AI Playbook for Startups and SMB

The AI Playbook for SMBs and Startups

Most businesses in the SMB and startup class, still struggle to grasp the broad definition of AI disciplines, what AI really is to their business, where to get started and what successful execution looks like for their businesses. Simplifying a focus area narrowing these concerns specifically to address your business pressures is helpful to keep team in line with long term business goals and strategies.

There are many AI Playbooks written by renown gurus such as Andrew Ng, meant for Fortune 500 organisations and they do contain some very useful guidance and tips for all businesses. Nevertheless, smaller ventures with limited funding, skills and resources may need to rework tactics to suit business.

Here we present steps to formulate a generic AI playbook meant for small and medium sized businesses, which includes stages to:

• internalise AI definition for the business; 
• outline and prioritise business problems that can be addressed with AI;  
• experiment with ready pre-trained model
• build required skills or internal structures;  
• prepare training and test data; 
• choosing a path between pre trained and custom built model for solutions, 
• and assessing overall impact to businesses performance. 

The generic AI Playbook should ideally incorporate other dimensions unique to your business, industry and commercial eco-system over time, for maximum results.

Define and internalise AI for your business...

AI itself is a broad subfield of computer science. It encompasses many branches of studies including rule based AI and decision trees; machine learning (regression, classification, neural networks/ deep learning, reinforcement learning, supervised and unsupervised learning); robotics; text and speech processing (NLP, NLU, NLG); vision (computer vision, image recognition) and several other applications that still have not left the laboratories. 

AI is a field where one or several of these approaches are used to create machines, agents and models that perceive, learn and adapt to perform various tasks and cognitive function similar to humans.  

The AI domain is practically exploding with hundreds and thousands of new approaches, methods and solution areas in transfer learning (learning from lesser data and smaller models), networks with memory (generalising AI, e.g. DeepMind’s differentiable neural computer), reinforcement learning, training simulations, hardware for training and inference that are rapidly advancing in the research, development and real world application stages. 

Communicate how AI should be perceived in your business....

As such, it's easy for businesses to get lost in an ocean of offerings that comes with vendor specific methods, best practices, strategies, tools and ready use cases. Charting and communicating how AI should be perceived within the business very early on, in your AI journey is crucial to ensure potent utility, informed expectations and performance. 

For instance should AI merely assist workers to perform better at tasks? Should AI considered as an avenue to outsource cognitive functions otherwise performed by human workers ? Or should AI be a form of cognitive technology to help expand ordinary worker's ability to explore meaningful solutions to business problems both creative and logical?

Setting the right internal tone on AI can guide alignment with work culture, strategies, high impact projects, investments, selection of tools and services that can drive AI values across the business.

Define business problems that can be solved with AI

The common misconception in this stage is, data science or business team alone is responsible to outline business problems solvable by AI. 

Every business unit can contribute to this stage including sales, marketing, services and tech departments. Imagine how it will affect your business if tech can reduce 40% of cooling needs in your datacenter or how your customer service or inbound sales department can improve performance by multi-fold with realtime speech to text analysis that prompts them with emotion, sentiment and other useful information during a call with a customer.

Build working team; conduct regular meet-ups; answer business questions around growth hacking, improving or staging customer experience, workforce productivity and innovation; keep a revisable record of issues pressing the business growth (this can be a simple excel sheet with list of items); prioritise problems to solve; assess data availability; experiment, assess skills requirement; select initiatives for executive sponsorship and create projects to execute. 

Assess data availability and prepare training data for the problem

Data sources...

Data can be gathered from within the business, public datasets, procured from external sources (e.g. Image Net, Google Data search) or a mix of these. Identify useful data assets for your business problem, list what data is missing or not available and which data can be excluded from your raw selections. Collect and aggregate sufficiently to fit your model. 

Data preparation is a significant contributor to cost of building AI solutions specifically ML models, aside from high performance computing resources though GPU innovations has brought so much economics since a decade ago.

Clean and transform data for training...

Most ML models requires large amounts of data to train and converge before they can be applied to business. Preparing these data may require domain experts to collect raw data, identify features with strongest prediction power and label them. Though the business problem you are aiming to solve, tools and selected algorithms ultimately influences data preparation approach and mechanisms. 

Data transformation is revisited several times....

It is common for developers to spend 50% or more time preparing and scrutinising data quality and revisit data transformation processes (or feature engineering) several times during the development of the model. 

Split data sets for training, test and validation...

The prepared data is usually split into training set to fit the model, test set to evaluates final model fit on the training dataset and validation set to evaluates model fit on the training dataset while tuning model parameters.

Consider pre-train models

Ready to train models for various use cases...

For common business problems which requires simple recommendations, classifications, regression, clustering, anomaly detection and ranking, look for pre-trained model offerings by public cloud vendors such as Google, AWS, Alibaba and Microsoft. Many large and niche AI vendors now offer pre trained models for other slightly complex solutions to perform predictions, speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

No need to build algorithm or ML models.....

These offerings saves businesses the hassle of building algorithms and models from scratch. Plus it's a great way to get started with applied AI for business with quicker results and fast ROI times without having to reinvent the entire process (the value is in the model outcome not building it, especially for non-tech businesses). 

No need to replicate work for  ML backend .....

The team need only concentrate on preparing data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models/ functions can then be deployed with other production applications and systems through APIs provided by cloud vendors to apply learnings on new data samples without replicating any work for ML backend.

ML and DL frameworks

In the event that the business problem you aiming to solve is much complex and does not have a ready pre trained model available, it is advisable to custom build the model custom build the model on a machine learning and deep learning frameworknof your choice, offered by cloud vendors through their ML-as-a-service offerings.

Frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, comes readily packaged with interface, libraries, tools, pre-built and optimised components to facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

However, it is highly recommended for new teams to get experience from simpler projects involving pre trained models first before venturing into challenging custom building models. It is easy to loose ROI when tinkering with ML or DL models without past experience and benchmarks.

Building AI skills 

When it comes to smaller businesses, building AI skills in-house is really a trial and error exercise to find the right blend of subject matter experts (or the minimal quo) to address AI problems and build solutions. This team's skill level is adjusted according to how they plan to address business problems, for instance with ready models or custom build models.

AI requires programming (e.g. R, Python, Lisp, Prolog, Scala, C, C++ and Java) data science (at least fundamental level) and business domain knowledge at the minimum to obtain some high level results. A strong foundation in mathematical fields such as probability, statistics, linear algebra, mathematical optimisation is necessary if your business wish to develop own algorithms or modify existing ones to fit specific goals and constraints.

Training existing workforce, hiring from other organisations and university grads are some of the common methods to build the right team.

Education sites such as Coursera, EDX, Machine Learning Mastery and many others provide neutral self learning content suitable for both technical and business audience. Content varies from fundamental to advanced level and comes with certification process that can be committed at one's own pace. 

Vendors such as AWS, GCP and Microsoft too have their own academies but syllabus may be skewed towards respective services. AWS and Microsoft are so far the best corporate academies I encountered and complies to many criteria mentioned in Andrew Ng's Fortune 500 AI Playbook in addition to easy to follow content and no fuss lab use (note that this is paid resources).

Assessing impact to business..

Once trained, tested and deployed, weigh the model's impact to your business performance. I use a no fuss method where I categorise what was increased, reduced, created and eliminated over a set period of time.

For instance:

• Did the model INCREASE overall customers value, revenue or profitability ? 
• Did the model REDUCE fraud risk, customer churn or cost ?
• Did the model CREATE new growth engines, data products or differentiators ? 
• Did the model ELIMINATE manual task, blind spots or redundancies ?

These answers are then scored and quantified for best alignment with overall strategic goals and objectives. The data is then collated for all AI projects to visualise and quantify the total impact to business financially (profit, revenue, growth rates, etc) and other areas such as competitiveness, reputation, and customer satisfaction. 

This exercise is helpful in two or three ways :

• to identify successful AI projects and approaches
• to build AI projects that can increase the value or performance of existing models
• find optimal opportunities to apply AI learnings to other functional areas 

There are many ways to measure impact, choose one that you are most comfortable with and put it to work. Use the discoveries to increase effectiveness of AI projects for your business while filling gaps in skill sets and access to useful data assets.

Conclusion - Ethics, Explainability and Human-Machine Interaction

In the end, AI systems are nothing but stacks of infrastructure and programs in various architectures that learns and executes continuously, depending on its utility. The machines aid human workers to predict outcomes, navigate a situation and decide on best action path with less mental drudgery while spanning solution options that was never possible before.

But that doesn't mean AI systems and models are perfect or free from self vested exploitation. Since the 2010s public concerns about racial and other biases in the use of AI for criminal sentencing decisions, creditworthiness and other forms of social credit systems (e.g. China) is driving demand for transparent AI systems and agents.

Explaining decisions of AI algorithms or models, especially those powered by deep learning or other complex neural networks is still a difficult task and is also known as the 'interpretability' problem.  Unlike decision trees and Bayesian networks that are more transparent to inspection, complex neural networks stack on one another and may involve millions of neurones processing towards the outcome.

As such, deploying sufficient governance protocols for data privacy and security; an architecture which facilitates transparency and tools to detect bias in algorithms is as critical as creating successful models or functions. Though in many cases engineering for more explainability and transparency may lead to significant accuracy trade offs. 

Making Sense of Public Cloud Investments

The public cloud infrastructure services (Iaas) market, according to Gartner's estimation is reaching $60 billion in 2019 and is growing at 27% YoY (fastest growing cloud services segment) from the previous year. Assuming Asia contributes 25% of the global share, turns the region into a critical geo for cloud vendors to battle for market leadership. 

However, cloud practitioners in Asia are still somewhat battling with what is considered as decade old cloud myths evolving around security, compliance, IT control and short term ROIs when conversing with regional customers riding on conventional structures and work cultures. This includes financial institutions, communication service providers, retailers, manufacturers and other asset intensive industries.

While these are all valid concerns, over the years premium cloud vendors have more than proven that their infrastructure and platform services (IaaS, PaaS) are superior to any single institutional  environment in terms of performance, security, reliability and overall economics to sustain just about any type of workloads. The public cloud values are now evolving rapidly with introduction of newer, advanced services in opensource databases, artificial intelligence, low code coding, and running mission critical workloads, enabling businesses to gain better control of operations as they cut waste, optimise performance, develop the much needed new capabilities and differentiators faster than ever. 

Open-source Database Services

Businesses have been dabbling with open-source databases (as well as open-source operating systems, libraries and tools) even before cloud computing services emerged and was aware of the potential to free organisations from growing commercial database licensing burdens. However, managing these open-source databases required additional training, operational and maintenance resources, apart from the unpredictable fixes and releases from the open source community. These issues limited the usage of open-source databases such as PosgreSQL to a small number of non critical, in-house development projects and applications in the past. 

This scenario changed in the recent years as cloud computing companies started to offer fully managed open-source database services such as PostgreSQL, MariaDB, MySQL and many others. Businesses start to increase adoption overnight by consuming cloud based open-source database services for existing and new applications while gaining new efficiencies, never possible before. The polyglot nature of cloud native development, where multiple types of databases and models are deployed in an application composed of micro-services and API calls only drives this trend further. 

Popularity of open-source databases according to DB Engine Ranking site, is about to converge with commercial databases in 2019 or in the early parts 2020. 

Artificial Intelligence, Machine Learning, Deep Learning

Explore pre-trained ML models

If new to artificial intelligence and its sub domains machine learning, deep learning and transfer learning, take advantage of the various pre-trained models offered by public cloud vendors such as Google, AWS and Microsoft to perform prediction, classifications, natural language processing (NLP), speech processing, computer vision (video, image and object recognition), decision support and planning tasks. 

Start by experimenting solutions to business problems while inflating returns on organisational data assets. This provides an opportunity for the business to quickly reap benefits from readily available models where the team need only prepare data, select features and train the model for required tasks (e.g predictions, classifications).  Trained models can then be deployed with other production applications and systems through APIs to apply learnings on new data samples.

Building custom models..

As the internal team gain experience and get familiarise with ML projects, commence a process to create step-by-step AI playbook to define and prioritise business problems that can be addressed with AI, data preparation, algorithms selection, and developing a model to make predictions. This exercise should involve critical members from business, data science and the engineering teams, ideally aligned to addressing growth challenges, resources optimisation, innovation and enhancing customer experience. 

Machine learning and deep learning frameworks such as Tensorflow, Pytorch, MXNET, Keras, Caffe, Scikit, Microsoft Cognitive Toolkit and Theano, that comes readily packaged with interface, libraries, tools, pre-built and optimised components facilitates fast development of AI models without getting into the details of underlying algorithms and complex architectures. 

Pick a cloud vendor for ML-as-a-service that best fit your needs...

Top public cloud vendors such as AWS, Microsoft Azure, Alibaba and Google provide support to most of the frameworks mentioned above for custom building models apart from their pre trained model offers for common business problems. New developers can benefit from starting with frameworks such as Keras and delve deeper into Tensorflow or other suitable frameworks as they get accustomed with the building blocks and programming languages.

Develop cloud native applications faster ...

Today a challenge sparks an idea, an idea turns into a prototype and a prototype transforms into a multi-platform first release in just matter of days and weeks. Aside from core infrastructure services, cloud vendors offer various advanced services ranging from devop, language SDKs, container orchestration, serverless computing, no to low code development platforms (both proprietary and open-source), APIs, code libraries to ready templates to help organisations accelerate application development arising from sudden business needs. 

Most post cloud applications are series of micro-services connected to one another via APIs leveraging languages, databases and code bases most suitable for the required function and the supporting technical environment.

No to Low Code Platforms

In addition, low code platforms such as Mendix, Outsystems, Google App Maker, Appian, and Microsoft Power App empowers traditional developers, IT professionals and to a certain degree the average business users to accelerate software delivery for business use cases made up of common features and components by simply utilising existing templates, forms, objects, drag and drop of prebuilt features.

Ensure performance of mission critical applications....

Learn which of your mission critical applications are suffering from intermittent or persistent performance issues affecting business processes critical to productivity, customer experience and revenue activities. Cloud vendors offer various services and pricing plans to optimise business applications from SAP, Oracle, Microsoft, IBM and others where data grows exponentially with transactions in time.

Exploring public cloud offers for the aforementioned type of applications can reveal credible areas of savings, operational simplification, performance improvement, speedy delivery of new business requirements, higher utilisation, improved security and compliance. Keeping a revisable record of requirements and working with cloud vendors to formulate cloud contracts to efficiently counter all planned and unplanned workloads can free internal resources for myriad of higher impact business activities.

Know values, learn constraints and strategise for long term...

Ultimately cloud values can vary significantly from business to business depending on the nature of operational model and the overall ecosystem. The four areas mentioned here are general enough for most sectors, though some businesses might benefit immensely from other emerging tech such as augmented reality, IOT, 3D or bigdata platform services.

In addition, identifying constraints that prohibit enterprise architectures from extracting full potential of public cloud eliminates aimless unproductive explorations. For instance legacy apps on rigid centralised architectures that may not gain much efficiencies when moved to the cloud.

Finally having a solid long term strategy, helps. For instance all new development and deployment to take the 'Cloud First' approach, or maintaining a hybrid cloud environment with only unpredictable workloads moving to public cloud or 'Cloud Only' approach where the internal teams build in-house brokerage capabilities to fully monitor its environments on multiple public clouds and perform various optimisation as needed.

'Chip War' in search of AI Supremacy!

Ever wondered why we need GPUs or AI accelerators for optimal performance of AI Applications? Ever wondered why a field that existed since the the dawn of twentieth century is only now burgeoning with breakthroughs? 

As Kaplan and Haenlein puts it, "AI is a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.

Artificial intelligence applications that mimics cognitive functions involves deep neural networks, machine vision, sensor technologies, and machine learning techniques that depends on multi core designs, low precision arithmetics and in memory computing capabilities to work well in both cloud and the edge.

In fields like robotics, autonomous vehicles, drones, decease diagnosis, speech recognition, object or face recognition, capability to vastly expand AI calculations by embracing massively parallel processing power, means near real-time learning, energy efficiency and optimal performance. This is why AI applications requires AI accelerators in  its infrastructure design to support various types of computing based on the use cases for sustaining performance during training and inferences.

AI Accelerators

Researchers in scientific fields were probably the first to discover, experiment and adopt GPUs for accelerating AI applications, almost 2 decades ago.  Since then, a 'Chip War' has slowly formed along with other technological developments and is now reaching its peak, among cloud companies, Internet giants, chip makers and startups searching for supersonic growth, especially in the fields of artificial intelligence, machine learning and deep learning. 

Apart from GPUs (graphics processing unit), other types of microprocessors available in the market currently are FPGA (field programmable gate array), ASIC (application specific integrated circuits), in memory architecture and heterogenous computing. As of today, there is no dominant design that underpins all AI architectures, but NVDIA do seems to command some leadership here while others such as Intel, AMD, WAVE Computing, UK's Graphcore and Israel's Habana Labs  are all beginning to ship viable products. 

Chinese players...

China has been investing aggressively to produce indigenous AI chips to wean from dependence on US based producers, with ongoing researches underway by major tech corporations, such as Baidu, Alibaba, Tencent and Huawei, apart from government initiatives. 

Huawei released several products last year, claiming to be faster than NVDIA with ready adopters in datacenters globally. Another startup called Cambricon released products and is aiming for 30% marketshare in China, setting off the Chinese AI chip industry. One other company worth mentioning here and quite notably working on a customised AI chip is China's leading facial recognition system provider, Sensetime, which is planning to build another three or four supercomputers to process data feeds for facial recognition from millions of cameras nation-wide.

The AI chip leadership momentum will only continue to intensify in 2019....

Intel, Nvidia, AMD and Qualcomm are not the only one competing to produce an omni product that can infiltrate into gadgets, computers, machines and robots. There are others such as IBM, Amazon, Apple, Facebook, Google, Microsoft, Tesla and many more in the Deep Learning space, all making attempts at the design and potentially a universally accepted standard and product.

Facebook recently sent a strong signal by hiring Google's Head of Chip Development, that the social networking firm is serious about building its own semiconductors, joining the likes of Apple, Google, and Amazon.

Cerebras Systems, a startup still in stealth mode hired a top Intel executive, Dhiraj Mallick as its Vice President of Engineering and Business Development. Mallick served as the VP of architecture and CTO of Intel’s data center group prior to this.

What to expect...?

For now, the market for deep learning chips is overwhelmingly dominated by Nvdia graphics processors (or GPUs), which have also been widely used in games and other graphically-intensive applications.

Startups looking to attack this space, has the opportunity to beat the big chipmakers and create a new generation of hardware that will be omnipresent among any AI devices. Think autonomous vehicle, robotics, drones or even a server within a healthcare organization training models for medical problems.

As new products from companies such as WAWE, Huawei, Cambricon, Graphcore and Habana enters datacenters and selected enterprises this year, we might see a flow of special purpose devices being released into the market for AI and deep learning.

AI chip innovation will also aid researchers to make further breakthroughs in various fileds such as Computer Vision, Conversational AI, Natural Language Processing, Reinforcement Learning, Transfer Learning, and General AI. Eventually some businesses and governments from buyer nations, may start to take advantage of the available AI chip offerings and form their own discreet AI clouds for a variety of high profile projects across the organisation for deeper business differentiation and operational excellence as models train faster and learn in realtime.

Should Government Regulate Ride-Hailing?

#LKminiblog - Should Government Regulate Ride-Hailing?

Indonesia is planning to regulate ride-hailing rates, amid pressure and protest from driver groups. Both Grab and Go Jek depended on low price offers to passengers in the past for initial growth and expansion, but prices have always surged as business matures. Plus, the ride hailing firms subsidises drivers during discount campaigns. 

Low price is just an entry strategy....

The low price was just an opportunistic route to break into new grounds and get customers accustomed to a new alternative. Over time, reliable and consistent service quality became the foundation to sustaining the massive success of these unicorns. 

Ride-hailing businesses run on leading edge technologies, not an easy feat to replicate...

Unlike traditional transportation service providers, ride-hailing companies built their business capabilities by adopting various leading edge technologies (AI, ML, DL, Augmented Reality, Mobile app, bigdata and IOT) for operational automation, service delivery, prediction and planning. User data is collected through mobile app and harnessed to innovate faster, improve services and maximise values to the whole business eco system. 

A well functioning alternative service to riders.....

The arrival of ride-hailing companies in Southeast Asia were welcomed, as for once passengers had a choice to abandon conventional transportation service providers, that mistreated clients for decades (all of which were regulated businesses). Since the arrival of ride-hailing companies, more passengers comfortably leave their vehicles at home and use the ride-hailing services. After all, passengers can easily book a ride via their mobile app and get served within 7 to 10 minutes, as opposed to the old call booking system where getting through is extremely difficult.

The solution to driver economics problem is dynamic in nature...

Question is, why would we need government intervention to solve a problem already resolved? Secondly, there are two methods to solve this driver economics issue - one by increasing passenger prices, the other is by streamlining the large number of drivers according to current demand. Both are dynamic elements and neither strategies can be executed by the government efficiently without realtime data, reliable predictive capabilities and the backing of a credible data science team.

Let's not get politics in the way of good business....

Finally, driver groups involved in protests may carry other hidden agendas (speculative but that's the popular trend) than just preserving their economic interests. Government intervention here might end up protecting business interest of politically linked individuals or groups that destroyed service quality, encouraged business monopoly without competition and frustrated consumers in the past.

5 Levers to Optimise Learning

“Nothing is ever Achieved without Enthusiasm”, Emerson

Ever wondered how Uber, ANT Financial (Alipay), Xiaomi, DiDi Chuxing, or Airbnb turned into world's largest unicorns in 2017 (and yes, please note that 3 out of 5 are actually from China) ?

Perhaps it was the early market lead, a disruptive technology, platform inspired business model, successful fund raising rounds or simply favourable government policies. Each firm hacked growth based on different mix of factors but shared one similarity. Their leadership and workforce was able to keep pace with the supersonic growth and recalibrate repeatedly to the next future state.

Entrepreneurs whom are in constant pursuit of new knowledge and finds a thrill in the perils of solving difficult business problems are effective learners. They promote sharing of information, inferences and team collaboration for optimal execution of every business function. Making optimising learning capacity of individuals and teams in organisations an imperative measure in driving and sustaining growth. A metrics closely observed by leaderships and funding ventures alike.

Technology to Assist and Augment 

Businesses operate in an extremely fast environment today, where advancements in consumer gadgets and enterprise technologies have enabled us with massive computing power capable of deciphering quintillion bytes of data in nano seconds. Artificial intelligence and machine learning is further sophisticating automation of softwares, machines, neural networks, robots and humanoids.

Ignoring such developments and their benefits in assisting and augmenting work in sectors such as health, legal, high tech, retail and financial will only leave the business irrelevant to market over time. Instead every technology disruption provides a purposeful learning opportunity to move higher in the work chain that should be embraced.

Make Sense of Data

Similarly online business models, platforms and devices are flooding us with data and information. Researching a customer or partner, means pulling and collating information from various sources internal and external (e.g. within the enterprise walls, certified agencies and what is available publicly).

Using analytics to make sense of the different data sets and correlation to business helps to build better reasoning for business cases, speedily scratch the surface of critical operational issues, dive deeper into situations, or anticipate an upcoming threat (or avoid the ‘boiling frog’ phenomenon). It expands cumulative ability to uncover answers to inherent business questions and expose unchartered frontiers for seeking new understandings. This improve resources allocation and focus for all the right business activities in product innovation, sales, marketing and support.

Practice Problem Solving

Growing startups exposes entrepreneurs to various types of business constraints. Some problems are clearly defined with goals, while others are inhibited by vagueness, thrusting us into a panic zone. The iterative process of identifying, classifying, defining, diagnosing, understanding and breaking down the problem, results in expansive mental progress that improves strategies and methodologies in problem-solving over time.

However, exhausting teams with repetitive problems (which is a target for complete automation anyway) will only erode this cognitive exercise to an inertia. Instead refocus them to address complex challenges, where the process of active revealing and listening in search of a solution mechanism takes place. It is here, where many startups stumbles over a lead, growth engine, untapped market, or a golden opportunity to gauge market share from conventional players.  Riding back on the iceberg parable illustrated in the previous point, the deeper you dwell into business inhibitors, the more questions you will uncover. The journey to answer these questions will lead to breakthroughs.

Failures multiply Worth of Lessons

It's bizarre but success and failure lies in the same direction. Success is reiteration of adjustments made from failure to failure without ever loosing the excitement for the venture.

If Abraham Linchon would have shied away from numerous disappointments and feared the angst that may arise, it would have taken a lot longer to abolish slavery and build a modern America. If Nelson Mandela would have stopped fighting apartheid in South Africa at the thought of being imprisoned for life, South Africa will still be torn in civil wars and severe human rights crisis.

Failure teaches value of resilience, focus, reflection and to bounce back stronger each time a pursuit hits a dead end. Only by apprehending the lessons of defeat, one can gain clarity to amend path forward and avoid repeating mistakes. In fact, no one successful is ever reserved from having to confront calamities, criticism, and temporary standstills. After all, success is sweet when you can tell a story that can inspire others.

Performance Support Tools

Performance support tools, such as collaboration platforms, portals, advance analytics (including bigdata), case and content management solutions (e.g.  JIRA, G Suite, Slack, Asana, and other SMB SaaS Services) that are integrated across the various business functions in the organisation is a great way to distribute and update team members of newly available learning assets. In addition, the design and representation of these tools across functions can influence how quickly complications in process or product can be resolved.

The Act of Perfecting the Game

Using the levers mentioned above will speed learning pace and get us quickly to the deeper composite nature of any business riddle. This creates more room to effectively piece personal mastery with cumulative learning assets garnered from others in a collaborative manner. Pushing teams to increase adoption of core capabilities to understand complexities, prioritising what matters most and develop effective conversations to perfecting the game.

Practise does make us perfect (or at least better) but equally important is to break away from bad habits of not seeing the big picture quick enough, getting stuck in management myths, or living in a delusion that learning comes with experience (The Fifth Discipline, Peter Senge). As they say, you can’t gain without pain or by being oblivious.