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

 

 

 

 

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 

Tomorrow of every connected enterprise is Hyper-connected Enterprise

 

Hyper-connected Home

Where do buy sugar from? Pay cash and buy from local grocery store? or order sugar on mobile app? If you are using #BigBasket or #Amazon to buy 1kg Sugar then you should be aware that 100s of computers/devices and at least half a dozen companies,from manufacturer to stockist to cargo delivery to Amazon, all are collaborating over a 'hyper connected network' to get your sugar delivered on time. Which means you are already part of Amazon's Hyper-connected Enterprise where computers, devices and employees create a seamless delivery experience for the customer. Your smart-city is becoming a Hyper-connected city - water, electricity, garbage, emergency services, healthcare, social-welfare everything has either moved or moving to Hyper-connected network of enterprises. You may want to check how companies like Libelium World are helping monitor environment in smart-cities in Spain.

Take another example of entertainment industry. How do you watch your favorite sport? In your living room on large screen TV, live stream on your mobile/tablet/laptop or do you use a dedicated app like Hotstar? Most likely you use all these medium to watch a game of live cricket. Entertainment is being delivered today everywhere and anywhere you choose to be. It is possible because #Hotspot and #Accenture have created a Hyper-connected environment to deliver the content on all medium to deliver content even when you have poor network connectivity. Energy sector suffered huge losses because of electricity leakage and was one of the first to adapt Smart Meter & Smart Energy distribution system. A decade back #USA #Florida based Duke Energy claimed that its electrical system have the capacity to automatically detect, isolate, and reroute power when a problem occurs.

 

Your enterprise too is live 24/7 because the customers expect you to provide service not just on phone between 9 to 5 but at a time that is convenient to them and over a medium of their  choice. The epidemic has only accelerated seamless service delivery over digital medium in a transparent, reliable and secure manner. The enterprise has evolved in many ways and new business models of collaborations are in play to deliver services to the consumer.The digital enterprise is evolving to a new paradigm and it is called Hyper-connected Ecosystem.The biggest change that we are witnessing today is not just the mandate for extraordinary agility and business resilience but also a drastic shift in consumer demand. An evolution of the digital ecosystems that are driving businesses today required organizations, people, devices connecting seamlessly by leveraging an effective hyper-connected ecosystem.

Some leaders talk of Hyperconnected Enterprise as the next phase of Digital enterprise what they don't realize is the evolution had already started years back and you are already on a Hyper-connected Network.The core idea behind Digital Enterprise was always to deliver a services over a connected ecosystem and pandemic has  necessitated innovation to make a business impact and ensure sustainability. The focus today is to enable new innovative open ended technology solutions that are seamless and integration ready to reach the masses. 

 

Blocks of Hyper-connected Enterprise

 

Some enterprises may already have a well designed information system that requires minimal work to become hyper-connected enterprise. The remaining enterprises have to redesign the process ,underlying systems and imbibe the culture of digital dexterity.  The enterprise vision has to change along with the employee mindset to adapt and embrace emerging technologies along with existing technologies to achieve better business outcome and to deliver new products. Not an easy order as it requires enterprise and employees to learn new skills as well as change in culture. Every industry is either moving to become a hyper-connected enterprise or they will have to quickly do the transformation if they intend to stay relevant and compete with the competition who have adopted the new way of doing business. 

The journey

Today enterprises need to be connected to deliver value. From lead generation to fulfillment to customer support all the processes have to be digital.In the next post we will discuss some examples of Everywhere Digital Enterprise. Until everything is connected to everything else...

 

The Evolution of Software Integration

 

Every successful enterprises depend s heavily on underlying software applications and communication between the applications. The problem is that, as time goes by, enterprises invariably end up with software created with disparate technologies and built by several vendors.

The number of software applications varies according to the size of the organization. According to a research small businesses use an average of 10 to 22 applications, and in large enterprises, this number rises to an amazing 700 to 1000 software applications!

The Motivation for Software Integration

All these disparate software applications often need to work together, and this is where software integration comes in. I see various motivations for software integration when I talk to business owners and IT managers. They usually want to achieve one of the following:

• Produce a unified set of functionalities, for example, a unified customer support system
• Increase productivity by reducing the need to switch between applications
• Have easier user adoption, especially if one of the software applications being integrated is new
• Enable data analytics by getting data from multiple sources
• Automate data entry – getting data from another application is less costly than manual data entry

In the early stages of software integration, one of the main issues would be that everything was proprietary and closed. You would buy an application, and all the information you put in it was accessible only from within that application. Not to mention that often it would be available on a single machine or on a limited set of machines. If you wanted to, for example, access that information from another software application, you were into trouble.

But when there is a will, there is a way, and so software integration started. The software integration challenges were initially addressed by implementing in-house integration solutions that used ad hoc and proprietary protocols themselves.

Eventually, with the addition of more and more software systems and the wider spread of internal and external networks, home-grown solutions were no longer adequate. Software integration evolution had reached a new level. The motto "no system is an island" became common, but there was still no standard solution to the problem.

Software Integration Evolution & APIs

Over several decades, enterprise integration technologies leveraged one or more integration styles. The earliest integration style was the Remote Procedure Call (RPC) style, used in single-language environments and very popular in the 80s and early 90s. Later, multi-language systems such as CORBA appeared. In these, an explicit Interface Definition Language (IDL) was used, so that the caller and callee could be implemented in different programming languages and even different platforms.

In the end, the use of Application Programming Interfaces, best known as APIs, became the rule. APIs emerged to expose business functionalities provided by one application to other applications. APIs exist so you can reuse them – the concept has been, from the beginning, that multiple applications could consume the same API. The idea was that developers could rely solely on the API to access an application's functionality without worrying about their implementation details.

What Is API Management & Why You Need It

When developers start using an API, they hope that the API is a contract that will not change. However, APIs are susceptible to the same environmental pressures to change that all software systems face. They are often upgraded, reworked, and sometimes refactored. When this happens, finding actual points of change in the API and making things work again is painstaking work for the developer. This is why API lifecycle management appeared.

Developers also hope they will have to work with as few APIs as possible. This is because each new API represents a new learning curve and involves time and effort. Moreover, when you come across the upgrade problems we mentioned, the developer knows it will help to have few APIs and few inter-dependencies - not to fall into spag

The thing is that this is not always up to the developer, as the need to integrate different software systems grows.

This is what API management is: API lifecycle management for multiple APIs.

As a result of sheer demand, API management using middleware has emerged as a way of using APIs and getting their advantages, while avoiding their known problems. Please note we are looking at API management from the API consumer perspective. If you look at it as an API producer, then the focus will be different.

API Management Tools As Integration Software Solutions - An Example

The new middleware technologies have eliminated the need to call APIs directly. Instead, the developer writes SQL in their new or legacy code, then use prebuilt connectors to translate standard SQL syntax in that code into API calls. These calls retrieve the needed information from the target system (4). This can work for retrieving data (SELECT) or to input/change it (INSERT, UPDATE, DELETE).

The middleware acts as a translator that speaks all the API variants the developer needs, translating them into ANSI standard SQL syntax that the developer knows well and can use together with his favorite programming language, such as Python, Java, or C#, just to name a few.

By using such translating middleware, the developer no longer needs to learn a new programming language or gain expertise in the target system API. This makes all the difference, dramatically reducing the time and effort necessary to integrate software.

Using SQL Connector, the developer has two options:

• he can build his own custom integration software in the programming language of his choice or
• he can start from the source code of any software from the past 40 years.

In both cases, completing the integration will require few lines of code and be quite straightforward.

Using such middleware also eliminates the need to redo your code when you upgrade the target system or its APIs. The middleware company itself will handle all the maintenance efforts. It is now their job to guarantee forward compatibility (and sometimes backward compatibility too).

Ultimately, API management gives enterprises greater flexibility by enabling them to go for the software integrations they need while shielding them from the negative aspects and not compromising on security, e.g. maintaining GDPR compliance.

Last word

Software integration has long been a pain point for businesses, often leading companies to either maintain their legacy systems for longer than they should or fork over large sums of money on developers to migrate to the latest and greatest.

Fortunately, with software integration evolution, you can easily solve current integration challenges and prepare companies for the future by using today's technology of API management middleware. Whether to simply share data between systems, to modernize legacy systems, or to meet complex requirements, endless integration possibilities are at your fingertips once you start using API middleware.