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.
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:
|“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
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).
|Assignment||Identify what something is (classification)||
|Identify how connected items are (regression)||
|Grouping||Given data, determine correlations and subsets (clustering)||
|Generation||Given an input, create an image or text (generation)||
|Forecasting||Given time series data, predict future changes (sequencing)||
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.
|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:
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.
“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:
In Chapter 3, we explain how to develop a full data strategy to support your AI initiatives.
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.
Source: MMC Ventures
“Solving really hard problems using AI takes time and depth. It follows a different curve. Endurance is key.”Fabio KuhnVortexa
The budget you require for your AI initiatives will depend upon multiple factors including:
“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:
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.
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:
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.
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
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.
When your first project is underway, anticipate the longer- term aspects of your AI strategy. Your long term AI strategy should consider:
“Remember that AI is a capability, not a product. It’s always improving.”David BenigsonSignal
“Plan for the long term and then obsess about capabilities to make your vision come true over five to ten years”Timo BoldtGousto