces (tokens) to process efficiently.
- **Embedding**: Converting words into numerical representations.
- **Training on GPUs/TPUs**: Using massive computational resources to adjust millions (or billions)
of parameters.
2. Fine-Tuning and Reinforcement Learning
Once pre-trained, LLMs undergo fine-tuning to specialize in specific tasks (e.g., medical chatbots,
legal document summarization). Reinforcement learning with human feedback (RLHF) further
refines responses to be more useful and ethical.
3. Inference (Generation Phase)
When you input a query, the model predicts the most likely next words based on probability, crafting
coherent and relevant responses.

Hands-On Exercise: Understanding Model Output

**Task:**
- Input a simple sentence into an LLM-powered chatbot (e.g., "What is the capital of France?").
- Observe and analyze the response. Identify patterns in the generated text.
- Modify your input slightly and compare results.

Applications of LLMs

LLMs are widely used in various industries:
- **Chatbots & Virtual Assistants**: AI-powered assistants like ChatGPT enhance customer support
and productivity.
- **Content Generation**: Automated article writing, marketing copy, and creative storytelling.
- **Translation & Summarization**: Converting text across languages or condensing information.
- **Programming Assistance**: Code suggestions and bug detection in development tools.

Case Study: AI in Healthcare

**Example:** Researchers have fine-tuned LLMs to assist doctors by summarizing patient histories
and recommending treatments based on medical literature. This reduces paperwork and allows
doctors to focus more on patient care.

Challenges and Ethical Concerns

Despite their potential, LLMs face challenges:
- **Bias & Misinformation**: Trained on human-generated data, they can inherit biases or generate
incorrect information.
- **Computational Costs**: Training LLMs requires expensive hardware and immense energy
consumption.
- **Security Risks**: Misuse of AI-generated content for misinformation or unethical applications.
## Best Practices for Using LLMs
- **Verify Information**: Always fact-check AI-generated content before using it.
- **Monitor Ethical Usage**: Be mindful of potential biases and adjust model outputs accordingly.
- **Optimize Performance**: Fine-tune models for specific tasks to improve accuracy and reduce
errors.

 Future of Large Language Models

Research continues to improve LLMs by enhancing their efficiency, reducing bias, and making them
more transparent. As AI advances, these models will become more integral to various domains,
from education to healthcare and beyond.

Group Discussion: The Role of AI in the Future

**Question:**
- How do you see LLMs shaping different industries in the next 5-10 years?
- What ethical safeguards should be in place to ensure responsible AI use?

Conclusion

Large Language Models represent a significant leap in AI capabilities. Understanding their
strengths, limitations, and ethical implications is crucial for leveraging their potential responsibly. As
technology progresses, LLMs will continue to shape the future of human-computer interaction.

Prompt Engineering for Lawyers: My Comprehensive Guide for Lawyers at Accenture

In today's digital-first legal environment, lawyers are increasingly turning to AI to automate research, drafting, summarization, and even litigation preparation. While tools like ChatGPT can be powerful, they require well-structured prompts to deliver optimal results. This guide introduces lawyers to the art of prompt engineering—how to write effective queries for AI tools to enhance legal work without compromising on quality or ethics.

Prompt engineering is your bridge between legal knowledge and AI capability. If you're new to AI or want to extract better, more accurate results from legal tech tools, mastering prompt engineering is essential.

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2. What is Prompt Engineering?

Prompt engineering is the process of crafting precise and intentional instructions for an AI language model to get a desired outcome. Think of it as briefing a junior associate—you need to be clear, concise, and detailed. The better the input, the better the output.

Example

• Poor Prompt: "Summarize this case."

• Better Prompt: "Summarize the key legal issues, holding, and reasoning in the Supreme Court case 'Dobbs v. Jackson Women’s Health Organization (2022).'"

---

3. Why Prompt Engineering Matters in Law

Legal work is nuanced, rule-bound, and jurisdiction-specific. Without precision, AI tools can misinterpret legal concepts, miss key issues, or generate misleading content.

Effective prompt engineering helps ensure:

• Greater accuracy in case law interpretation

• Stronger contract drafting and compliance

• Better client communication and clarity

• Reliable legal research output

---

4. Principles of Effective Prompting

A. Be Specific

Avoid generalities. Detail the legal issue, jurisdiction, audience, and intended output.

B. Give Context

Specify statutes, case names, or factual scenarios to frame the prompt.

C. Define Output Format

Clarify if you want a bullet list, memo, contract clause, table, etc.

D. Use Step-by-Step Reasoning

Ask the model to walk through logic like a legal analysis.

---

5. Types of Legal Prompts

Prompt Type	Use Case
Case Summarization	Research, memos
Contract Drafting	Transactional work
Legal Research	Trial prep, advice
Compliance Review	In-house risk mitigation
Client Emails	Clear communication
Legal Argumentation	Brief writing, court prep
Legal Training	Associates, students
Due Diligence	M&A, discovery
Risk Assessment	General counsel work
Jurisdictional Comparison	Multistate practices

---

6. 10 Practical Prompt Examples for Lawyers

1. Case Law Summarization

Prompt: "Summarize the key facts, legal issues, holding, and reasoning of the case 'Marbury v. Madison (1803)' in under 300 words for a constitutional law memo."

---

2. Drafting a Clause

Prompt: "Draft a non-compete clause for a Delaware-based employment contract for a software engineer, enforceable for 12 months in the U.S."

---

3. Legal Research Support

Prompt: "List three leading cases in New York that define the duty of care in premises liability lawsuits involving commercial landlords. Summarize each in 100 words."

---

4. Compliance Analysis

Prompt: "Evaluate whether a GDPR-compliant privacy policy must include provisions related to automated decision-making and profiling. Include regulation citations."

---

5. Client Communication Draft

Prompt: "Write a professional, easy-to-understand email explaining to a client why their LLC operating agreement should include dispute resolution provisions. Limit to 300 words."

---

6. Summarize a Contract

Prompt: "Summarize the rights, obligations, and termination clauses in this SaaS agreement in bullet points." (Insert text)

---

7. Legal Argument Drafting

Prompt: "Write an opening argument for the defense in a breach of verbal contract case for software delivery. The defense argues no meeting of minds occurred."

---

8. Legal Education

Prompt: "Explain the difference between 'res judicata' and 'collateral estoppel' with examples suitable for a first-year law student."

---

9. Risk Assessment

Prompt: "Assess the legal risks of third-party API integrations in a fintech app operating in California. Focus on consumer privacy and liability."

---

10. Jurisdictional Comparison

Prompt: "Compare the enforceability of e-signatures in real estate contracts in California and New York. Use a table format with citations."

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7. Common Mistakes and How to Avoid Them

Mistake	Problem	Solution
Vague Prompt	Unfocused answers	Specify facts, goals, jurisdiction
No Output Format	Hard to read results	Ask for structure (bullets, table)
Ignored Audience	Wrong tone or detail	Define audience: client, judge, etc.
Blind Trust	AI may hallucinate	Always verify legal content

---

8. Tools and Techniques to Improve Prompt Outcomes

Chain-of-Thought Prompting

Ask the AI to reason step-by-step.

"Evaluate each element of negligence: duty, breach, causation, damages. Apply to the facts provided."

Few-shot Prompting

Show examples to teach the model.

Multi-turn Prompting

Break a complex task into steps.

Self-Critique Prompting

Ask the AI to review or improve its own answer.

"Review your response for clarity and missing legal elements."

Templates

Develop reusable formats for:

• Legal memos

• Risk assessments

• Clause libraries

---

9. Ethics and AI Usage in Legal Practice

AI tools should supplement—not replace—your legal judgment. Key ethical considerations include:

• Confidentiality: Never share client-identifying data

• Accuracy: AI can fabricate case law—verify everything

• Disclosure: Consider informing clients of AI assistance

• No Unauthorized Practice: Don’t allow AI to create legal advice in jurisdictions where you’re not licensed

⚖️ ABA Rule 1.1 (Competence) now includes understanding of relevant technology

---

10. Final Takeaways

Prompt engineering is becoming a vital legal skill. Like legal writing or oral argument, it can be mastered through clear thinking, practice, and precision. Use this guide as your starting point to navigate the world of legal AI with confidence.

Nuisances of mobile architecture for integration architects

Architecting a mobile application involves defining the structure and design of the app, including the technology stack, architecture patterns, and how different parts of the app will interact. It ensures the app is scalable, maintainable, and performs efficiently.

Let's quickly breakdown of the key steps:

1. Understanding Requirements and Scope:

• Define the app's purpose and target audience: 
• What problem does the app solve
• Who is the intended user as is common with any other application development.
• Outlining functional and non-functional requirements: 
• Consider features, performance expectations, security needs and any specific platform requirements (Android, iOS, etc.)
• Determine the app's complexity: 
• Is it a basic and simple utility application or a feature-rich application

2. Choosing the Right Architecture Pattern:

• Consider different architectural patterns: Common choices include 
• MVC (Model-View-Controller), 
• MVP (Model-View-Presenter), 
• MVVM (Model-View-ViewModel) &
• Clean Architecture
• Evaluate each pattern's strengths and weaknesses: 
• Consider factors like testability, maintainability, scalability, and ease of implementation.
• Choose the pattern that best suits the app's requirements: 
• For instance, MVVM is often preferred for complex apps with frequent UI updates, while MVP is suitable for simpler applications.
• Examples:
  • MVC: A classic pattern that separates the application's data (Model), user interface (View), and interaction logic (Controller)
  • MVP: Provides a clearer separation of concerns by introducing a Presenter layer that manages communication between the View and Model
  • MVVM: Enables data binding and reactive programming, making it easier to update the UI in response to data changes
  • Clean Architecture: Focuses on separating the core business logic from the UI and other external dependencies, promoting testability and maintainability.

3. Selecting the Technology Stack:

• Choose the right programming language: Consider factors like platform compatibility, development speed, and performance requirements.
• Select appropriate frameworks and libraries: Frameworks like React Native or Flutter enable cross-platform development, while libraries provide specialized functionalities.
• Consider backend services and data storage: Choose the appropriate database and API for storing and managing data.

4. Designing the Application Layers:

• Data Layer: Handles data access and persistence, including database interactions, API calls, and data storage.
• Business Layer: Contains the core logic of the application, such as calculations, validations, and business rules.
• Presentation Layer: Responsible for the user interface, including views, widgets, and UI elements. 

5. Implementing and Testing:

• Develop the application based on the chosen architecture and technology stack: Follow best practices for code quality, documentation, and version control.
• Test the application thoroughly: Conduct unit tests, integration tests, and user acceptance tests to ensure functionality and performance.
• Optimize for performance and scalability: Consider techniques like caching, lazy loading, and asynchronous operations to improve app speed and responsiveness. 

6. Maintaining and Updating:

• Establish clear guidelines for code maintenance and updates: Ensure that the app can be easily modified and improved over time.
• Follow best practices for code documentation and version control: This helps maintain a clean and organized codebase.
• Plan for future enhancements and features: Consider how the architecture can be adapted to accommodate new requirements and features.

Prompt Engineering in Artificial Intellegence

AI prompt engineering has taken center stage in many industries since 2022. The reason is that businesses have been able to garner better results with AI using prompt engineering techniques. With the right prompt engineering strategy, the results of all AI and ML applications are improved.

Many individuals have also switched careers due to the high demand for prompt engineers in recent times. Seeing how industries are recognizing the importance of prompt engineering and its potential, it is undeniably one of the fastest-growing fields in the world of AI consulting.

But what behind the hype over AI prompt engineering, and how exactly does it go on to help businesses? Let us find out by taking a closer look at what AI prompt engineering is and its benefits and challenges.

What is AI prompt engineering?

AI prompt engineering is carried out by prompt engineers to leverage the natural language processing capabilities of the AI model to generate better results. Organizations are typically looking to achieve the following objectives with prompt engineering techniques:

• Improved quality control over AI-generated results
• Mitigate any biases in the output from the AI model
• Generate personalized content for very specific domains
• Get consistent results that are relevant to the expectations of the user.

All-in-all, the meaning of prompt engineering is providing insightful prompts to an AI model to get accurate and relevant results without a lot of corrections or additional prompts. This is to go beyond the natural language processing abilities and give the model exact instructions on how to respond.

This process is mainly done by understanding how the AI model interacts with different prompts and requests. Once the behaviors of the artificial intelligence or machine learning model are clear, prompt engineers can guide AI models with additional prompts that achieve the desired outcome.

Benefits of AI prompt engineering for today's business

Let’s get yourself acquainted with the key prompt engineering benefits:

Enhanced reliability:

After the right prompts have been set, the results generated by the AI model are very predictable and usually fall within your standards for informational accuracy. You could also set up the AI model to only deliver output that complies with content sensitivity guidelines.

Knowing that your results will only fall within the guidelines that you have set by prompt engineering AI models is very reassuring when it comes to reliability. Such a prompt-engineered generative AI can be very useful to publications for rapid content creation.

Faster operations

Establishing your requirements and expectations through AI prompt engineering beforehand can go a long way to speed up your operations in general. The time taken to generate the ideal result is reduced, as the objective is predefined in adequate detail to the AI model.

Additionally, you also spend less time working on errors generated in the final output because prompt engineering fine-tunes the responses of the AI model to replicate the ideal outcome as closely as possible, allowing you to cut down on the time spent on correction and reiteration.

Automate your business workflows

Automate monotonous tasks and make internal processes more efficient.

 

Easier scalability

Since the accuracy and speed of AI-generated output are improved so drastically by prompt engineering, you also get to quickly scale the use of AI models across your organization. Once AI prompt engineers have figured out the ideal prompts, replicating similar results across workforce becomes easy.

Users also can record all interactions with the AI model to understand how it reacts to different prompts, allowing them to refine their understanding of the model and its capabilities. This newfound knowledge can then, in turn, be used to further improve the results that are generated.

Customized AI responses

Perhaps the greatest advantage of using prompt engineering techniques is the ability to get customized results from your choice of AI models. The impact of customized responses can best be observed on bigger AI models such as ChatGPT, where there is a lot of variation in data.

While these larger AI models often generate very generalized and simple results, they can be fine-tuned to deliver responses at a much greater depth. Leveraging AI models in this manner can also deliver completely radical results that wouldn’t be possible unless you prompt engineer AI.

Cost reduction

Upon finding the best AI prompts for their applications, businesses can significantly speed up their AI-driven processes, which reduces the need for constant human intervention. As a result, the costs spent on corrections and alterations are reduced as well.

There is also the environmental cost that is rapidly rising due to the rampant use of powerful AI software that consumes a lot of energy. These reductions in costs may seem miniscule at first, but they quickly add up and help you save a lot of resources in the long run.

Challenges associated with prompt engineering

As fantastic as prompt engineering is, it does come with its fair share of challenges that are left for AI prompt engineers to deal with. The scope of these problems ranges from minor inconveniences to outright failure when generating a response.

Crafting prompts

While the advantages of effective prompting are brilliant, creating these prompts is a completely different ordeal. Finding the perfect prompts takes a lot of trial and error by human prompt engineers as they go through all of their options.

Over generalization

Over generalization is an issue with AI applications that can render them completely useless and occurs when the model provides a highly generalized result to any given query. This is exactly the opposite of what you want when implementing prompt engineering strategies.

While there are many reasons for over generalization, the ones related to prompt engineering are usually due to inadequate training data. Making your query too focused may force the AI model to give you a generalized answer as it lacks the data to give out a detailed response.

Interpretation of results

During the testing phase of new prompt formulations, prompt engineers have to accurately decipher the results delivered by the AI model. The evaluation of the quality of results is a time-consuming task that requires the prompt engineer to be vigilant at all times.

Ensuring that the output quality is up to the mark is only half the battle, as prompt engineers have to understand how they can refine their prompts to gain better results. If the interpretation of the results is incorrect, then the whole efficiency of the model is compromised. This is where the competency of AI prompt engineers is also tested heavily to ensure that they can implement AI in business with ease.

AI model bias

Almost all AI models possess some level of bias when it comes to their generated output. While this is not exactly malicious, it is an inherent part of using massive data sets to train AI models. Because these biases stem from data, there are not a lot of effective ways to mitigate them.

While prompt engineering does eliminate bias if done correctly, it is quite burdensome to identify all the biases that are present within an AI model. Factor in the time to generate new prompts based on the discovery of biases, and you can estimate how long it will take to get the perfect set of prompts.

Changes to data

Unless you have your very own AI model running locally, it is pretty difficult to have any control over the data used in the AI model. In such circumstances, it is very difficult to predict how existing prompts will hold up in the long term with future updates that are made to the AI model.

When additional data is added, the responses to pre-made prompts can be radically different from the expected result. Whenever such updates are made, it usually involves reformulating your entire prompt library to get the best out of AI solutions.

Model limitations

In some cases, the prompts themselves would work well on certain AI models but wouldn’t be very effective on others. This is all because of the different limitations that are encountered in different AI and ML models, which makes AI consulting very difficult.

Since new AI models are being rolled out fairly frequently, it can quickly become overwhelming to adapt your prompt engineering tactics to other models. Some AI models might be downright incapable of generating coherent responses to your prompts altogether.

Who is prompt engineering for?

Much like with any other new solution, some sectors can prove to gain better results than others due to their nature of operations. Knowing how prompt engineering supercharges the generative abilities of AI models, such as AI marketing solutions, the following sectors can benefit the most from prompt engineering:

1.  Content Creation 
2. Data Analysis
3. Finance
4. Research
5. E-Commerce
6. Health Care
7. Legal Services
8. Customer Services
   

Among all the large language model benefits, one is the ability to use prompts that yield better results when compared to generic prompts for AI. Knowing the magnitude of difference that is created in the results, it becomes essential to try and integrate prompt engineering practices. While the advantages of prompt engineering are undeniably great, the investment of time and effort from a prompt engineer may not be worth it if you are in the initial stages of implementing AI solutions in your organization.

In scenarios of integrating AI into regular work processes, it is very important to evaluate the capabilities of the AI model that you choose to use and if you can really benefit from prompt engineering.

 

 

 

Prompt Engineering: A Comprehensive Guide with Examples

In the era of generative AI, prompt engineering has emerged as one of the most essential skills for effectively interacting with large language models (LLMs) like OpenAI's ChatGPT, Google's Gemini, and Anthropic's Claude. While traditional software engineering relies on coding, prompt engineering is the craft of designing input text (prompts) to get desired outputs from AI systems.

This guide is aimed at beginners who are curious about prompt engineering, offering a comprehensive overview of the fundamentals, techniques, and practical examples.

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What is Prompt Engineering?

Prompt engineering is the process of crafting inputs to AI models in a way that yields the most useful, relevant, and accurate results. Because LLMs generate responses based on patterns learned from massive datasets, the way you ask a question can significantly influence the answer.

In essence, prompt engineering is about:

• Understanding how LLMs interpret and respond to input.

• Designing prompts to guide the model's behavior.

• Iterating and refining prompts to improve outcomes.

---

Why Prompt Engineering Matters

AI models are highly capable, but they are not mind readers. They depend entirely on the text provided. Subtle variations in phrasing, tone, specificity, or structure can change the results dramatically.

Benefits of good prompt engineering include:

• More accurate and relevant outputs.

• Reduced hallucinations or fabricated content.

• Increased efficiency in achieving results.

• Better alignment with business, educational, or creative goals.

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Basic Principles of Prompt Engineering

1. Clarity

   • Clear prompts produce clearer responses.

   • Avoid ambiguity.

2. Specificity

   • The more specific the prompt, the better the output.

   • Specify the format, tone, length, or point of view if needed.

3. Contextualization

   • Provide background or context to help the model generate more informed responses.

4. Instructional Language

   • Use imperative or guiding language: "List", "Summarize", "Compare", etc.

5. Iteration

   • Refine and reword prompts based on outputs.

   • Use feedback loops.

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Types of Prompts

1. Descriptive Prompts

   • Example: "Describe the atmosphere of Mars."

2. Instructional Prompts

   • Example: "Explain how a blockchain works in simple terms."

3. Creative Prompts

   • Example: "Write a poem about a robot discovering emotions."

4. Comparative Prompts

   • Example: "Compare the economic policies of Keynes and Hayek."

5. Conversational Prompts

   • Example: "Pretend you're a tour guide in ancient Rome. Walk me through a day in the city."

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Common Techniques in Prompt Engineering

1. Zero-Shot Prompting

   • Asking the model to perform a task without providing examples.

   • Example: "Translate this sentence into French: 'The sky is blue.'"

2. Few-Shot Prompting

   • Providing a few examples to guide the model.

   • Example:

     Translate the following sentences to French:  1. The apple is red. -> La pomme est rouge.  2. I like music. -> J'aime la musique.  3. She is reading a book. ->  

3. Chain-of-Thought Prompting

   • Encouraging the model to reason step by step.

   • Example: "If there are 3 apples and you take away 2, how many are left? Explain your reasoning."

4. Role-based Prompting

   • Asking the model to adopt a specific role or persona.

   • Example: "Act as a professional career coach and give resume tips."

5. Prompt Templates

   • Predefined prompt formats to standardize input.

   • Useful in automation and large-scale tasks.

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Tips and Best Practices

1. Be Iterative

   • Start simple and refine as needed.

2. Use Constraints

   • Limit word count, specify format (e.g., bullet points), or define tone (e.g., formal, friendly).

3. Test for Edge Cases

   • See how the model responds to unexpected inputs.

4. Break Down Complex Tasks

   • Use a series of prompts for step-by-step tasks.

5. Utilize System Messages (if supported)

   • Many APIs allow for system-level instructions to guide behavior consistently.

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Examples of Effective Prompting

1. Basic to Advanced Prompting

   • Basic: "Tell me about Newton's laws."

   • Better: "Summarize Newton's three laws of motion in simple language for a 10-year-old."

2. Formatting Output

   • Prompt: "List the benefits of solar energy in bullet points."

3. Using Roles

   • Prompt: "You are a chef. Give me a quick, healthy dinner recipe using spinach and chickpeas."

4. Creative Prompting

   • Prompt: "Write a short science fiction story about AI taking over Mars colonies."

5. Chained Reasoning

   • Prompt: "Solve this math problem step-by-step: What is 25% of 240?"

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Challenges in Prompt Engineering

1. Ambiguity in Prompts

   • Unclear inputs lead to unpredictable outputs.

2. Hallucinations

   • Models may generate false or fabricated information.

3. Token Limitations

   • Each model has a maximum context window (measured in tokens).

4. Bias and Ethics

   • Outputs can reflect biases present in training data.

5. Consistency

   • Responses may vary between runs even with the same prompt.

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Applications of Prompt Engineering

1. Software Development

   • Code generation, debugging, documentation.

2. Marketing

   • Ad copy, email campaigns, content ideas.

3. Education

   • Personalized tutoring, lesson planning, quiz generation.

4. Research

   • Summarizing papers, generating hypotheses.

5. Creative Arts

   • Poetry, storytelling, idea generation.

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Future of Prompt Engineering

As AI models grow more sophisticated, the role of prompt engineering will evolve. The future may include:

• Prompt programming languages: Tools or DSLs for structured prompting.

• Multi-modal prompting: Integrating text with image, audio, or video inputs.

• Automated prompt optimization: AI optimizing prompts for best results.

• Embedded prompt layers: Built into apps and workflows seamlessly.

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Conclusion

Prompt engineering is the bridge between human intent and machine response. It's a powerful tool that unlocks the potential of AI, enabling users to tailor outputs to their specific needs. By understanding the fundamentals, practicing different techniques, and learning through iteration, anyone can become proficient in this modern skill.

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Useful links

• OpenAI Cookbook: https://github.com/openai/openai-cookbook

• Awesome Prompt Engineering: https://github.com/promptslab/awesome-prompt-engineering

• Papers with Code: Prompt Engineering Papers

• Prompt Engineering Guide: https://www.promptingguide.ai/

10 Tips for Creating a Foundation Model for India

As we are discussing creating  Large Language Model (LLM) for India instead of using LLM created by American and Chinese companies I thought of sharing some tips to build a AI with a difference. Here are 10 key tips for building a strong foundation model for India, considering its unique linguistic, cultural, and infrastructural diversity:

 

India

1. Multilingual Training Data

   • India has 22 official languages and hundreds of dialects. A robust foundation model must incorporate high-quality, diverse, and regionally balanced data across multiple languages.

2. Bias Mitigation in Data

   • Socioeconomic, gender, and caste-based biases exist in many datasets. Implement bias detection and fairness checks to ensure inclusive AI outputs.
     
     
     
     

3. Incorporation of Local Knowledge

   • AI should integrate indigenous knowledge, traditional practices, and cultural references to provide more accurate and contextually relevant responses. 
     
     
     
     

4. Handling Low-Resource Languages

   • Many Indian languages lack sufficient digital data. Utilize transfer learning, synthetic data generation, and crowd-sourced datasets to enhance AI capabilities.
     
     
     

5. Adaptation to Regional Variations

   • Words and phrases can have different meanings across states. Training should include localized NLP models to understand context-specific variations.

6. Data Quality and Noise Reduction

   • Ensure datasets are accurate, well-annotated, and free from misinformation. Remove noisy or misleading data from social media sources.

7. Infrastructure and Scalability

   • Indian users access AI on a wide range of devices, from high-end smartphones to basic feature phones. Optimize the model for efficiency and offline accessibility.

8. Legal and Ethical Compliance

   • Follow India’s data protection laws (such as the DPDP Act) and ensure responsible AI practices to prevent misuse and protect privacy.

9. Customization for Sectors

   • Train AI specifically for key Indian sectors like agriculture, healthcare, education, and governance to provide domain-specific solutions.

10. Community Involvement & Open-Source Collaboration

• Engage with local AI researchers, linguists, and developers to create an open, collaborative model that truly represents India's diversity.

Leveraging Prompt Engineering for Business Agility

Introduction

This tutorial is aimed at beginners who are curious about prompt engineering, offering a comprehensive, hands-on overview of the fundamentals, techniques, and step-by-step examples to help you practice and master the skill.

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What is Prompt Engineering?

Prompt engineering is the process of crafting inputs to AI models in a way that yields the most useful, relevant, and accurate results. Because LLMs generate responses based on patterns learned from massive datasets, the way you ask a question can significantly influence the answer.

In essence, prompt engineering is about:

• Understanding how LLMs interpret and respond to input.

• Designing prompts to guide the model's behavior.

• Iterating and refining prompts to improve outcomes.

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Why Prompt Engineering Matters

AI models are highly capable, but they are not mind readers. They depend entirely on the text provided. Subtle variations in phrasing, tone, specificity, or structure can change the results dramatically.

Benefits of good prompt engineering include:

• More accurate and relevant outputs.

• Reduced hallucinations or fabricated content.

• Increased efficiency in achieving results.

• Better alignment with business, educational, or creative goals.

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Basic Principles of Prompt Engineering

1. Clarity

   • Clear prompts produce clearer responses.

   • Avoid ambiguity.

2. Specificity

   • The more specific the prompt, the better the output.

   • Specify the format, tone, length, or point of view if needed.

3. Contextualization

   • Provide background or context to help the model generate more informed responses.

4. Instructional Language

   • Use imperative or guiding language: "List", "Summarize", "Compare", etc.

5. Iteration

   • Refine and reword prompts based on outputs.

   • Use feedback loops.

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Step-by-Step Prompt Engineering Examples

Example 1: Summarizing Text

Task: Summarize a paragraph about renewable energy.

Input Text: "Renewable energy sources such as solar, wind, and hydroelectric power have seen increased adoption across the world due to their sustainability and low environmental impact. These sources help reduce carbon emissions and reliance on fossil fuels."

Basic Prompt: "Summarize the paragraph."

Output: "Renewable energy sources like solar, wind, and hydro are sustainable and reduce carbon emissions and fossil fuel use."

Improved Prompt: "Summarize the following paragraph in one sentence using simple language for a high school student."

Output: "Renewable energy like solar and wind is good for the environment and helps us use less fossil fuel."

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Example 2: Rewriting in a Specific Tone

Task: Rewrite a message to sound more professional.

Original Text: "Hey, I need that report by tomorrow or we're going to miss the deadline."

Prompt: "Rewrite the following message to sound professional and polite: 'Hey, I need that report by tomorrow or we're going to miss the deadline.'"

Output: "Could you please share the report by tomorrow? It's essential to meet our project deadline."

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Example 3: Code Generation

Task: Generate a Python function to calculate the factorial of a number.

Prompt: "Write a Python function to calculate the factorial of a number."

Output:

def factorial(n):      if n == 0 or n == 1:          return 1      else:          return n * factorial(n - 1)  

Improved Prompt: "Write a Python function using recursion to calculate the factorial of a number. Include comments for beginners."

Output:

# This function calculates the factorial of a number using recursion  def factorial(n):      # Base case: factorial of 0 or 1 is 1      if n == 0 or n == 1:          return 1      # Recursive case      else:          return n * factorial(n - 1)  

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Example 4: Role-based Prompting

Task: Get travel tips from a local perspective.

Prompt: "You are a local from Tokyo. What should I do if I have just 2 days to explore the city?"

Output: "If you have only 2 days in Tokyo, start with Asakusa to visit Senso-ji Temple, then head to Shibuya for the famous crossing and local food. Day 2: Visit Meiji Shrine, explore Harajuku and Omotesando for shopping, and end with a night view from Tokyo Tower."

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Example 5: Step-by-Step Reasoning

Task: Solve a basic math word problem.

Prompt: "If a pencil costs $2 and a notebook costs $5, how much would 3 pencils and 2 notebooks cost? Show your steps."

Output: "Step 1: Cost of 3 pencils = 3 x $2 = $6 Step 2: Cost of 2 notebooks = 2 x $5 = $10 Step 3: Total cost = $6 + $10 = $16"

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Techniques Recap with Examples

Technique	Description	Example Prompt
Zero-shot	No examples provided	"Translate to Spanish: 'Good morning.'"
Few-shot	A few examples provided	"Translate: 1. Hello -> Hola, 2. Cat -> Gato, 3. Dog ->"
Chain-of-thought	Reasoning steps included	"How many legs do 3 dogs and 2 cats have? Show steps."
Role-based	Model acts in a specific role	"You are a nutritionist. Suggest a healthy breakfast."
Output formatting	Specify bullet points or tables	"List pros and cons of remote work in bullet points."

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Tips and Best Practices

1. Be Iterative – Start with simple prompts and refine them.

2. Use Constraints – Set output length, format, or style.

3. Test Edge Cases – Check how the model handles unexpected or incorrect input.

4. Chain Prompts Together – For complex tasks, break them into smaller sub-prompts.

5. Maintain Context – Provide background when needed.

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Common Mistakes to Avoid

• Being too vague or general.

• Using long, complex sentences without structure.

• Not checking model output for factual errors.

• Relying on a single prompt for a complex task.

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Applications of Prompt Engineering

1. Software Development – Generate code, test cases, documentation.

2. Education – Create lesson plans, quizzes, explain topics.

3. Marketing – Write copy, generate ideas, refine slogans.

4. Customer Support – Draft replies, suggest solutions.

5. Creative Writing – Develop stories, characters, plot ideas.

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Future of Prompt Engineering

• Prompt Libraries – Standard reusable prompts for industries.

• AI-Generated Prompts – Meta-models that optimize prompts.

• Natural Language APIs – Interfaces where prompt engineering is embedded.

• Prompt GUIs – Visual builders for non-technical users.

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Conclusion

Prompt engineering is the bridge between human intent and machine intelligence. Mastering this skill allows you to unlock the true potential of AI models. Through clarity, specificity, structure, and creativity, you can design prompts that deliver powerful, practical, and precise results.

Practice with the examples in this tutorial, experiment with variations, and you'll become proficient in crafting prompts that give expected results.

Understanding Generative AI and Generative AI Platform leaders

We are hearing a lot about power of Generative AI. Generative AI is a vertical of AI that holds the power to #Create content, artwork, code and much more. Numerous studies have shown this transformative capability has led to numerous benefits across sectors. There has been 40% increase in efficiency in content creation, a 75% surge in creative output & an upto 90% growth in the level of automation in certain workflows. It is interesting to study how GAI (Generative AI) is revolutionizing traditional processes and opening doors to innovative possibilities. Generative AI, as you know is a subset of AI that focuses on teaching machines to produce original and creative content. 

While traditionally AI operates based on predetermined rules, Generative AI builds ability to learn from data and generate content autonomously. This technology leverages complex algorithms and neural networks to understand patterns and produce outputs that mimic human-like creativity.

The significance of generative AI lies in its potential to revolutionize industries across the board. From content creation to software development, generative AI tools are paving the way for greater efficiency, creativity, and innovation. Companies are increasingly adopting these tools to streamline their processes, reduce manual efforts, and unlock new possibilities that were once unimaginable.

• Healthcare: In the medical field, generative AI assists in analyzing medical images, Xrays & scans, diagnosing diseases & predicting patient outcomes. Radiologists using generative AI for image analysis reported above 30% improvement in accuracy in detecting subtle anomalies, ultimately leading to more timely and accurate diagnoses.

• Software development : Generative AI is transforming the way developers write code. It aids developers by generating code snippets, improving software testing by identifying approximately 30% more defects & even suggesting optimal solutions to coding challenges. These features result in faster development cycles , reduce redundancy and deliver better code quality.                                                                                                                                        
• Content creation: Writers, marketers, and content creators are utilizing Generative AI to automate content generation, effectively streamlining workflows and achieving a remarkable 40% reduction in time spent on content creation. This efficiency boost allows them to focus on higher-level strategic tasks and creativity                                                       
• Language translation Language barriers are being broken down as generative AI tools translate text and speech in real-time, enabling seamless communication across diverse languages. These tools achieve an amazing 95% accuracy in translation and that is helping foster global collaboration and understanding.                                                                                              
• Gaming : Developers are using generative AI to create immersive virtual worlds, generate in-game content, & adapt gameplay based on player behavior. Some reports hace found that this real-time adaptation is resulting in upto 50% increase in player engagement and satisfaction, enhancing the overall gaming experience.                                                                                                                                    
• Finance: For a long time now institutions are leveraging generative AI to analyze market trends, predict stock movement with an impressive 85% accuracy & optimize trading strategies. This technology-driven approach has led to a 25% increase in trading profitability and more informed investment decisions.                                                                                                 
• Art work & Design:  Artists are exploring generative AI for creating unique visual art, illustrations, and designs, pushing the boundaries of creativity. A study found that incorporating generative AI in the design process led to a remarkable 75% increase in the number of innovative and eye-catching design concepts produced.

• Music Composition: Generative AI tools have extended their capabilities to the realm of music composition. These tools analyze existing musical compositions and generate original melodies, harmonies, and rhythms. Musicians and composers can leverage these tools to break creative barriers and discover new musical ideas.

As generative AI continues to advance, its applications across every industry vertical  are expected to become even more sophisticated, further revolutionizing the way we work, create, and interact across various sectors.

You may want to read about the following platforms that are revolutionizing AI

1.ChatGPT

2.Scribe 

3.AlphaCode

4.Gpt-4

5.Bard

6. Cohere Generate

7.Dall-E2

8.Synthesia

9.Flowmachines

10.Claude -  Multi lingual Code Helper

11.ArtBreeder -  Image Creation

12,AI Dungeon - Storyteller

13. DeepCode - Code Reviewer & Suggestions

14. Duet AI - 

15. PaintChainer - B/W Coloring tool

 

 

 

 

ABC Technical Architect Training

ABC Technical Architect Training

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Overview of my 12 week training plan designed to equip aspiring and current technical architects with the essential knowledge and skills required to design, evaluate, and lead modern software systems. Each week includes theoretical sessions, hands-on labs, and assessments.

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Week 1: Foundations of Software Architecture

• Introduction to Architecture Roles and Responsibilities

• Architectural Styles and Patterns (Monolith, Microservices, Event-Driven, etc.)

• SOLID, DRY, KISS, YAGNI Principles

• Case Study Discussion + Assignment

Week 2: Architecture Design Principles

• Domain-Driven Design (DDD)

• Clean Architecture & Hexagonal Architecture

• Architecture Decision Records (ADRs)

• Group Exercise: Designing a Modular Application

Week 3: Cloud-Native Architecture

• Cloud Service Models (IaaS, PaaS, SaaS)

• Serverless Design & Event-Driven Computing

• 12-Factor Apps

• Lab: Deploying a Serverless App

Week 4: Infrastructure & DevOps for Architects

• Infrastructure as Code (Terraform, AWS CDK)

• CI/CD Pipeline Architecture

• Containerization and Kubernetes Basics

• Lab: Create a CI/CD Pipeline with GitHub Actions

Week 5: Application & Integration Architecture

• API Design (REST, GraphQL, gRPC)

• Event-Driven Design and Messaging Patterns

• API Gateway, Service Mesh (Istio, Linkerd)

• Hands-on: Implementing API Gateway with Rate Limiting

Week 6: Security Architecture

• Threat Modeling & Secure Design Principles

• IAM, Encryption, Zero Trust Architecture

• Compliance (SOC2, GDPR, HIPAA basics)

• Exercise: Securing a Cloud-Native Application

Week 7: Observability & Resilience

• Monitoring, Logging, Tracing (OpenTelemetry, Prometheus)

• Chaos Engineering Concepts

• Lab: Building Resilience with Circuit Breakers & Retries

Week 8: Data & AI Architecture

• Data Modeling and Storage Patterns (OLTP, OLAP, NoSQL)

• Real-Time Data Streaming Architecture (Kafka, Kinesis)

• AI/ML Architecture, MLOps Overview

• Workshop: Designing a Data Pipeline

Week 9: Enterprise & Solution Architecture

• TOGAF, ArchiMate, Zachman Basics

• Business Capability Modeling

• Roadmapping & Portfolio Architecture

• Practice: Drafting a Solution Architecture Document

Week 10: Platform Engineering & Developer Experience

• Internal Developer Platforms (IDPs)

• GitOps, ArgoCD, Feature Flags

• Platform as a Product Mindset

• Exercise: Building a Developer Onboarding Flow

Week 11: Emerging Technologies & Trends

• Edge & IoT Architectures

• Blockchain, Web3, DApps

• Quantum-Resilient Cryptography

• Green Software & Sustainable Architecture

Week 12: Capstone & Review

• Capstone Project Presentation

• Architecture Review Board Simulation

• Soft Skills: Stakeholder Communication, Trade-off Narration

• Final Evaluation & Feedback

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Deliverables:

• Weekly Quizzes & Assignments

• Hands-on Labs & Mini Projects

• Capstone Project

• Certificate of Completion

Recommended Tools:

• Visual: Lucidchart, PlantUML, draw.io

• DevOps: GitHub Actions, Docker, Kubernetes, Terraform

• Cloud: AWS, Azure, GCP (based on organization preference)

• Data: Kafka, PostgreSQL, Redis, Athena

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Optional Tracks (Post Training):

• Specialized Deep Dive: AI Architect, Cloud Solution Architect, Platform Architect

• Certification Preparation: AWS SA Pro, Azure Architect Expert, TOGAF

 

What is Artifical Intelligence infused BPM ?

When I had created Accenture's Point of View (POV) on BPM (comparative study of BPM products or iBPM products as Gartner likes to calls it). We had predicted year on rise of 25% in adoption of BPM by the top 5 industry verticals and year on average productivity improvement of over 10% for enterprises that have implemented matured BPM tools (We had suggested that a mature BPM implementation alone could improve the enterprise productivity by 10%) . Back then Digital Transformation was not a buzz word and we see not just businesses but even governments are aggressively pushing for Digital Transformation. Feels great when I see that today people are well past discussing Need For BPM for Digital Transformation and discussion has moved over to the sensational Artificial Intelligence infused BPM'.
                                                      Artificial intelligence focuses on making already “intelligent” systems capable of simulating human-like decision-making and execution – enabling those systems to perform functions traditionally executed by skilled human professionals – but do so at a much higher level, because of the speed and power available on modern computing platforms.  One needs to understand that for AI TO REALLY HAPPEN the AI software architecture would have to be be similar to our own central nervous system, which controls most of the things we do even though we don’t consciously think about it. So when ever AI matures instead of nerve signals, AI uses algorithms to simulate human behavior.
                                                  Frankly 'what we are implementing today does not have 'human like decision making' capability and that's why we cannot call it AI.  AI is the future and huge investment in research are being done but existing systems do not have intelligence similar to humans because we do not have capability to produce software that has emotional and biochemical aspects of a human brain. What people at large refer to as AI (as of  Jan 2018 ) is actually Machine Learning driven by big data & data mining and which gives insight to improve decision making but there is no Human Like Intelligence as claimed by some companies. Fact remains that the insight from big data aids better and smarter decision making as decision making has definitely improved as we have huge data and technology to process the data at a fast pace.  As such we have been using data insights from historical data to make better business decisions for quite a few years now and if industry decides to call this data insight as AI then we can say AI & BPM are old friends.
                                                                     So if someone tells you he is working on something revolutionary, integrating AI with BPM, you can tell him that AI-BPM is in production for quite sometime - actually for quite a few years (smile)! We did implement Smart Business Process that could be triggered by events from Complex Event Processing framework based on certain event types. We did implement Real Time Big data processing and integrated it with BPM to get insight from Data In Motion and make smarter decisions in real time.  In short we have been doing AI driven BPM for years so don't get stoned by tall claims by some AI-BPM expert!

Point I want to make is that though AI-BPM is not new at the same time AI has been evolving at a fast pace along with ML and we need to continuously  innovate and integrate ML with BPM to get better business insights. What we have already implemented for various industry is a Smart Next Best Action capability that aids a software system to take better decision in real time. Typically NBA is a custom software that uses intelligent insights extracted from big-data processing to aid enterprise decision making and we are using the word intelligent not because system is smart like humans but because it makes decisions based on millions of past records or transactions to recommend the most appropriate action  - something which can almost act like a human not because of intelligence but because of Machine Learning.

Here are some random industry numbers about AI & BPM  -

• As of today more than 50% of the businesses that are processing Big Data have implemented AI solutions and these businesses have reported more than 50% increase in new business insight from Big Data.
• AI has helped 50% of implementations to make better business decisions, 20% businesses claim to improved automated communication with help of AI and only 6% businesses claim to have reduced work force by implementing AI
• Most implemented area for AI is Predictive Analytics (eg Weather Data, Operational Maintenance etc )
• More than 80% of the implementors claim that AI has improved efficiency & created new jobs
• Almost all implementors acknowledge that Data Analytic technologies are more efficient when coupled with AI
•                                                                       
  

So the intellegence from insight from the huge data is helping make busniness more smart, more proactive, more predictive,  more efficient, more productive and more customer friendly thus opening avenues for new products and expansion of business.

So how is AI or DI changing in the BPM?

1. Intelligent Recommendations - Continuous machine-learning can  provide relevant recommendation to customer as well as business
2. Intelligent Marketing - AI can make recommendations to agents or directly to consumers using profile attributes & response behavior and keep learning in real-time, so that the next best offers are relevant to the customer and keep improving over time. Software can help marketing  agent deliver the right recommendations to the right customer at the right time.
3. Process Automation - Data-insight help reduce workflow inefficiencies, automate human tasks & processes, and reduce repetitive tasks. 
4. Preferential Treatment to Valued Customers - ML and predictive analytics can estimates a customer’s behavior and guide the agent to both satisfy the customer.
5. Next Best Action -  NBA helps agents guiding them about the next-best-action to take that will solve a specific problem and lead to higher customer satisfaction and also predicts the sales lead conversion and reduce customer churn
6. Sales Prediction -  Predictive Analytics helps predicts the likelihood of a lead to close and suggests next best action and strategies to the sales agent.  Predictive engine can identify new sales oppurtunities that may not be outright visible to the team.
7. Customer Retention - Predictive engine can predict customer churn and also suggest steps required to retain customer.      

Game changing BPM & Data Intelligence/ Artificial Intelligence

What BPM can deliver today is not just efficient and smart process management but a real time business management. The BPM game is changed and BPM is now offering

1) Predictive Business  - Analyze, Sense, Learn, Predict, Act
2) Proactive Recommendation leads to better customer service
3) Reduce Churn by predicting and addressing customer concerns
4) Better value to customer value delivered based on customer insight
5) Better Forecasting by 360 degree view of customer and business
6) Real time enterprise proactively addressing real time events

There are many BPM vendors and vendor analysis by Accenture, Gartner or Forrester can help you decide which BPM vendor has product and features that are right to deliver your solution. Pega, Appian are some of the leading BPM layers of 2018 but there are at least 19 BPM vendors to chose from and you can refer to How to select a BPM (Business Process Management) product? to know how to go about selecting the right BPM product