The AI Playbook

Chapter 1: Strategy


  • Recognise AI’s potential for value creation. While you should not add AI to your initiatives for the sake of doing so, not exploring what AI can offer your company risks losing competitive advantage.
  • Identify appropriate problems for AI to solve. AI is particularly effective at: assignment (identifying what something is, or the extent to which items are connected); grouping (determining correlations and subsets in data); generation (creating images or text based on inputs) and forecasting (predicting changes in time series data).
  • All businesses will have challenges where the above apply and, therefore, where AI can be fruitful.
  • Prioritise projects according to value and viability. Ensure you have a clear, concise specification of the problem and desired outcomes. Assessing viability includes considering whether your training data is balanced (free from bias), exhaustive (captures all relevant variables), diverse (captures rare situations) and is of sufficient volume.
  • Timescales for creating AI are less certain than for traditional software development – and typically extend non-linearly with desired accuracy. Timescales vary according to
  • the problem type, subject domain and data availability. Frequently, a prototype with limited accuracy can be developed within three months.
  • Align your budget with your goals and deployment strategy. The budget an AI initiative requires will depend
  • on multiple factors including the complexity of the problem, the availability and quality of training data, and the deployment strategy you select.
  • AI deployment strategies include: calling third party AI APIs; using managed AI services from third parties; building a small in-house AI team; and building an extensive in-house AI team. A large, in-house team is a multi-million-pound annual investment. Many companies develop a proof- of-concept using their existing development teams, and third-party APIs or paid services. Then, they create a budget proposal and begin with a small, in-house AI team.
  • Seek sponsorship from senior executives. Support from management will be important for new AI initiatives to succeed. To build support, educate senior management regarding the benefits of AI while setting realistic expectations regarding timescales and results.
  • Anticipate and mitigate cultural concerns about AI.
  • To some, AI will be unfamiliar. Others will see their workflows change. Many people may be concerned about the impact of AI on job security. Frequently, AI will enhance an individual’s role by offering ‘augmented intelligence’. Address concerns proactively by highlighting the ways in which AI will support individuals’ goals and enable team members to redirect their time to engaging aspects of their roles.
  • Expect non-traditional security considerations. Protect against malicious activity via thorough system testing and exception handling.
  • When your first project is underway, anticipate the longer- term aspects of your AI strategy. Consider: maintenance; data (budget to retrain your system as data evolves and increases); evolving algorithms (new techniques will offer better results in the future); scaling (extending useful AI systems to additional business units and geographies); innovation (a roadmap for new AI initiatives); and regulation (a strategy to comply with new legislation as it emerges).

Strategy: The Checklist

Identify use cases

  • Understand the categories of problem AI can address
  • Seek ideas and advice from AI practitioners
  • Create a list of potential AI initiatives offering business benefit

Prioritise initiatives

  • Develop a clear statement of the business challenge and opportunity
  • Define measures of success
  • Review the suitability of available data

Understand timescales

  • Appreciate the need to iterate AI systems
  • Develop realistic goals regarding accuracy and timescales

Develop a budget

  • Understand budget requirements for different AI development strategies
  • Select an initial and long-term development strategy for creating AI systems

Build buy-in

  • Define the return on investment of your AI strategy
  • Develop a clear, detailed implementation plan
  • Educate senior management regarding AI and establish realistic expectations

Mitigate cultural and security concerns

  • Educate and involve your workforce to address concerns
  • Plan for non-traditional security challenges

Develop a long-term strategy

  • Set aside budget for maintenance, updates and re-training
  • Review new technology and evolving data sets
  • Develop plans to extend your AI to additional business units and to undertake new AI initiatives Develop a process to ensure compliance with evolving legislation

AI is a powerful tool. Before you invest time and money in the technology, you need a strategy to guide its use. Without an AI strategy, AI will become an additional cost that fails to deliver a return on investment. Below, we describe how to: identify appropriate use cases for AI; select your first AI initiative; explore deployment strategies; anticipate timescales; predict required budget; and establish the cultural buy-in necessary for success.

Recognise AI’s potential for value

AI is a powerful set of techniques offering companies tangible cost savings and increased revenue. Further, adoption of AI is ‘crossing the chasm’, from innovators and early adopters to the early mainstream. While you should not attempt to add AI to your initiatives for the sake of doing so, and should be mindful of its limitations, not exploring the ways in which AI could offer value your company risks losing competitive advantage. Approach AI based on its transformational potential.

To engage effectively with AI, separate AI myths from reality:

Fig. 1. Separate AI myths from reality
Myth Reality
“AI is a distant dream.” While general, human-level artificial intelligence will not be available for many years, there are many applications for AI that are viable today and offer companies cost savings and revenue growth.
“We don’t have the budget to implement AI.” While a large, in-house AI team will require extensive investment, third parties offer access to AI services (via API) for as little as several hundred pounds. Further, as AI democratises, growing libraries of pre-trained models offer results at low cost. If you have a software engineering team, you can validate benefit from AI at minimal cost.
“AI is dominated by the big technology companies. There’s no point in my company trying to compete.” While companies including Amazon, Google, IBM and Microsoft have developed extensive AI services, they lack the strategic desire, data advantage or domain expertise to tackle the many sector – or function – specific applications for AI. Today, a rich ecosystem of startups, scale-ups and corporates are deploying AI for competitive advantage.
“We can’t use AI because our business requires explainable processes.” There are several ways to explain what is occurring inside an AI system (see Chapter 6). Some AI is directly explainable. With deep learning systems, where explainability is a challenge, it is possible to explain how input variables influence output.
“I can throw AI at my data and it will offer efficiencies.” AI is a tool that requires a structured problem and appropriate data to be effective.

Source: MMC Ventures

“Always focus on the problem you’re using AI to solve.”

Tim SadlerTessian

Identify appropriate problems

AI can be effective at solving problems – but it is important to begin with a clear problem in mind. Broad considerations are insufficient. When creating a list of potential AI initiatives, develop a precise definition of a problem you wish to address. “Always focus on the problem you’re using AI to solve” (Tim Sadler, Tessian). Do you have a problem whose solution will add value within the business or to customers? Can the problem be solved using AI? AI is particularly effective in four problem domains: assignment; grouping; generation and forecasting (Fig. 2).

Fig. 2. AI is highly effective at Assignment, Grouping, Generation and Forecasting
Problem Domain Definition Examples
Assignment Identify what something is (classification)
  • Understandthesentimentoftext
  • Recognise logos in images
  • Make a medical diagnosis based on symptoms
Identify how connected items are (regression)
  • Quantify the relationship between a preservative and product shelf life
  • Evaluate how consumer income affects propensity for impulse purchasing
  • Predict the purchase price of a second-hand vehicle based upon its condition
Grouping Given data, determine correlations and subsets (clustering)
  • Identify social subgroups within a customer base for enhanced targeting
  • Evaluate factors that correlate with patient melanomas
  • Identifythemesincustomerfeedbacksurveys
Generation Given an input, create an image or text (generation)
  • Create a chatbot for customer service
  • Translatecustomerconversationstoadifferentlanguage
  • Create photorealistic media for advertising
Forecasting Given time series data, predict future changes (sequencing)
  • Predict weekly sales to avoid the loss of perishable stock
  • Determine the probability of equipment failure to enable proactive replacement
  • Predict exchange rate fluctuations

Source: MMC Ventures

“The applications of AI are endless.”

Timo BoldtGousto

All businesses will have challenges of the types above – and therefore problems to which AI can be usefully applied. The table below provides examples of popular AI use cases.

Fig. 3. AI is being fruitfully applied to a wide variety of use cases
Sector Example use cases
Asset Management Investment strategy Portfolio construction Risk management Client service
Healthcare Diagnostics Drug discovery Patient monitoring Surgical support
Insurance Risk assessment Claims processing Fraud detection Customer service
Law & Compliance Case law review Due diligence Litigation strategy Compliance
Manufacturing Predictive maintenance Asset performance optimisation Utility optimisation Supply chain optimisation
Retail Customer segmentation Content personalisation Price optimisation Churn prediction
Transport Autonomous vehicles Infrastructure optimisation Fleet management Control applications
Utilities Supply management Demand optimisation Security Customer experience

There are many ways to identify and evaluate potential AI projects, including:

  • Network: to familiarise yourself with AI and its use cases, engage with your professional network and AI communities on LinkedIn and (on, communities can establish informal gatherings and there is a thriving AI community). Many community events are free. Ask an attendee for a coffee and you will find a useful sounding board for your questions and ideas. Informal advice is valuable; you can discuss whether AI might be suited to your use cases, why, and how to turn your idea into an initiative. “Find someone who is already using AI and bounce your ideas off them. Work out if your idea is possible. Have that conversation before even thinking about a consultant.” (Miguel Martinez, Chief Data Scientist, Signal Media).
  • Conferences: seek inspiration, talk with experts and understand industry best practises through conferences. Conferences tend to be high-level executive briefings, sales pitches or presentations of academic research. If you are early in your AI journey, prioritise events with multiple tracks for less experienced practitioners, or a mixture of levels so you receive an overview. Useful conferences will provide access to companies with successful AI solutions, which you can talk to for advice and collaboration. The cost of conference attendance varies from several hundred pounds to several thousand. Familiarise yourself with sessions before you book to ensure a return on investment.

Prioritise projects according to value and viability

Once you have ideas for AI projects, beyond assessing the relative value of each to your company, determine the most viable by addressing the following questions. As well as enabling you to choose a feasible project, the answers will help you define project parameters and objectives.

  • Problem: Does the project fall within the definition of assignment, grouping, generation or forecasting? If you cannot clearly define the type of problem, it may be a viable undertaking but is unlikely to be an ideal first AI project for your company.
  • Definition: Can you state the problem clearly and concisely? If not, you will lack a clear definition of the system’s purpose and will struggle to select and employ appropriate AI techniques.
  • Outcomes: Can you define the levels of accuracy and speed the system must achieve to be successful? Avoid initiatives that lack these measures. If converting an existing manual process, do you know the accuracy and speed of the current workflow? If you are undertaking a new initiative for your company, define what will be deemed a successful outcome.
  • Data: Do you have sufficient data to train and test a system? Without adequate, high quality data to train your system your initiative will fail. If you are choosing between a range of otherwise viable projects, select the engagement supported by the greatest quantity of high-quality data.

“Without adequate, high quality data to train your system, your initiative will fail.”

It can be challenging to assess data suitability. Typically, data must be:

  • Representative: Data you use to train your AI model should reflect the data you will feed your system in its live environment. If the data differs significantly, results will be poor even if the accuracy of your system during training is high.
  • Diverse: Even rare situations should be captured in available training data. Without diverse data, your system may not generalise effectively. Overall accuracy may be high, but your model will fail (misclassify, wrongly correlate or poorly predict) in less frequent situations.
  • Balanced: A biased data set produces a biased system. Does your data have inherent bias? For example, are you analysing CVs for suitability to a role and most candidates are of the same gender? Liaise with individuals in your organisation who understand your data and can advise on its inherent bias.
  • Exhaustive: All relevant variables must be included in the available data. For assignment and grouping problems, missing variables will lead to oversimplified results (unwarranted correlations). In other problem domains, you may be unable to derive utility from your system.
  • Sufficient: While a smaller volume of high-quality data is preferable to extensive, poor-quality data, the volume of data you can acquire must be sufficient to train your algorithm well. For assignment problems, useful results frequently begin to emerge after approximately 1,000 examples for each output label. Some problems require more or fewer. For forecasting problems, you may require data spanning at least double the duration of the periodicity of the item forecasted. For grouping and generation challenges, typically output improves with data volume but again 1,000 examples are frequently a minimum. Typically, the more complex the challenge, the more data points you will require.

In Chapter 3, we explain how to develop a full data strategy to support your AI initiatives.

Timescales will extend non-linearly with accuracy

Timescales for AI initiatives can be less certain than for traditional software development. AI systems cannot predictably be developed once, tested and then deployed. Typically, multiple cycles of training are required to identify a suitable combination of data, network architecture and ‘hyperparameters’ (the variables that define how a system learns). These dynamics will vary according to domain, the nature of the problem and the data available. Accordingly, it can be challenging to predict or automate AI initiatives unless they are very similar to projects you have previously undertaken.

While timescales will vary according to the problem you are addressing, the resources you have committed and the buy-in you have achieved, you can frequently develop a prototype within three months. It may take days to develop a first version of a system that offers 50% accuracy, weeks to improve the system to 80% accuracy, months to achieve 95% and much longer for additional incremental improvements (Fig. 4).

For straightforward problems, expect a similar progression but over shorter timescales. For particularly challenging problems, which require extensive data to describe the problem or new techniques to solve it, this timeline may extend significantly.

Fig. 4. Timescales typically increase non-linearly with desired accuracy

Source: MMC Ventures

“Solving really hard problems using AI takes time and depth. It follows a different curve. Endurance is key.”

Fabio KuhnVortexa

Align your budget with your goals and deployment strategy

The budget you require for your AI initiatives will depend upon multiple factors including:

  • the nature, complexity and domain-specificity of the projects you undertake;
  • available, and preferred, development strategies (use of third-party services versus an in-house development team);
  • availability, quality and consistency of relevant data;
  • a well-considered starting point;
  • regulatory and ethical considerations to be addressed.

“Costs will vary according to the development strategy you select.”

Some challenges can be addressed with a readily-available third-party application programming interface (API). Others may be solved with a single pass of data through an existing, public domain network architecture. Others still will require extensive research and multiple iterations of training and adjustment to meet success conditions. Costs will vary according to the development strategy you select. The following strategies offer progressively greater functionality and uniqueness in return for increased spend:

  • Third-party APIs: If another company has already solved your business problem, and you need only call the counterparty’s service via an API to receive a result, prices can start as low as several hundred pounds. Using third-party APIs is the fastest way to deploy AI in your company and requires minimal time from your existing development team.
  • Bespoke third-party services: To obviate the need for your own AI team, you can engage third-parties to develop and train your AI models. You will need to gather and prepare your own data and have a broad overview of the process of creating models. You are unlikely to require a budget of more than a few thousand pounds for training and running costs, plus the cost of an individual – ideally a data expert already in your business – with an understanding of AI to manage the process.
  • A small, in-house team: A dedicated in-house AI team is likely to cost at least £250,000 to £500,000 per year, even for a small team. Whether you seek to repurpose publicly- available models, or solve unique problems, you will need to pay for: two to four individuals; the hardware they require to train and run their models and potentially extra hires for productionising the resulting system.
  • A large, in-house team: An extensive team, recruited to solve problems at the edge of research, will require a multi- million-pound investment in personnel and hardware. This investment may yield a unique AI offering. It should only be considered as a first step, however, if your challenge cannot be solved with existing AI techniques and solutions, if you have access to unique data, and if you face significant restrictions on your ability to pass data to third parties.

We describe, in detail, the advantages and disadvantages of different development strategies in Chapter 4 (Development). You may wish to develop a proof-of-concept, using your existing development team and third-party APIs or paid services, before creating a budgetary proposal. Most companies then start with the small, dedicated AI team.

Seek sponsorship from senior executives

Support from senior management in your organisation will be important for new AI initiatives to succeed. Your company may have a Board that strongly favours adopting AI; that sees AI as over-hyped and irrelevant; or has a healthy scepticism and seeks validation of benefits before assigning extensive resources. To build support within your company, define the focus of your first AI initiative and then set realistic goals. Your system will not, and need not, offer 100% accuracy. If it can save effort, even if results require human verification, you can deliver increased efficiency.

You can then present to senior management a plan that includes:

  • a statement of the problem your AI will solve;
  • a summary of outputs and benefits for the company;
  • details of the nature and volume of data required;
  • a viable approach with realistic timescales.

Leaders may be reluctant to invest in technology they do not understand. To achieve buy-in, it may be necessary to educate senior management regarding the benefits of AI while setting realistic expectations regarding timescales and results.

Anticipate and mitigate cultural concerns

When deploying AI, anticipate the potential for cultural resistance. For many in your team, AI will be unfamiliar. Some employees will see their workflows change. Many employees are concerned about the impact of AI on their job security.

Frequently, AI will enhance an individual’s role by delivering what is termed ‘Augmented Intelligence’. AI can bring new capabilities to an employee’s workflow or free a human operator from repetitive, lower value tasks so he or she can focus on higher-value aspects of their role.

Address concerns proactively by highlighting the ways in which AI will support individuals’ goals and workflows – and enable your team to redirect their time to the most engaging aspects of their roles.

“We go through a change management program to educate the workforce. We explain that AI takes care of repetitive tasks so people can focus on bigger things.”

Dmitry AksenovDigitalGenius

Address non-traditional security considerations

In addition to the traditional security considerations you must manage, AI systems can be attacked in non-traditional ways.

If a classification or grouping system is given an input beyond the scope of the labels on which it has been trained, it may assign the closest label it has even if the label bears little relation to the input. Causes of confusion, more broadly, may be exploited. Malicious individuals have manipulated system inputs to obtain a particular result, or to disrupt the normal practise of AI systems (for example, by spraying obscure road markings to confuse autonomous vehicles).

Protect against malicious activity via thorough system testing and exception handling, undertaken from the perspective of an individual deliberately attempting to undermine or exploit your system.

A long-term strategy should incorporate evolution and extension

When your first project is underway, anticipate the longer- term aspects of your AI strategy. Your long term AI strategy should consider:

  • Maintenance: To maintain your system’s intelligence, regularly test results against live data to ensure results continue to meet or exceed your acceptance criteria. Set aside budget for future updates and retraining and monitor for performance degradation. Chapter 5 provides a blueprint for maintaining AI systems effectively.
  • Data: Monitor changes in your data over time. As your business grows or changes focus, data fields (including time series data, languages and product characteristics) will evolve and expand. Retraining your system regularly should be a component of your long-term AI strategy. To develop a comprehensive data strategy for AI, see Chapter 3.

“Remember that AI is a capability, not a product. It’s always improving.”

David BenigsonSignal
  • Algorithms: AI techniques are developing rapidly; what you create today may be less accurate and slower than systems you develop in 12 months’ time using the same data. Ensure a member of your team understands advances being made in AI and can advise on when to apply them to your use cases.
  • Scaling: A plan to leverage your existing AI systems by extending their deployment to additional business units and geographies.
  • New initiatives: A roadmap of new use cases for AI within your organisation to deliver increased cost savings, greater revenue or both.
  • Legislation: Developments in AI are being monitored by legislative authorities (see Chapter 6). Develop a strategy to comply with new legislation as it emerges.

“Plan for the long term and then obsess about capabilities to make your vision come true over five to ten years”

Timo BoldtGousto