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Chapter 11 | Defining an AI Strategy
Mobile-first is simply one of the sensible pillars of anything digital and not a huge achievement. There is no need to have it be the defining, outward facing strategic statement of the company. It’s business as usual.
With the 2017 AI-first announcement, Google was recognizing publicly that there was a new opportunity on the horizon and new challenges to overcome. The opportunity revolves, once more, around meeting users’ expectations and demands. Users are now savvy digital citizens. We all expect a whole new level of sophistication from our devices, especially as we catch glimpses of how things can work better and are no longer mystified by digital services. It’s not enough to simply have access to the service from any device or for that service to be reliable. The more technologically savvy tech companies are demonstrat- ing how smart interconnected services can be and that, in turn, leads us to want all our services to work in the same way. When you can order any meal and have it show up at your front door in 20 minutes or buy any item and have it delivered that same day, it creates a certain set of expectations. This in turn creates a feedback loop that forces companies to upgrade everything else that they do. To stay ahead of the competition and satisfy users, services have to be far more aware of our context and our needs and react appropriately to them, or even proactively anticipate them. If an app doesn’t “get it” we are quick to call it useless, complain in frustration, and move on to the next one. The path to more user-friendly services though is paved with AI techniques. This is what an AI-first strategy means: recognizing that the next evolution of your products and services will depend heavily on your ability to enhance them through the use of AI techniques. It also acknowledges that integrating AI techniques will require concentrated effort and focus, to overcome some of the challenges that will inevitably present themselves. It is not business as usual.
■ An AI-first strategy acknowledges that the path to smarter and better services depends on an organization’s ability to effectively exploit AI techniques and capabilities.
It’s not just Google, of course. Every major technology company has a signifi- cant AI effort underway, transforming both how it does work internally and the products it offers. IBM has been promoting the IBM Watson brand since
2010, even briefly turning it into a household name in 2011 when it won the Jeopardy! TV game show in the United States. SAP has the Leonardo platform, what they call an all-encompassing digital innovation system with machine learning and other AI techniques at its core. Salesforce has Einstein, an AI platform that weaves AI across its CRM offerings. Amazon is arguably as advanced as Google in considering how AI can transform every single aspect of their business. With Apple the strategy is all-encompassing, with services being enhanced through AI and their hardware evolving to better support AI through dedicated chips able to speed up AI-specific computations on devices.
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One can’t help but feel somewhat overwhelmed by the efforts of the technol- ogy behemoths. The effort has reached such a fever pitch that universities are complaining that they can’t staff their own AI research groups because com- panies are hiring people as fast as they can find them.1
How can other organizations develop their own thinking around AI in straight- forward terms? Are we all supposed to move to an AI-first strategy and join the race? Do we need to start hiring AI-experts?
One of the main aims of this chapter is to give you a practical approach to developing your own AI strategy. It’s not about joining the AI race (unless there are extremely good reasons to do so). It’s about identifying the princi- ples to adhere to and tactics to apply at different stages and for different needs for your particular journey, so that you make sure you are making the most out of what AI technologies can offer your business.
A Practical Approach to AI Strategy
“[Strategy is] strength applied to the most promising opportunity.”
—Richard P. Rumelt, Good Strategy Bad Strategy2
Developing a strategy for anything is an incredibly challenging task. The only information you have to act on is what has happened in the past. Everything that will happen in the future is, ultimately, a guess. In the same way AI tech- niques work, a strategy depends on you having a model of how the world works, based on the information you’ve had so far, and devising a set of rules that will allow you to make a prediction about the future. However, unlike the problems AI can currently help us solve, you need to deal with an almost unlimited set of variables. To make matters worse, there is no one-size-fits-all solution. You have to craft the strategy that is right for your organization, and the best strategy for your team will, almost by definition, be an unsuitable strategy for a different place.
As such, there is no single true recipe that will lead to your ideal AI strategy. Only you know what the real ingredients are. What I can do is talk about general principles, ways to prepare and specific methods to apply. Like a chef in a kitchen presented with a mystery box of ingredients, it is your task to open the box, figure out what you have available, and make the best possible use of each ingredient.
1 An iconic example of this was when Uber gutted the CMU Robotics lab by hiring 40 of the lab’s top researchers (out of 100), including the director.
2 Crown Business, 2011.
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Principles
Principles for a strategy act as guiding stars to help us choose one direction rather than another when the ground truths don’t clearly point to a choice. The principles I present here are the distillation of numerous conversations with large and small organizations about what has and hasn’t worked for them, and what is important to focus on and what instead is more of a distraction.
Think Big, Start Realistically, Scale Appropriately
Those of you familiar with the lean startup movement will see the seeds of that movement’s mantra in the heading. It usually goes: “think big, start small, scale fast.” The lean startup movement was started by Eric Reis, who, through his book The Lean Startup3 described a methodology for doing business that embraces uncertainty and minimizes risk by testing the viability of business models through the execution of carefully managed and cost-effective experi- ments. The experiments are designed to test the most critical hypothesis behind a new feature or product before scaling that product to full-blown production. For lean startupers, it is important to have a big far-reaching vision but then start with small experiments that are not that costly, before moving on to scaling as fast as possible in order to capture market value. A strategy for applying AI techniques to your workplace can benefit from the same sort of thinking. At the same time, there are a couple of things to keep in mind—which is why I distorted the mantra to “Think Big, Start Realistically, Scale Appropriately.”
Think Big
The potential of the application of AI techniques to how we do work is trans- formational. It is necessary to think big in order to understand the true scope and assign it appropriate importance. Just like Google with their public AI-first strategy statement, it is about giving the entire mission enough importance so that people pay attention and prioritize it appropriately.
Thinking big is also about recognizing that automation is not just about more of the same but at a lower cost or higher speed. Don’t think of the introduc- tion of automation in a business as akin to a factory line, where component A can be attached to component B in a faster and less error-prone way. Automation through AI also enables entire new ways of doing things. It enables new business models. It is a circle whereby the need for automation leads to digitalization of information and process, which in turn generates
3 Eric Reis, The Lean Startup (New York: Crown Publishing, 2011).
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more information that can be further exploited and leads to completely new ways of solving a problem.
For example, automating support handling doesn’t just mean that you will need fewer resources to deal with the same amount of support requests. It also means that you can better understand support issues as each support request is carefully analyzed by increasingly more sophisticated natural lan- guage-understanding capabilities. The resulting data can be fed directly into product development, and the results of product improvements can be traced back to the types of questions that come through support after a release. It means that you can evolve your support teams to become customer success teams that engage with clients in a completely different way. The introduction of automation in support handling can affect the business all the way through to how product design and development take place and customer relation- ships are managed. However, to realize the full benefits of the effort, you need the big vision version so that you put in place the right stepping stones at each phase to be able to get to the top of the hill.
Figure 11-1 illustrates this flow for any process. We start from a place with historical data and understanding of our processes (1) and do the work to augment our standard processes through automation (2). This, if done care- fully and with a mind to the future, can lead to enhanced data and understand- ing (3). This enhanced understanding starts creating a virtuous feedback loop that can go back into augmenting existing processes (2) but also into creating additional opportunities (4) and even new processes (5).
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Figure 11-1. The AI virtuous process enhancment flow
Start (and Evolve) Realistically
Applying new technologies always involves a high amount of risk. It is not busi- ness as usual. You need to be able to absorb and react to a lot of learnings, and things will not turn out as you would expect them. The lean startup approach has made it popular for people to push to start with small experi- ments (minimum viable products) that will test a key hypothesis before com- mitting additional resources. Overall, that’s a very sensible approach. The crucial question, of course, is exactly how minimal a minimum viable product can be. It has to be enough to give you information about whether it is worth scaling the entire effort. If the product is too simple and too minimal, it is not going to be useful.
With AI techniques we are in a very similar situation. If you are attempting to deploy a prediction algorithm, you need to ensure that you are giving enough space for people to prepare enough data and try enough approaches to have a firm understanding of whether the problem can be solved. Think of it as the equivalent of the escape velocity for a space launch. Engineers know that unless a launch missile is able to develop a velocity of 25,000 km/h it will not
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be able to escape the earth’s gravitational pull. If you know that your invest- ment in engineering will not be enough to surpass that minimum threshold, you are better off spending that money elsewhere altogether.
Now, recognizing where that threshold lies requires a combination of experi- ence and willingness to have some false starts. As such, I believe that there are two key tests that a plan must pass. The first is to ensure that the individual steps in the roadmap have enough clarity that there can be some preliminary research into their feasibility, and that no step is so big that if it fails there is no space for a change of direction. The second is to ensure that each step is fed the appropriate level of inputs, a sort of readiness gate that ensures you are doing enough for it to be useful without doing too much. If coming out of one step you do not meet the readiness gate to move into the next step, you can stop and reconsider.
Scale Appropriately
We, of course, want to scale fast. A large part of the appeal of automated decision-making is that it can enable organizations to move much faster.
However, it is precisely because of the ability of automation to scale quickly and the nature of the automation taking place that we need to be cautious about the speed at which we scale. Automation, in this case, depends on use of past data and knowledge to encode the rules of how a certain aspect of the workplace operates. The resulting software program is only as good as our own understanding of our problem and the data that we used. Before scaling from one thousand to ten thousand users, or from one geographical location to another, we need to ensure that the underlying assumptions remain the same. Rushing to scale an automation model beyond the confines of its test environment without checks in place to ensure that it is behaving as it’s sup- posed to can lead to unintended consequences, such as exhibiting bias in its decision-making or introducing errors that are hard to catch.
Tangible Outcomes Matter
An AI strategy, like any other plan, needs to take into account the wider envi- ronment within which it will develop. Too often anything new is cornered off and placed with an innovation lab where it will be studied and experimented on, but where it faces a hard time to see the light of day in the real world of the business. This is especially true of technologies like AI.
Innovation labs are not, by default, a bad idea. Organizations that can afford to set up dedicated teams to experiment are lucky, and they should fully take advantage of the possibilities. The learnings of innovation, however, need to be put up against the cold, hard light of the real situation. You are not solving
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the entire problem unless you have dealt with the entire reality of it. It’s like saying you want to build a rover to explore Mars, but you only test it on your local neighborhood roads.
An AI strategy that does not plan for how a technology is going to move from the cocoon of an innovation team to the actual business is not a complete strategy. This involves convincing stressed business units with day-to-day operational priorities that they should adopt new technologies. It involves ensuring that the solution brings measurable benefits that will make a differ- ence to a division’s budget. It involves dealing with concerns of staff that they are going to be replaced by automation, and putting in place training programs to deal with the change in the way things are done. If those elements are not there, you have no real measure of the success of the effort.
An AI strategy Is Not About AI
This heading is counterintuitive on purpose.
An AI strategy is not about using AI at all costs. I’ve seen enough companies get lost in trying to make AI work no matter what, that I feel this really needs to be driven home from the very start.
As we said repeatedly in this book, AI is useful in helping us delegate decision- making to machines. Also, as we said in the intro to this chapter, AI helps us meet user needs and expectations. AI, however, is not a goal in and of itself. The objective is never and should never be simply to “use AI.” The goal of an AI strategy is to create the necessary preconditions and processes that will allow an organization to
1. Determine whether AI techniques are applicable and can help solve problems in a better way.
2. Ensure that the organization is in a position to exploit the opportunity, provided that AI techniques are applicable.
It is perfectly OK if the outcome of the process is that the use of AI tech- niques is simply not a good idea for solving a specific problem.
This is exactly what happened in a particular conversational AI project. The organization was looking to implement a chatbot to handle queries from cli- ents who had issues with their travel documents while not in their own coun- try. Effort went into thinking about what the appropriate language understanding solution would be to identify what happened to the travel documents (lost, damaged, stolen, etc.). However, when it came to under- standing what the appropriate response should be for each type of problem, they realized that it was always the same. No matter what caused the issue, the way to deal with it always consisted of filling out the same form or getting in touch via phone for urgent cases. All the up-front effort in recognizing the
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cause of the problem was not required. This was a problem that could be resolved with improved information architecture on the web site, and did not require the use of any AI technology.
There Is No “True” AI Test to Pass
Similar to the previous principle, this one is a result of another common pit- fall. When people embark on AI projects, one of the main concerns is to do something that is “real” AI. In many ways, it’s only natural to have this con- cern. We are dealing with new technologies with definitions that are incredi- bly fluid. People feel they need to ensure they are doing the “right thing” in the face of a lot of conflicting information. Whether hiring or looking outside for help, organizations want to ensure that they are not “cheated” with “fake” AI.
The result often is that solutions to problems are overengineered, or per- fectly suitable solutions are discarded because they don’t meet some unclear “AI” test. When dealing with virtual assistants, this often involves discussions around what is a sophisticated enough conversation that feels human-like. Simple, to-the-point conversations are replaced with open-ended conversa- tions that would mimic natural language more in order to meet this human- like standard. With prediction algorithms and machine learning it is often about discarding advice that calls for the use of standard and well-established techniques from statistics in favor of techniques that involve the direct use of deep learning because deep learning feels like truer AI.
The challenge with these issues is that they are hard to deal with. The end solution works. A problem has been solved. But it is a much more brittle solu- tion because more AI technology was imposed than what was really required. Everyone feels excited because it feels more futuristic but, in reality, they are only setting themselves up for more pain as the solution evolves.
A comprehensive AI strategy needs to include checkpoints where an honest discussion is had about whether the solution is the best one for the problem at hand, or whether it is simply a solution that satisfies the need to demon- strate the use of AI as opposed to reaping the true benefits of the technology.
Decide If You Really Want It
Talking about how things ought to be is easy. Nobody can disagree with those slick diagrams that have the user at the center with concentric circles span- ning out, neatly featuring words such as collaboration, communication, and connection. Bringing about actual change is hard though. Reality very rarely matches these idealized approaches. Reality is messy, with layers of processes,
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data, tools, and people having evolved and changed over time. Reality also doesn’t take a break. You can’t hit the pause button, figure out what you want to do, put it in place, and then hit play again. Decisions will be interconnected, and the current state and future state will need constant understanding and untangling.
It’s for this reason that the biggest challenge in realizing an AI strategy (and any other strategy really) is to get firm commitment from all stakeholders that it is a mission worth going on. That includes you, the AI pioneer in your organization. Both at a personal and organizational level, there needs to be acceptance that it will be a long and complex process to get to a place where the digital workplace begins to align with what your vision and mission are. There are no simple solutions, no silver bullets, and it’s not about how much money you can throw at the problem (although obviously resources will be needed).
As such, the first real question to ask is whether you, individually, and the entire team, as a group, want to embark on the journey of changing and improving things. Depending on the organization it may start out as a lonely trip, and you may need to state your case multiple times to different stake- holders that all need to sign up for the effort. However, as cliched as it sounds, deciding that the journey is one worth taking is the most significant step.
Methods
The previous section gave us some principles to help us judge and steer our plan. The next step is to look at some more specific methods that will help us to get started and realize a plan.
Find Your Place
In developing a plan that introduces automation through AI techniques you will always end up asking three very specific questions.
1. How do we currently solve a problem? What steps do we go through to deal with an issue, and where are the rules that define the process?
2. What data do we have about the problem? What historical data do we have and in what condition is it to help us better understand the problem?
3. What current activities are taking place that would enhance or hinder our ability to use AI techniques to solve a specific problem?
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How Do We Currently Solve a Problem?
Mapping out processes is a fundamental task of any organization and there are numerous techniques that can help with that, from business process modeling to data flow modeling. It is not the task of this chapter to provide an intro- duction to those techniques. Instead, it highlights some of the issues that are relevant to understanding processes with a view of applying AI techniques to those processes.
Uncover the Real Process
One of the first lessons that most customer service support automation problem solvers learn is that the way people on the front lines deal with issues will differ from what the manual describes, and it does so for a very good reason. The manual is wrong.
The humans, as the intelligent and extremely adaptive beings that they are, have figured out all the shortcuts, hacks, and workarounds to the processes to make them actually work. They’ve crossed out the wrong information in the manual, stuck a post-it note on their screen with the fix and moved on. When we attempt to introduce automation, we need to ensure that that knowledge is captured and included in the automated process.
Automation means spending time with the people actually doing the tasks, to learn what the real process looks like right now. This will give invaluable infor- mation about what can effectively be fully automated and what can be done to augment and assist the people involved in the process.
Simulate Processes with Humans
Whether attempting to automate an existing process or looking to introduce a new one, it is useful to consider whether you can simulate the process: put simply, whether you can fake it using humans. As tedious as it may seem, I would advise you to get a volunteer who will be that process to start with. Have a human sit behind a keyboard and pretend to be an automated procure- ment information service answering questions about the status of various invoices from across the organization.
This gives you invaluable information about how people will interact with an automated service without having to build the service itself. Those interac- tions can in turn inform how the service gets developed and uncover issues around the integration of the service into the organization. Dedicating a few days to learn how people are likely to use the service is the most cost-effec- tive way of doing it.
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Don’t Ignore Experience
Of course, not every solution can be simulated by a human being. If you want to identify the best sales prospects to contact based on data analysis of infor- mation in your CRM, you will have to do that data analysis. You should, how- ever, introduce the subject matter experts as soon as possible, to interpret the data and see if their intuition matches what the data is saying or whether there is a significant mismatch. If there is a mismatch it needs to be examined. It doesn’t mean the data is wrong, or the experts are wrong. You simply need to recognize that data analysis will uncover correlations, but not all correla- tions translate to actual valid causal effects. An expert will be able to smoke out some of the more obvious misleading results.
What Data Do We Have?
We’ve already discussed data in the previous chapter. One issue we left out, though, is mapping out what data is currently there. To provide a simple framework for describing your data, I will borrow from ideas that were devel- oped by the open data movement. Tim Berners-Lee (yes, that Tim—the inventor of the Web) suggested a five-star scheme for describing open data4 to share publicly. However, it is also a useful guide for describing data within an organization, especially as we think of data being shared between different groups and departments.
One-Star—Data Available for Use in Whatever Format
The first step is to simply have data available for use in whatever format is possible, in a way that is accessible to the wider organization. The test for one-star data within an organization is that people can actually find it and are able to trace who is responsible for it and what rules govern access to it. The data is very likely to be unstructured data in PDF documents, but at least it is findable. You will need to deploy more heavyweight techniques such as text mining to extract data.
Two-Star—Data Available in a Structured Format
A step up is to have this findable and attributable data in a structured format that is more machine friendly. It could be an Excel spreadsheet, for example, as opposed to a scan of a table. Structured data means that it is easier to get to, but you may still be dealing with file formats that are not in use by any software right now or where the schema behind them is not well understood or documented.
4 https://5stardata.info/en/.
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Three-Star—Data Available in a Well-Understood Structured
Format
Three-star data is data that you can access in a well-understood structured format. We may be dealing with a CSV (comma-separated values) file or a database table. You will still need to uncover information around the schema and access to the data.
Four-Star—Data Available via Documented API
In this case we are dealing with structured data available via a documented and well-maintained API (application programming interface). This indicates that there is a team on the other end that is curating access to the data and that more well-thought out data governance processes are in place.
Five-Star—Data Linked Across Sets to Provide Context
With five-star data we are not only able to gain access via a well-documented API, that data is interlinked to other data sets within the organization, allow- ing us to make more interesting inferences about the context of the data.
Discovering and ranking datasets provides a map that can indicate where the best starting point is. We can start from where data is of the best quality to prove the value of AI-powered automation and motivate the improvement of the rest of our datasets.
Connect Activities
The development and execution of an AI strategy should not be viewed as something that happens in isolation to other activities. It is crucial that it informs thought from the very start. You could view AI capabilities as simply a toolbox you reach into and pull out useful tools to help solve problems as they appear. You could argue that since AI is a technology, a way of solving a problem, it doesn’t need to feature when defining overarching strategies. Only once you get to the point where you need to build a solution do you start exploring the space of AI techniques and capabilities to see what applies to your problem.
I think that approach is flawed. It fails to capitalize on one of the most signifi- cant aspects of strategy: the orchestration of activities so that the whole is greater than the sum of its parts. An AI strategy cannot and should not stand in isolation from your wider digital strategy, which should be connected to your overall strategy. Each supports the other and lays the groundwork for the whole to succeed. If you view AI as simply another tool you can apply, you miss out on defining strategies that are only possible because of the capabili- ties of AI.
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■ It is crucial to connect your overall strategy to your digital strategy and your AI strategy.
Each informs the other and enables objectives and courses of action that would not be possible if the different aspects were dealt with in isolation.
At a more mundane level, connecting activities also means determining how to best time and coordinate projects so that you get a positive outcome over- all. A typical example of not doing this is that as one group within an organiza- tion is working to develop capabilities for automated prediction, the software that produces the data that that prediction depends on is already planned to be replaced—the equivalent of pulling the carpet under the feet of the first team.
You want to avoid conversations like the one below:
“- The new CRM project is well underway—the new system will be up and running by early next year.”
“- Will the new CRM be able to supply the relation- ship data and historical sales data that we need to enable prediction?”
“- I don’t know. That wasn’t part of the requirements a year ago when the request for proposals went out to vendors.”
“…”
Getting a firm grasp on process, data flows, and activities that will influence these is crucial for coordinating a successful AI implementation. It does require effort at the planning stage and it points to the need for wider stake- holder participation so that everyone is aware of how changes will affect activities across the team.
Build Your Roadmap
Once we have a better grasp of where we are, we can start planning out the steps that will take us to where we want to be. At the very highest level I find it useful to consider three broad possibilities. In part, these three approaches can be viewed as stages along the evolution of your AI capabilities, but ulti- mately they are three streams that you can follow concurrently and at times you can decide to move from one to the other. These three streams are:
• Hire tools with AI built in. In this case we are looking for tools with AI capabilities already built in. We don’t need to develop anything from scratch. Simply take advantage of what is available.
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• Build solutions with prebuilt AI components. Here we are developing our own custom solutions, but when it comes to using AI techniques or capabilities, we don’t train or develop our own models. Instead we use AI ser- vices that are pretrained or in some way prepackaged to give us the functionality we need.
• Build AI components and infrastructure. Finally, we can consider building our own AI models to include within wider solutions. This step could be further divided into building AI models using existing techniques or developing new techniques to help us derive models.
We will consider each of the options in more detail in the next sections.
Hire Tools with AI Built In
A very straightforward choice is to “hire” services with AI capabilities built in. This provides an immediate step on the AI evolution ladder without having to dedicate significant resources to build something internally. There are thou- sands of vendors vying to provide intelligent capabilities to businesses, and taking advantage of this innovation is a great way to see how AI capabilities can enhance your current workflows.
From a practical, implementation perspective it means adding another dimen- sion to your purchasing decisions, whereby you explicitly evaluate the possi- bilities that a service creates around automation and how those possibilities can address your needs. There are two key questions to be considered:
Is it addressing a real need within the organization? Is it solving a real problem we are facing that would benefit from automation? This seems like an obvious question, but it protects against “checklist purchasing”—where purchase decisions are done by a separate department within an organization, and all that department is looking to do is tick off the “AI capabilities” on their list. As we have already said numerous times in this book, AI techniques vary greatly. In many ways simply specifying “AI capabilities” is about as useful as specifying that a computer program should use a programming language! Instead, we have to address the specific capability or set of capabilities we are looking for with respect to the problem we are trying to solve.
How is the underlying data treated? Will you be able to use the data without that product? Will data produced be able to be fed back in the virtuous cycle we described at the start of this chapter? I believe this is crucial. Going back to the data rating scheme we described earlier, we can judge what type of data will be produced from the system we are hiring and what level of lock-in to a specific vendor this creates. The understanding generated through your own activities is valuable intellectual property that ideally should be closely
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guarded. In a situation where all competitors are using the same tool, it is the data and process configuration of that tool that can give you a competitive advantage.
All the major vendors offer interesting solutions that allow you to “hire” solu- tions with AI built-in. For example, the major CRM vendors (Salesforce, Oracle, SAP, Adobe, Microsoft) have all bundled AI capabilities within their CRM tools. Let’s briefly consider what Salesforce has done, as it is a real-life example of the type of tactics we are discussing here. The first step was to recognize that offering AI capabilities within their CRM would represent a key competitive advantage and potential differentiator. Then, in order to quickly build up their AI capabilities they went on an acquiring spree, purchasing AI startups that either provided specific functionality such as intelligent meeting management (so tools with AI built in). Then they purchased companies that borough lower- level techniques into the mix so that they can, for example, create a machine learning platform - enabling building with AI. All these startups were eventually combined into a comprehensive solution called Einstein, enabling Salesforce to build new AI techniques and capabilities. Einstein provides features such as accounts insights, leads prioritization, and automated data entry.
Build with AI
The next approach to take is to build solutions using easily accessible AI com- ponents. In this case you are not hiring the final functionality with its pre- defined UI and feature list outright. Instead you are building your own tool (say a dashboard to be able to view potential candidates ranked) and you are using an external AI platform to provide you with the necessary capabilities (e.g., natural language processing).
Once more, all the major technology providers offer easily accessible APIs along these lines. Microsoft’s cognitive services include tools to enable Decision, Vision, Speech, Search, and Language. Amazon AWS calls theirs Recommendations, Forecasting, Image and Video Analysis, Advanced Text Analytics, Document Analysis, etc. As you can see just by the names of the services, these are broad capabilities that can be fed with your own data and composed to provide more comprehensive solutions
Of course it’s not just about cloud-based services from the big providers. There is a wealth of open source tools such as spaCy for natural language understanding that provide incredible capabilities and require little effort to get started.
The appeal of these ready-made capabilities is that you can plug into your solutions without requiring specialist in-house skills to understand them. Your differentiator, once more, is in how you compose the solution and the quality of the data and overall problem understanding that you bring to the table.
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Build AI
The last step is to actually invest in building basic AI internally. This makes sense once you have more clarity about the state of your processes and data and can start building your own models, combining foundational techniques such as neural networks, reasoning, etc. It will mean building out AI skills in- house and requires a bigger investment, but it is also the space that is likely to bring the most interesting returns because this is the area that you can truly differentiate what you do. The balancing act to handle here is ensuring that you are investing in the right direction in building your own AI and are not simply trying to compete with technology behemoths.
AI Everywhere
Developing and defining your own AI strategy, as with any high-level strategic work, is a challenging but ultimately highly rewarding activity. It means that you will need to dig deep into understanding your own motivations and the motivations of your colleagues and organization as a whole. It means looking at how you solve problems and attempting to derive explicit rules and clarity. That process alone is incredibly valuable. It is one thing to look at processes for the purpose of documenting them for other humans and quite another to look at that same process and attempt to describe it to a machine. It forces a level of clarity that at times may even feel uncomfortable or awkward. The outcome, however, will undoubtedly be very valuable.
We’ve seen that there are quite a few options about how to get started on the journey from hiring AI, building with AI, and building AI. Each has its own advantages and disadvantages and while they may feel like different steps along an evolutionary ladder, they are not mutually exclusive. They can coexist, and you can make different choices for different use cases within your organiza- tion. It also means that you can get started quickly and you can start showing the benefits of an AI strategy early on, which in turn will fuel further support and make the next steps easier to sell to the rest of the team.
AI techniques and capabilities will influence every aspect of the workplace. I hope I have demonstrated throughout this book that I am not one to get excited by fads and hype. Given how hyped AI technologies are at this point in time, it may be hard to see through that to what their real impact can be. However, as we argued in the first chapters, the impact of AI will be far- reaching. Comparing it to fire and electricity (as companies such as Microsoft and Google are doing) may sound far-fetched. There is truth in those state- ments though. Just like fire or electricity, AI has the ability to change how we do everything. If you start from a position that is pragmatic about the chal- lenges but also recognizes the opportunities, you can make a lasting impact on how work is done in your organization. Getting started on an AI strategy may be one of the most significant decisions for the future of your organization.
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Chapter 11 | Defining an AI Strategy
Mobile-first is simply one of the sensible pillars of anything digital and not a huge achievement. There is no need to have it be the defining, outward facing strategic statement of the company. It’s business as usual.
With the 2017 AI-first announcement, Google was recognizing publicly that there was a new opportunity on the horizon and new challenges to overcome. The opportunity revolves, once more, around meeting users’ expectations and demands. Users are now savvy digital citizens. We all expect a whole new level of sophistication from our devices, especially as we catch glimpses of how things can work better and are no longer mystified by digital services. It’s not enough to simply have access to the service from any device or for that service to be reliable. The more technologically savvy tech companies are demonstrat- ing how smart interconnected services can be and that, in turn, leads us to want all our services to work in the same way. When you can order any meal and have it show up at your front door in 20 minutes or buy any item and have it delivered that same day, it creates a certain set of expectations. This in turn creates a feedback loop that forces companies to upgrade everything else that they do. To stay ahead of the competition and satisfy users, services have to be far more aware of our context and our needs and react appropriately to them, or even proactively anticipate them. If an app doesn’t “get it” we are quick to call it useless, complain in frustration, and move on to the next one. The path to more user-friendly services though is paved with AI techniques. This is what an AI-first strategy means: recognizing that the next evolution of your products and services will depend heavily on your ability to enhance them through the use of AI techniques. It also acknowledges that integrating AI techniques will require concentrated effort and focus, to overcome some of the challenges that will inevitably present themselves. It is not business as usual.
■ An AI-first strategy acknowledges that the path to smarter and better services depends on an organization’s ability to effectively exploit AI techniques and capabilities.
It’s not just Google, of course. Every major technology company has a signifi- cant AI effort underway, transforming both how it does work internally and the products it offers. IBM has been promoting the IBM Watson brand since
2010, even briefly turning it into a household name in 2011 when it won the Jeopardy! TV game show in the United States. SAP has the Leonardo platform, what they call an all-encompassing digital innovation system with machine learning and other AI techniques at its core. Salesforce has Einstein, an AI platform that weaves AI across its CRM offerings. Amazon is arguably as advanced as Google in considering how AI can transform every single aspect of their business. With Apple the strategy is all-encompassing, with services being enhanced through AI and their hardware evolving to better support AI through dedicated chips able to speed up AI-specific computations on devices.
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One can’t help but feel somewhat overwhelmed by the efforts of the technol- ogy behemoths. The effort has reached such a fever pitch that universities are complaining that they can’t staff their own AI research groups because com- panies are hiring people as fast as they can find them.1
How can other organizations develop their own thinking around AI in straight- forward terms? Are we all supposed to move to an AI-first strategy and join the race? Do we need to start hiring AI-experts?
One of the main aims of this chapter is to give you a practical approach to developing your own AI strategy. It’s not about joining the AI race (unless there are extremely good reasons to do so). It’s about identifying the princi- ples to adhere to and tactics to apply at different stages and for different needs for your particular journey, so that you make sure you are making the most out of what AI technologies can offer your business.
A Practical Approach to AI Strategy
“[Strategy is] strength applied to the most promising opportunity.”
—Richard P. Rumelt, Good Strategy Bad Strategy2
Developing a strategy for anything is an incredibly challenging task. The only information you have to act on is what has happened in the past. Everything that will happen in the future is, ultimately, a guess. In the same way AI tech- niques work, a strategy depends on you having a model of how the world works, based on the information you’ve had so far, and devising a set of rules that will allow you to make a prediction about the future. However, unlike the problems AI can currently help us solve, you need to deal with an almost unlimited set of variables. To make matters worse, there is no one-size-fits-all solution. You have to craft the strategy that is right for your organization, and the best strategy for your team will, almost by definition, be an unsuitable strategy for a different place.
As such, there is no single true recipe that will lead to your ideal AI strategy. Only you know what the real ingredients are. What I can do is talk about general principles, ways to prepare and specific methods to apply. Like a chef in a kitchen presented with a mystery box of ingredients, it is your task to open the box, figure out what you have available, and make the best possible use of each ingredient.
1 An iconic example of this was when Uber gutted the CMU Robotics lab by hiring 40 of the lab’s top researchers (out of 100), including the director.
2 Crown Business, 2011.
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Principles
Principles for a strategy act as guiding stars to help us choose one direction rather than another when the ground truths don’t clearly point to a choice. The principles I present here are the distillation of numerous conversations with large and small organizations about what has and hasn’t worked for them, and what is important to focus on and what instead is more of a distraction.
Think Big, Start Realistically, Scale Appropriately
Those of you familiar with the lean startup movement will see the seeds of that movement’s mantra in the heading. It usually goes: “think big, start small, scale fast.” The lean startup movement was started by Eric Reis, who, through his book The Lean Startup3 described a methodology for doing business that embraces uncertainty and minimizes risk by testing the viability of business models through the execution of carefully managed and cost-effective experi- ments. The experiments are designed to test the most critical hypothesis behind a new feature or product before scaling that product to full-blown production. For lean startupers, it is important to have a big far-reaching vision but then start with small experiments that are not that costly, before moving on to scaling as fast as possible in order to capture market value. A strategy for applying AI techniques to your workplace can benefit from the same sort of thinking. At the same time, there are a couple of things to keep in mind—which is why I distorted the mantra to “Think Big, Start Realistically, Scale Appropriately.”
Think Big
The potential of the application of AI techniques to how we do work is trans- formational. It is necessary to think big in order to understand the true scope and assign it appropriate importance. Just like Google with their public AI-first strategy statement, it is about giving the entire mission enough importance so that people pay attention and prioritize it appropriately.
Thinking big is also about recognizing that automation is not just about more of the same but at a lower cost or higher speed. Don’t think of the introduc- tion of automation in a business as akin to a factory line, where component A can be attached to component B in a faster and less error-prone way. Automation through AI also enables entire new ways of doing things. It enables new business models. It is a circle whereby the need for automation leads to digitalization of information and process, which in turn generates
3 Eric Reis, The Lean Startup (New York: Crown Publishing, 2011).
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more information that can be further exploited and leads to completely new ways of solving a problem.
For example, automating support handling doesn’t just mean that you will need fewer resources to deal with the same amount of support requests. It also means that you can better understand support issues as each support request is carefully analyzed by increasingly more sophisticated natural lan- guage-understanding capabilities. The resulting data can be fed directly into product development, and the results of product improvements can be traced back to the types of questions that come through support after a release. It means that you can evolve your support teams to become customer success teams that engage with clients in a completely different way. The introduction of automation in support handling can affect the business all the way through to how product design and development take place and customer relation- ships are managed. However, to realize the full benefits of the effort, you need the big vision version so that you put in place the right stepping stones at each phase to be able to get to the top of the hill.
Figure 11-1 illustrates this flow for any process. We start from a place with historical data and understanding of our processes (1) and do the work to augment our standard processes through automation (2). This, if done care- fully and with a mind to the future, can lead to enhanced data and understand- ing (3). This enhanced understanding starts creating a virtuous feedback loop that can go back into augmenting existing processes (2) but also into creating additional opportunities (4) and even new processes (5).
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Figure 11-1. The AI virtuous process enhancment flow
Start (and Evolve) Realistically
Applying new technologies always involves a high amount of risk. It is not busi- ness as usual. You need to be able to absorb and react to a lot of learnings, and things will not turn out as you would expect them. The lean startup approach has made it popular for people to push to start with small experi- ments (minimum viable products) that will test a key hypothesis before com- mitting additional resources. Overall, that’s a very sensible approach. The crucial question, of course, is exactly how minimal a minimum viable product can be. It has to be enough to give you information about whether it is worth scaling the entire effort. If the product is too simple and too minimal, it is not going to be useful.
With AI techniques we are in a very similar situation. If you are attempting to deploy a prediction algorithm, you need to ensure that you are giving enough space for people to prepare enough data and try enough approaches to have a firm understanding of whether the problem can be solved. Think of it as the equivalent of the escape velocity for a space launch. Engineers know that unless a launch missile is able to develop a velocity of 25,000 km/h it will not
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be able to escape the earth’s gravitational pull. If you know that your invest- ment in engineering will not be enough to surpass that minimum threshold, you are better off spending that money elsewhere altogether.
Now, recognizing where that threshold lies requires a combination of experi- ence and willingness to have some false starts. As such, I believe that there are two key tests that a plan must pass. The first is to ensure that the individual steps in the roadmap have enough clarity that there can be some preliminary research into their feasibility, and that no step is so big that if it fails there is no space for a change of direction. The second is to ensure that each step is fed the appropriate level of inputs, a sort of readiness gate that ensures you are doing enough for it to be useful without doing too much. If coming out of one step you do not meet the readiness gate to move into the next step, you can stop and reconsider.
Scale Appropriately
We, of course, want to scale fast. A large part of the appeal of automated decision-making is that it can enable organizations to move much faster.
However, it is precisely because of the ability of automation to scale quickly and the nature of the automation taking place that we need to be cautious about the speed at which we scale. Automation, in this case, depends on use of past data and knowledge to encode the rules of how a certain aspect of the workplace operates. The resulting software program is only as good as our own understanding of our problem and the data that we used. Before scaling from one thousand to ten thousand users, or from one geographical location to another, we need to ensure that the underlying assumptions remain the same. Rushing to scale an automation model beyond the confines of its test environment without checks in place to ensure that it is behaving as it’s sup- posed to can lead to unintended consequences, such as exhibiting bias in its decision-making or introducing errors that are hard to catch.
Tangible Outcomes Matter
An AI strategy, like any other plan, needs to take into account the wider envi- ronment within which it will develop. Too often anything new is cornered off and placed with an innovation lab where it will be studied and experimented on, but where it faces a hard time to see the light of day in the real world of the business. This is especially true of technologies like AI.
Innovation labs are not, by default, a bad idea. Organizations that can afford to set up dedicated teams to experiment are lucky, and they should fully take advantage of the possibilities. The learnings of innovation, however, need to be put up against the cold, hard light of the real situation. You are not solving
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the entire problem unless you have dealt with the entire reality of it. It’s like saying you want to build a rover to explore Mars, but you only test it on your local neighborhood roads.
An AI strategy that does not plan for how a technology is going to move from the cocoon of an innovation team to the actual business is not a complete strategy. This involves convincing stressed business units with day-to-day operational priorities that they should adopt new technologies. It involves ensuring that the solution brings measurable benefits that will make a differ- ence to a division’s budget. It involves dealing with concerns of staff that they are going to be replaced by automation, and putting in place training programs to deal with the change in the way things are done. If those elements are not there, you have no real measure of the success of the effort.
An AI strategy Is Not About AI
This heading is counterintuitive on purpose.
An AI strategy is not about using AI at all costs. I’ve seen enough companies get lost in trying to make AI work no matter what, that I feel this really needs to be driven home from the very start.
As we said repeatedly in this book, AI is useful in helping us delegate decision- making to machines. Also, as we said in the intro to this chapter, AI helps us meet user needs and expectations. AI, however, is not a goal in and of itself. The objective is never and should never be simply to “use AI.” The goal of an AI strategy is to create the necessary preconditions and processes that will allow an organization to
1. Determine whether AI techniques are applicable and can help solve problems in a better way.
2. Ensure that the organization is in a position to exploit the opportunity, provided that AI techniques are applicable.
It is perfectly OK if the outcome of the process is that the use of AI tech- niques is simply not a good idea for solving a specific problem.
This is exactly what happened in a particular conversational AI project. The organization was looking to implement a chatbot to handle queries from cli- ents who had issues with their travel documents while not in their own coun- try. Effort went into thinking about what the appropriate language understanding solution would be to identify what happened to the travel documents (lost, damaged, stolen, etc.). However, when it came to under- standing what the appropriate response should be for each type of problem, they realized that it was always the same. No matter what caused the issue, the way to deal with it always consisted of filling out the same form or getting in touch via phone for urgent cases. All the up-front effort in recognizing the
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cause of the problem was not required. This was a problem that could be resolved with improved information architecture on the web site, and did not require the use of any AI technology.
There Is No “True” AI Test to Pass
Similar to the previous principle, this one is a result of another common pit- fall. When people embark on AI projects, one of the main concerns is to do something that is “real” AI. In many ways, it’s only natural to have this con- cern. We are dealing with new technologies with definitions that are incredi- bly fluid. People feel they need to ensure they are doing the “right thing” in the face of a lot of conflicting information. Whether hiring or looking outside for help, organizations want to ensure that they are not “cheated” with “fake” AI.
The result often is that solutions to problems are overengineered, or per- fectly suitable solutions are discarded because they don’t meet some unclear “AI” test. When dealing with virtual assistants, this often involves discussions around what is a sophisticated enough conversation that feels human-like. Simple, to-the-point conversations are replaced with open-ended conversa- tions that would mimic natural language more in order to meet this human- like standard. With prediction algorithms and machine learning it is often about discarding advice that calls for the use of standard and well-established techniques from statistics in favor of techniques that involve the direct use of deep learning because deep learning feels like truer AI.
The challenge with these issues is that they are hard to deal with. The end solution works. A problem has been solved. But it is a much more brittle solu- tion because more AI technology was imposed than what was really required. Everyone feels excited because it feels more futuristic but, in reality, they are only setting themselves up for more pain as the solution evolves.
A comprehensive AI strategy needs to include checkpoints where an honest discussion is had about whether the solution is the best one for the problem at hand, or whether it is simply a solution that satisfies the need to demon- strate the use of AI as opposed to reaping the true benefits of the technology.
Decide If You Really Want It
Talking about how things ought to be is easy. Nobody can disagree with those slick diagrams that have the user at the center with concentric circles span- ning out, neatly featuring words such as collaboration, communication, and connection. Bringing about actual change is hard though. Reality very rarely matches these idealized approaches. Reality is messy, with layers of processes,
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data, tools, and people having evolved and changed over time. Reality also doesn’t take a break. You can’t hit the pause button, figure out what you want to do, put it in place, and then hit play again. Decisions will be interconnected, and the current state and future state will need constant understanding and untangling.
It’s for this reason that the biggest challenge in realizing an AI strategy (and any other strategy really) is to get firm commitment from all stakeholders that it is a mission worth going on. That includes you, the AI pioneer in your organization. Both at a personal and organizational level, there needs to be acceptance that it will be a long and complex process to get to a place where the digital workplace begins to align with what your vision and mission are. There are no simple solutions, no silver bullets, and it’s not about how much money you can throw at the problem (although obviously resources will be needed).
As such, the first real question to ask is whether you, individually, and the entire team, as a group, want to embark on the journey of changing and improving things. Depending on the organization it may start out as a lonely trip, and you may need to state your case multiple times to different stake- holders that all need to sign up for the effort. However, as cliched as it sounds, deciding that the journey is one worth taking is the most significant step.
Methods
The previous section gave us some principles to help us judge and steer our plan. The next step is to look at some more specific methods that will help us to get started and realize a plan.
Find Your Place
In developing a plan that introduces automation through AI techniques you will always end up asking three very specific questions.
1. How do we currently solve a problem? What steps do we go through to deal with an issue, and where are the rules that define the process?
2. What data do we have about the problem? What historical data do we have and in what condition is it to help us better understand the problem?
3. What current activities are taking place that would enhance or hinder our ability to use AI techniques to solve a specific problem?
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How Do We Currently Solve a Problem?
Mapping out processes is a fundamental task of any organization and there are numerous techniques that can help with that, from business process modeling to data flow modeling. It is not the task of this chapter to provide an intro- duction to those techniques. Instead, it highlights some of the issues that are relevant to understanding processes with a view of applying AI techniques to those processes.
Uncover the Real Process
One of the first lessons that most customer service support automation problem solvers learn is that the way people on the front lines deal with issues will differ from what the manual describes, and it does so for a very good reason. The manual is wrong.
The humans, as the intelligent and extremely adaptive beings that they are, have figured out all the shortcuts, hacks, and workarounds to the processes to make them actually work. They’ve crossed out the wrong information in the manual, stuck a post-it note on their screen with the fix and moved on. When we attempt to introduce automation, we need to ensure that that knowledge is captured and included in the automated process.
Automation means spending time with the people actually doing the tasks, to learn what the real process looks like right now. This will give invaluable infor- mation about what can effectively be fully automated and what can be done to augment and assist the people involved in the process.
Simulate Processes with Humans
Whether attempting to automate an existing process or looking to introduce a new one, it is useful to consider whether you can simulate the process: put simply, whether you can fake it using humans. As tedious as it may seem, I would advise you to get a volunteer who will be that process to start with. Have a human sit behind a keyboard and pretend to be an automated procure- ment information service answering questions about the status of various invoices from across the organization.
This gives you invaluable information about how people will interact with an automated service without having to build the service itself. Those interac- tions can in turn inform how the service gets developed and uncover issues around the integration of the service into the organization. Dedicating a few days to learn how people are likely to use the service is the most cost-effec- tive way of doing it.
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Don’t Ignore Experience
Of course, not every solution can be simulated by a human being. If you want to identify the best sales prospects to contact based on data analysis of infor- mation in your CRM, you will have to do that data analysis. You should, how- ever, introduce the subject matter experts as soon as possible, to interpret the data and see if their intuition matches what the data is saying or whether there is a significant mismatch. If there is a mismatch it needs to be examined. It doesn’t mean the data is wrong, or the experts are wrong. You simply need to recognize that data analysis will uncover correlations, but not all correla- tions translate to actual valid causal effects. An expert will be able to smoke out some of the more obvious misleading results.
What Data Do We Have?
We’ve already discussed data in the previous chapter. One issue we left out, though, is mapping out what data is currently there. To provide a simple framework for describing your data, I will borrow from ideas that were devel- oped by the open data movement. Tim Berners-Lee (yes, that Tim—the inventor of the Web) suggested a five-star scheme for describing open data4 to share publicly. However, it is also a useful guide for describing data within an organization, especially as we think of data being shared between different groups and departments.
One-Star—Data Available for Use in Whatever Format
The first step is to simply have data available for use in whatever format is possible, in a way that is accessible to the wider organization. The test for one-star data within an organization is that people can actually find it and are able to trace who is responsible for it and what rules govern access to it. The data is very likely to be unstructured data in PDF documents, but at least it is findable. You will need to deploy more heavyweight techniques such as text mining to extract data.
Two-Star—Data Available in a Structured Format
A step up is to have this findable and attributable data in a structured format that is more machine friendly. It could be an Excel spreadsheet, for example, as opposed to a scan of a table. Structured data means that it is easier to get to, but you may still be dealing with file formats that are not in use by any software right now or where the schema behind them is not well understood or documented.
4 https://5stardata.info/en/.
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Three-Star—Data Available in a Well-Understood Structured
Format
Three-star data is data that you can access in a well-understood structured format. We may be dealing with a CSV (comma-separated values) file or a database table. You will still need to uncover information around the schema and access to the data.
Four-Star—Data Available via Documented API
In this case we are dealing with structured data available via a documented and well-maintained API (application programming interface). This indicates that there is a team on the other end that is curating access to the data and that more well-thought out data governance processes are in place.
Five-Star—Data Linked Across Sets to Provide Context
With five-star data we are not only able to gain access via a well-documented API, that data is interlinked to other data sets within the organization, allow- ing us to make more interesting inferences about the context of the data.
Discovering and ranking datasets provides a map that can indicate where the best starting point is. We can start from where data is of the best quality to prove the value of AI-powered automation and motivate the improvement of the rest of our datasets.
Connect Activities
The development and execution of an AI strategy should not be viewed as something that happens in isolation to other activities. It is crucial that it informs thought from the very start. You could view AI capabilities as simply a toolbox you reach into and pull out useful tools to help solve problems as they appear. You could argue that since AI is a technology, a way of solving a problem, it doesn’t need to feature when defining overarching strategies. Only once you get to the point where you need to build a solution do you start exploring the space of AI techniques and capabilities to see what applies to your problem.
I think that approach is flawed. It fails to capitalize on one of the most signifi- cant aspects of strategy: the orchestration of activities so that the whole is greater than the sum of its parts. An AI strategy cannot and should not stand in isolation from your wider digital strategy, which should be connected to your overall strategy. Each supports the other and lays the groundwork for the whole to succeed. If you view AI as simply another tool you can apply, you miss out on defining strategies that are only possible because of the capabili- ties of AI.
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■ It is crucial to connect your overall strategy to your digital strategy and your AI strategy.
Each informs the other and enables objectives and courses of action that would not be possible if the different aspects were dealt with in isolation.
At a more mundane level, connecting activities also means determining how to best time and coordinate projects so that you get a positive outcome over- all. A typical example of not doing this is that as one group within an organiza- tion is working to develop capabilities for automated prediction, the software that produces the data that that prediction depends on is already planned to be replaced—the equivalent of pulling the carpet under the feet of the first team.
You want to avoid conversations like the one below:
“- The new CRM project is well underway—the new system will be up and running by early next year.”
“- Will the new CRM be able to supply the relation- ship data and historical sales data that we need to enable prediction?”
“- I don’t know. That wasn’t part of the requirements a year ago when the request for proposals went out to vendors.”
“…”
Getting a firm grasp on process, data flows, and activities that will influence these is crucial for coordinating a successful AI implementation. It does require effort at the planning stage and it points to the need for wider stake- holder participation so that everyone is aware of how changes will affect activities across the team.
Build Your Roadmap
Once we have a better grasp of where we are, we can start planning out the steps that will take us to where we want to be. At the very highest level I find it useful to consider three broad possibilities. In part, these three approaches can be viewed as stages along the evolution of your AI capabilities, but ulti- mately they are three streams that you can follow concurrently and at times you can decide to move from one to the other. These three streams are:
• Hire tools with AI built in. In this case we are looking for tools with AI capabilities already built in. We don’t need to develop anything from scratch. Simply take advantage of what is available.
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• Build solutions with prebuilt AI components. Here we are developing our own custom solutions, but when it comes to using AI techniques or capabilities, we don’t train or develop our own models. Instead we use AI ser- vices that are pretrained or in some way prepackaged to give us the functionality we need.
• Build AI components and infrastructure. Finally, we can consider building our own AI models to include within wider solutions. This step could be further divided into building AI models using existing techniques or developing new techniques to help us derive models.
We will consider each of the options in more detail in the next sections.
Hire Tools with AI Built In
A very straightforward choice is to “hire” services with AI capabilities built in. This provides an immediate step on the AI evolution ladder without having to dedicate significant resources to build something internally. There are thou- sands of vendors vying to provide intelligent capabilities to businesses, and taking advantage of this innovation is a great way to see how AI capabilities can enhance your current workflows.
From a practical, implementation perspective it means adding another dimen- sion to your purchasing decisions, whereby you explicitly evaluate the possi- bilities that a service creates around automation and how those possibilities can address your needs. There are two key questions to be considered:
Is it addressing a real need within the organization? Is it solving a real problem we are facing that would benefit from automation? This seems like an obvious question, but it protects against “checklist purchasing”—where purchase decisions are done by a separate department within an organization, and all that department is looking to do is tick off the “AI capabilities” on their list. As we have already said numerous times in this book, AI techniques vary greatly. In many ways simply specifying “AI capabilities” is about as useful as specifying that a computer program should use a programming language! Instead, we have to address the specific capability or set of capabilities we are looking for with respect to the problem we are trying to solve.
How is the underlying data treated? Will you be able to use the data without that product? Will data produced be able to be fed back in the virtuous cycle we described at the start of this chapter? I believe this is crucial. Going back to the data rating scheme we described earlier, we can judge what type of data will be produced from the system we are hiring and what level of lock-in to a specific vendor this creates. The understanding generated through your own activities is valuable intellectual property that ideally should be closely
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guarded. In a situation where all competitors are using the same tool, it is the data and process configuration of that tool that can give you a competitive advantage.
All the major vendors offer interesting solutions that allow you to “hire” solu- tions with AI built-in. For example, the major CRM vendors (Salesforce, Oracle, SAP, Adobe, Microsoft) have all bundled AI capabilities within their CRM tools. Let’s briefly consider what Salesforce has done, as it is a real-life example of the type of tactics we are discussing here. The first step was to recognize that offering AI capabilities within their CRM would represent a key competitive advantage and potential differentiator. Then, in order to quickly build up their AI capabilities they went on an acquiring spree, purchasing AI startups that either provided specific functionality such as intelligent meeting management (so tools with AI built in). Then they purchased companies that borough lower- level techniques into the mix so that they can, for example, create a machine learning platform - enabling building with AI. All these startups were eventually combined into a comprehensive solution called Einstein, enabling Salesforce to build new AI techniques and capabilities. Einstein provides features such as accounts insights, leads prioritization, and automated data entry.
Build with AI
The next approach to take is to build solutions using easily accessible AI com- ponents. In this case you are not hiring the final functionality with its pre- defined UI and feature list outright. Instead you are building your own tool (say a dashboard to be able to view potential candidates ranked) and you are using an external AI platform to provide you with the necessary capabilities (e.g., natural language processing).
Once more, all the major technology providers offer easily accessible APIs along these lines. Microsoft’s cognitive services include tools to enable Decision, Vision, Speech, Search, and Language. Amazon AWS calls theirs Recommendations, Forecasting, Image and Video Analysis, Advanced Text Analytics, Document Analysis, etc. As you can see just by the names of the services, these are broad capabilities that can be fed with your own data and composed to provide more comprehensive solutions
Of course it’s not just about cloud-based services from the big providers. There is a wealth of open source tools such as spaCy for natural language understanding that provide incredible capabilities and require little effort to get started.
The appeal of these ready-made capabilities is that you can plug into your solutions without requiring specialist in-house skills to understand them. Your differentiator, once more, is in how you compose the solution and the quality of the data and overall problem understanding that you bring to the table.
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Build AI
The last step is to actually invest in building basic AI internally. This makes sense once you have more clarity about the state of your processes and data and can start building your own models, combining foundational techniques such as neural networks, reasoning, etc. It will mean building out AI skills in- house and requires a bigger investment, but it is also the space that is likely to bring the most interesting returns because this is the area that you can truly differentiate what you do. The balancing act to handle here is ensuring that you are investing in the right direction in building your own AI and are not simply trying to compete with technology behemoths.
AI Everywhere
Developing and defining your own AI strategy, as with any high-level strategic work, is a challenging but ultimately highly rewarding activity. It means that you will need to dig deep into understanding your own motivations and the motivations of your colleagues and organization as a whole. It means looking at how you solve problems and attempting to derive explicit rules and clarity. That process alone is incredibly valuable. It is one thing to look at processes for the purpose of documenting them for other humans and quite another to look at that same process and attempt to describe it to a machine. It forces a level of clarity that at times may even feel uncomfortable or awkward. The outcome, however, will undoubtedly be very valuable.
We’ve seen that there are quite a few options about how to get started on the journey from hiring AI, building with AI, and building AI. Each has its own advantages and disadvantages and while they may feel like different steps along an evolutionary ladder, they are not mutually exclusive. They can coexist, and you can make different choices for different use cases within your organiza- tion. It also means that you can get started quickly and you can start showing the benefits of an AI strategy early on, which in turn will fuel further support and make the next steps easier to sell to the rest of the team.
AI techniques and capabilities will influence every aspect of the workplace. I hope I have demonstrated throughout this book that I am not one to get excited by fads and hype. Given how hyped AI technologies are at this point in time, it may be hard to see through that to what their real impact can be. However, as we argued in the first chapters, the impact of AI will be far- reaching. Comparing it to fire and electricity (as companies such as Microsoft and Google are doing) may sound far-fetched. There is truth in those state- ments though. Just like fire or electricity, AI has the ability to change how we do everything. If you start from a position that is pragmatic about the chal- lenges but also recognizes the opportunities, you can make a lasting impact on how work is done in your organization. Getting started on an AI strategy may be one of the most significant decisions for the future of your organization.
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