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.

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.

Cost , When IT Advisory Breaks Down

No Enemy is Worst than ‘Bad Advice’

When the financial books are closed each year, we meticulously measure the profits; growth of assets;  intellectual property and patent portfolios; accumulated liabilities to employees, suppliers, service providers, regulatory bodies, investors and owners. Subsequently a lesser portion of entrepreneurs, submit further to observe number of unexpected business turns, mishaps or even anomalous increase in operational cost, which consumed significant part of the already tight resource allocations. In other words, the discovery of a series of cash burners that lead to the flushing of valuable resources which could have otherwise deployed

for growth engines. 

For instance, If a small business is operating on a budget of $50 million and is incurring 5% unexpected expenses on average per year, that translates into $2.5 million in financial losses alone. In addition, let’s say that 50% (25 million) of your operational budget is allocated for production which incurred an overrun of 10% of the forecast which translates into another $2.25 million. That is still a total of $5 million over the budget (assuming that no buffer allocation was made for unexpected expenses). Reflecting carefully a little deeper, every one of the drawbacks, may lead us to a point when we received ‘bad advice’ or even worst ‘no advice’ (withholding information intentionally is also a form of ‘no advice’) from a cadre of expert and specialist consultants that we retain as financial, legal, marketing, advertising or technology advisors, among others. According to a survey conducted in the U.K small businesses loss over £6 billion due to misdirections from experts with IT consultants leading the pack (44% followed by management and marketing consultants at 34% and 32% respectively) causing the most damage to businesses.

The common theme here is to take every advice with discretion. As such, investigating IT projects that are currently squandering resources and yet hindering the business from its goals, is a necessary step to identify sources of specialist misrepresentations. 

The Alluring Appeal of the Third Platform Infrastructure and Services

Asian SMBs are pouring billions of dollars (3rd in spending after North America and Europe) into technology with the hope to increase their competitiveness and success rate against larger businesses by adopting more and more of third platform infrastructure solutions (e.g. mobile, cloud, big data, analytics, Blockchain, social tech and collaboration tools) and services (e.g.  AI/cognitive, virtual /Augmented Reality, IOT, 3D, Security, Robotics). Choosing the obvious ‘cloud’ path (both private and public) may have reduced the conventional risks associated with IT projects but even then, there are questions to be asked and answers to be probed to avoid mistakes. Matching the software or services that links best with business operation; choosing the right cloud technology (often to be align with the existing tech eco system); picking the right development platform for mobile or IOT applications and even understanding the various direct and indirect licensing estates, is crucial in realising the returns of investments channeled into automation, optimisation, waste and redundancy elimination. In short, pretty much any decisions on solutions to business challenges depend on sound IT advice.

When Reputed Automation Projects turns into Drawbacks

When strategic IT initiatives get derailed they turn into impediments that weigh on the business forming waste, sluggish business processes, redundant workloads and prone to manual interventions to produce – the very same elements that we are trying to abolish for a much error free and productive business environment. For instance, in 2004 HP stated that is suffered a shortfall of $400 million in quarterly revenue due to a failed ERP migration of its ordering and supply chain systems. The breakdown caused a 12 weeks business interruption with order process and resulted in manual intervention to conduct day to day business, not the least three key executives fired by the CEO Carly Fiorina, at the time for the costly affair. Closer to home, AirAsia was sued by the Australian regulators in 2010 for breaching consumer law by not displaying the total ticket prices on their 
reservation systems. AirAsia later admitted that this was due to a poor localisation of their system for
the Australian market. However, this incident caused them $200,000 in fines. In 2006, CPF took action on a leading global IT Services provider for a failed IT project it contracted in 2001. It was cited that communication breakdown within the parties along with complacency has caused the project to collapse.

Such is the price to pay when IT projects goes awry. While they may not impact the business severely as isolated incidents, a plague can form collectively if not addressed in timely fashion resulting in overrun of budget, miss delivery dates, suboptimal applications that leak revenue, threat from various non-compliance (industry, consumer, data privacy, security, tax, accounting, software licensing), wasted computing resources (comatose VMs, equipment), damage to brand reputation and overall workforce productivity.

But Should Technology Advisors Condemned?

Pointing the fingers never helps anyone. In majority of cases, relationship breakdown between business and IT advisors over accountability and delivery of outcomes, or dissensions arising of it, are the true causes of project failures. The agency theory problem perhaps is best to explain why either party might get derailed from accomplishing project goals in the process of aligning and creating values for their employers (sometimes may involve several business units), partners, customers and their ownself. What’s important to know though, is that most IT advisors (technical and business) are earnest and perform credibly to stay in repute. Nevertheless, provisions should be allocated by both parties to take action in the event of negligence, complacency or breach of contractual agreements, that likely to emanate losses.

In addition, it’s crucial to tap into this rich pool of experience, knowledge and technology mastery in scoping and deliberating on what makes an IT project successful. What a cognitive waste it would be, to just ask this group of plans and recommendations but to never dive in the ‘why’, ‘how’ and ‘when’ such recommendations takes full effect to benefit business. Here are some notable areas to heed in navigating conversation with your IT advisors.

Firstly, understand who your IT Advisors are, what they represent, aims and areas of conflicting interests. In any one project, it is common to have several IT advisors with slightly different agendas and strength. On the vendor’s part, sales and consulting has a responsibility to promote, position and sell their solution as the best fit for your requirements. An independent or in-house IT champion may maintain a neutral position to assess what's the best for the organisation but tend to build assumptions and loyalty with certain providers from past experiences, creating a blind side to their judgement. Sizing these advisors and their leverage in key initiative, is the first code to crack. It is also advisable to adjust compensation model if necessary to suit the dynamics of the relationships, their interests and priorities (E.g instead of hourly rate to delivered functions).

Secondly, align expert recommendations and proposals with business, strantegy, users and its automation needs. Don’t underestimate the power of isolated units, their fiefdoms and current workarounds to complete order processing, procuring supplies, making payments or even connecting with other third party providers such as logistics to ensure business runs as usual. Bring together owners of processes to communicate the automation plans and why it is important to the business. Early involvement of all stakeholderst of the respective processes, aids in uncovering challenges that would be otherwise missed.

Thirdly, request your IT advisor to help you visualise a best case and worst case scenarios of success, with current resources, work culture, best practices, governance mechanisms, process methodologies and existing technology environment. This should help match risk areas during implementation, triage of business interruption, impact to productivity and regulatory compliance among others. This information will enable further adjustment to budget, timeline and drive the necessary changes (e.g. skills upgrade for workforce, upgrade of relevant tools and applications, inducing suitable best practices, familiarisation of the futuristic workplace notion) which in creases the success rate and contain risk exposures.

Technology Advisors turns into Priceless Assets

Recognising a reliable and credible technology advisor is somewhat facile. They are ‘rebels’ and ‘masters’ of their field, constantly contending the constraints of modern technology in a value creating business, even though they are not entirely immune to defeat. A good advisor will ensure you invest in the right business areas; choose the right technology solution; lead technology benefit analysis; help define a suitable integration strategy for best inter operability of tools, systems and applications; promotes acculturation of the right skills and best practises; outlines risk exposure; and is never without a mitigation and disaster recovery plan. 

Much importantly, they stick around wielding their prescience and immaculate social intelligence, when a project is hit with unanticipated calamities or additional requirements to include ongoing changes from regulatory, compliance, technology landscape, integration, operation, customer and market behaviour perspective. 


But these traits can only be an asset if the idea to manage failure, change and challenges is premeditated in the governance of IT initiatives. Expecting everything to go exactly as planned is a ‘mortal sin’ in this practise, as much as surrendering to stultifying statements claiming all application projects are headed for Armageddon (as stated by Gartner) which is both highly disturbing and questions the very constituent of IT advisory.

Third party platform may have intensified complications notably in areas of integration, security, data privacy, intellectual property, access to services, and multiple clouds; but instantaneously this also made way for much efficient delivery, flexibility and agility to the business. Exercising sufficient control on bodies of work according to timeline and extracting values as you go, is the new norm of the tech world. If this is understood correctly, than we know which part of our conventional wisdom should be relinquished for the future of a democratised technology environment

Relationship Formula for Small Businesses

If you want to go Fast, Go Alone. If you want to go Far, Go with Others.

I must admit that while I was writing my last blog on social media advertising as a crucial customer touch point, my mind was already filled with hundreds of questions on the premise of how various dimension of business relationships impacts upward improvement in revenue, profitability, stock prices, intellectual properties, brand, product utilisation, partner networks, markets, productivity, customer satisfaction, employee satisfaction, reputation and many other outcomes too granular to be mentioned.

What a grave mistake it would be to simply engage into action, accompanied with just a ‘gut feel’, before analysing these relationships, its layers and tiers; correlatives; strength; values or risks to your business? Instead, should we be calling our actions and channeling our investments based on the conditions of key relationships to the business? How does one relationship affect the other? For example, negative energy accumulating in the workforce can certainly impact customers and partners which are critical to growth; ruthless investor activism that pushes leadership into buy back programs and dividends during sales slump to quickly raise return of stocks will not only result in exhaustion of enterprise coffers, but will contribute greatly to income inequality in the workforce and the society in general.

There are countless number of ways to bring structure and automation to track most of these relationships, but a bubble diagram is perhaps sufficient to initiate study and map m utual values, which paves the way to mark priorities according to business goals. In fact, any investment on marketing, advertising and automation should take into consideration of such priorities and value creation activities. Making this a critical exercise especially for small businesses in rapid growth mode with small caps and trust me that this blog will not lead you to a CRM dialogue of any ‘X’ factor as a necessary point of resolution but may influence such conversations in the future.

A Case to Reflect

Long standing enterprises that has been around for over hundreds of years such as Colgate-Palmolive ( or Colgate rather), Coca-Cola, Citi, IBM and GE were some examples of businesses which survived the test of time mainly due to their founding executives ability to visualise, create, manage and control internal and external business relationships to generate an overall positive vibe that fuelled growth.

Coca-Cola for instance, was once sold for 5 cents a glass and started business in the late 19th century with just total of 7 or 8 serving a day through soda fountains. Today, this business has grown close to 1.9 billion serving per day and I need not explain the prices nor its brand prowess. The creator of this drink John Pemberton, a pharmacist and a war veteran was hoping to find an alternative or cure for morphine addiction, as many people suffered such an addiction back then due to the war, just like Pemberton. In fact, the first version of this drink was a coca-wine (alcohol and cocaine infused drink) like many other carbonated fountain drinks of the time (e.g from Spain and France). The non-alcoholic version was created only after the banning of such ingredients in fountain drinks (even though I strongly believe that the coca leaves are still a key ingredient ). At the time, Mr Pemberton also claimed that Coca-Cola cured many diseases, including morphine addiction, indigestion, nerve disorders, and headaches, though these aren't the reasons why we drink coca-cola today. How this business grew to what it is today? I would think finding the the secret recipe to an elixir that appealed to a global taste bud was the easiest part.

Pemberton, sold his business, prior to his death, to several businessmen including a young druggist, Asa Candler. Pemberton, also brought his sons to hold different property rights of the business. Candler, saw the potential of the drink and started building his downstream relationships following the soda fountain trails of restaurants, bars and other recreational outlets despite infighting among  the different stakeholders including a thorny relationship with one of Pemberton’s son. Candler also observed the increasing demand as a further opportunity to bring Coca-Cola directly to customers by bottling it with partners. Candler watched competition and imposters closely in order to understand how they try to clutter the market and introduced the unique design of the the bottle that is still in use today, to help customers choose the original product. Candler’s focus on relationships that created an advantage to his business soon overwhelmed and helped to severe toxicity from the equation and made more room for expansion.

A ‘Scribble’ is as good a Start as a ‘Doodle’

Sometimes finding the starting point is the hardest – and this is when a general mind map of all relationships that affect your business can come in handy. Scribble it or take it a notch further, just for the fun of it and doodle it (no one said business have to be boring or characterless).

Startups might find this exercise pretty straight forward if you are dealing with a single or range of interrelated products aimed at the same market category. A simple bubble diagram, indicating upstream, lateral and downstream relationships (see illustration 1 – Sample Key Relationship Analyses), along with markers to identify layers of connections is useful visual analyses which helps to get detail idea of your current relationship trends, returns, advantages, risks, value creation and investment activities. In small companies, this can be a revealing exercise that points you where much of your resources is being consumed and if the returns are worthwhile. Save the diagram, and you will see that the story it tells, will change as you revisit them every quarter. In fact, you may also observe changes in business relationships for the better or worst depending on incidents and actions that your business may have undertaken. This will also help the business from refraining or changing tactics where relationships are cold and value creation there would just be like running ghost trains.

Prioritise Relationships - Finding the Perfect Balance

But the idea to get into this exercise is not just to identify types of relationships and where bulk of your investments are being absorbed or even what improvement is being attempted in the past. It is, sort of a barometer to the validity of your current business models and identify which relationships deserves your utmost attention at this moment in line with growth agendas (which changes from time to time). Evidently, there are hundreds of relationship commitment hypothesis, studies and best practises contributed by researchers on how to form long standing buyer-seller relationship stratagems in a variety of businesses and non profit backdrops (e.g. Equality, Trust, Openness, Rationalism - Smith 1998; Overall Satisfaction - Garbarino & Johnson 1999; Cost of Relationship Discontinuation – Morgan and Hunt 1994; Flexibility - TA Scandura & MJ Lankau 1997), and much of these practices have been assimilated according to industry settings into best breed of technology solutions for easier and faster consumption by businesses. However, what’s missing though is the simplification process of these various commitment variables which is equally critical in growing startups and small businesses.

E.g An IT service provider whom developed a tool that could help enterprise customers migrate swiftly from one cloud to another can choose to sell the tool and relevant services directly to customers facing such business pain. But upon proving the successful adoption of the tool, they may also see the potential to sell through  partners and vendor marketplaces who may be in an ideal position to offer clients a transaction economics that the tool creator themselves are not able to deliver due to specialisation or other resource constriction. Hence, this becomes a question and point of decision between growing the partner network and the direct sales, or both. If so, what would be the ratio to be applied for ideal outcome.

Arms Length Transactions are not necessarily Evil

Many small businesses today are present online with complete automated systems. Clear content on products and terms of sale are self explanatory with online support for customers who may have additional requirements or questions. Apparel, groceries, movie tickets and other cyclical or standardised products (e.g cloud services) get sold without the need for a sales person to interact with each and every one of the customers. These customers don’t expect personal treatment, but just personalised services and products up to their expected quality of standards along with data protection and security assurance. Here, there is a necessity to capture and analyse customer data, purchase history and trends to make appropriate future updates or offers to woo the customers to return and form loyalty to the brand. Often the customers who fall in this category has a tendency to switch from one shop to another easily and as such understanding what is transaction economics to them is critical in deciding and architecting the right loyalty program, advertising media or partners to worky with. E.g. a wine shopper might also appreciate relevant wine accessories, gifting services or the right cold cuts and cheese to go with the wine, all in one place instead of having to visit 3 or 4 shops.

In other words, unlike popular believe, businesses can establish loyalty with arms length relationship by anchoring on the right value creation activities and fulfilling expectation voids left by competitors. As such, instead of running intensive loyalty campaigns for adhoc transactional clients, you might be better off, turning to methods of acquiring and maintaining them cost effectively apart from making continuous differentiation in your offerings. In addition, the freed sales resources can now be repositioned to work on other market segments that needs personal and advisory services to grow(e.g. B2B solutions).

Expect Toxicity and Difficult Relationships

In my years of observations (and personal experience), I am yet to find an enterprise or startup that is not faced with difficult or toxic relationships (futile search, but why not?). Mark Zukerberg and Eduardo Saverin of Facebook, the Ambani brothers of Reliance India, and the fallout between media mogul, Rupert Murdoch and Richard Li of the HK Satellite TV,  are just some high profile known examples to back this.

Partner disputes over shares, intellectual properties and other business rights; a toxic employee or manager de-energising your workforce; a channel partner or sales person taking your business hostage by claiming exclusivity to relationships; technical staff making unreasonable demands in return for critical business assets; or clients threatening to switch provider if you don't lower prices or insist to work with only certain individuals; fraud; and other acts of sabotage are all common difficult relationship situations that can sow dissension and clutter in the business. The absence of or loose governing policies, contracting, legal and other enterprise services in startups and small businesses tends to make this worst. Some organisations become critically effete and drained due to the distractions. Others, take a stance to stay on course with goals and thrive in success by addressing conflicts firmly while putting in place mechanisms to protect critical intellectual, material and relationship assets.

Rewarding Relationships are Manufactured by Minds

“Hear much, leave all that is doubtful alone, speak warily of everything else, and few will be offended. See much, leave all that is dangerous alone, deal warily with everything else, and thou wilt have little to rue. If thy words seldom give offence, and thy deeds leave little to rue, pay will follow.”, Confucius.

Point is, you could be in possession of a ground breaking idea, a disruptive technology patent, abundant materials, and a ready market to adopt your offerings. Though, without identifying and cultivating the right relationships, mutual interests, trust, needs and co-dependence, mobilising these resources could end in long time to return or worst, fails to return.

The effects of advances in multiple enterprise technology disciplines that is transpiring through cloud services for superior infrastructure and applications services; advance analytics; automation of business processes; along with the rise of mobile and sensor based devices today, are driving digital transformations of every imaginable industry. Enabling us with valuable data, that can complement the humble bubble diagram that we talked about earlier in the blog.  This present us opportunity to compare real figures of performance against relationships; recognise what we do and don’t know; where advantages exist; and where an upper hand’s support is critical to succeed.  This is where we bring our multi-dimensional intelligence into work – the best of social, emotional, intellectual, qualitative, empiric and quantitative capabilities to unearth best call of strategies and actions to hold strong the founding virtues of the business.