When \u201cbig data\u201d reached the apex of the technological hype cycle several years ago, one would have been hard-pressed to find an enterprise that wasn\u2019t scrambling to accumulate massive unstructured datasets \u2014 whether they needed them or not. The haphazardness of this bandwagoning was arguably a large part of why, in 2015, around 60 percent of big data projects were failing to advance beyond the piloting and experimentation phase.\n\nWhile big data analytics has subsequently become a fixture of the enterprise landscape, its rocky path to prominence is symptomatic of the ill-conceived approach large companies often take to the adoption of emerging technology. Instead of carefully considering how the tech solution du jour will help solve a distinct business problem, overeager enterprises tend to focus on adopting the solution \u2014 in whatever form \u2014 as quickly as possible.\n\nAs 2018 draws to a close, enterprises are facing growing pressure to engage with a new cutting-edge technology: artificial intelligence (AI). Recent research shows that 10 percent of companies currently use AI, 10 percent plan to start using it within the next 12 months, and another 10 percent plan to start using it within 12 to 24 months. Among companies employing 5,000 or more people, however, these splits fall at 31 percent, 18 percent, and 16 percent, respectively.\n\nIn other words, for enterprise-scale organizations, AI is on the brink of arriving in full force. If we are in fact just two years away from nearly two-thirds of enterprises adopting AI in earnest, it\u2019s incumbent upon every enterprise decision-maker to start crafting a strategic plan for AI implementation.\n\nTo that end, high-level stakeholders would be well-advised to keep their organizations\u2019 key business questions in mind as they go about drafting their AI implementation roadmaps. Not unlike big data, AI will be most effective not when used for its own sake, but when used to pursue actionable answers to clearly articulated questions.\n\nPutting actionability first\n\nThe success of enterprises\u2019 preliminary AI efforts hinges on the kinds of questions they ask. \u201cIn which areas do we want to leverage AI to stimulate growth?\u201d \u201cWhich of our processes do we want to make more efficient through the use of AI?\u201d \u201cWhich key business questions do we hope AI will help us answer?\u201d These \u2014 not, \u201cHow can we deploy AI faster and\/or more extensively than our competitors?\u201d \u2014 are the hallmarks of a sophisticated AI implementation strategy.\n\nAnd while, ordinarily, the viability of a new project depends on both its actionability and its potential business impact, when it comes to AI, it\u2019s the former that\u2019s of the utmost importance. Granted, the higher the business impact, the better, but incremental improvements driven by nascent AI initiatives tend to compound remarkably quickly, and thus should never be discounted.\n\nThat said, assessing an AI-powered tech solution\u2019s actionability \u2014 even one that has been deliberately chosen in view of a set of clear business questions \u2014 requires a comprehensive review of the environment into which it\u2019s being deployed.\n\nLaying the groundwork for successful AI implementation\n\nMost immediately, an AI solution without a steady stream of relevant data is like a luxury vehicle without gasoline: impressive to look at, but not getting anyone anywhere fast. AI\u2019s power rests with its ability to process \u2014 and elicit insights from \u2014 sprawling structured and unstructured datasets at superhuman speeds, but enterprises that are new to AI and opt to adopt an off-the-shelf tech solution often unwittingly end up with a cutting-edge algorithm and nothing to \u201cfeed\u201d it.\n\nAs such, wherever possible, enterprise decision-makers should deploy their AI solutions into business contexts that feature rich datasets detailing both the enterprise\u2019s historical and contemporary activity. The former are critical to acclimating a generic solution to a specific enterprise\u2019s needs, whereas the latter are a necessary condition of the incisive predictive analyses for which the most powerful AI solutions are renowned. If these datasets are inconsistent or excessively fragmented, no AI solution \u2014 no matter how sophisticated \u2014 will be able to produce actionable insights on a consistent basis.\n\nIn addition to a deployment vector guided by a precise business question and a steady stream of relevant data, an AI solution needs a pre-existing technological framework in which to exist. The underlying mechanics of a solution \u2014 the ways in which it receives data, processes it, and returns some sort of output \u2014 are meaningless without an appropriate context in which they can trigger tangible actions.\n\nConsider, for instance, a chatbot. It receives input data (a typed query), runs the data through a natural language processing algorithm, and delivers a sapient response. As impressive and efficiency-driving as this may be, such a solution would amount to nothing in a vacuum. It needs a website on which to \u201clive\u201d and, ideally, an attractive, intuitive user interface to encourage site visitors to actually utilize it.\n\nThough not always so neatly delineated, the need for proper technological contextualization exists everywhere AI solutions are deployed. Whether it takes the form of a natural language processing algorithm, a deep learning algorithm, or a neural network, AI as such must be embedded within a usable tech solution, which itself must be strategically situated with a company\u2019s broader technological landscape.\n\nIn short, if the tech solution is a luxury vehicle and the steady data stream is the gasoline, the AI itself is the engine. No matter how much horsepower it boasts, it\u2019s useless without wheels and a chassis, fuel, and a road on which to drive.\n\nThe financial services industry leads the way\n\nThus far, the financial services industry has been running in pole position in the AI adoption race. Over a fifth (21 percent) of financial services companies are currently using AI, and another 29 percent plan to be doing so within two years. Other than healthcare (14 percent) and IT (11 percent), no other industry has a current adoption rate above 8 percent.\n\nIn light of the requirements for successful AI deployment outlined above, financial services\u2019 advanced position is ultimately unsurprising. Let us take AI-powered fraud detection as an example.\n\nBanks and credit card companies have access to vast databases cataloging past instances of fraud. By feeding this historical data to machine learning algorithms, financial institutions have been able to level-up their AI solutions extremely quickly, resulting in ultraprecise fraud detection capabilities. What\u2019s more, these institutions are able to gather millions of new data points every day \u2014 each transaction a customer completes serves to further refine fraud detection models.\n\nJust as importantly, financial institutions have taken great pains to construct end-to-end technological frameworks that facilitate the jump from insight to intervention. As soon as an algorithm pinpoints an instance of potential fraud, the tech solution in which it\u2019s embedded pushes an alert to the relevant account owner \u2014 usually via text message, email, and\/or a mobile banking app. The account owner is then given the opportunity to confirm or dispel a suspect purchase\u2019s fraudulence, resolving the issue one way or the other immediately.\n\nNot only does this use case check all the boxes of successful AI deployment, it also happens to have a fairly significant business impact. First and foremost, it enables financial institutions to shut down fraudulent activity faster than was previously possible, which in turn minimizes the institutions\u2019 direct fraud-related losses. Further, fairly or not, today\u2019s consumers expect their bank or credit card company to spot fraud before they themselves do, meaning an effective AI-powered fraud detection solution has the ability to nudge consumers from one institution to another.\n\nA roadmap for the future\n\nFor large organizations operating in industries other than financial services, AI-powered fraud detection \u2014 and other financially-oriented use cases like determining credit-worthiness \u2014 serves as an excellent example of how to deploy an AI solution effectively. For CTOs and other high-level technical stakeholders within such organizations, the challenge is convincing board members and CEOs to resist the cresting hype and instead take a measured, strategic approach to AI implementation.\n\nAs AI technology continues to mature, potential use cases will start to emerge in nearly every industry. That said, only those that are actually actionable \u2014 i.e. have access to a steady stream of data and an appropriate technological framework \u2014 will meaningfully differentiate their adopters from industry baselines.\n\nUltimately, artificial intelligence is not a self-executing panacea, but a tool. When wielded properly, however, this tool has the power to solve complex business problems that once seemed unsolvable. Activating this power takes a great deal of organizational strategizing and a healthy dose of patience, but these initial growing pains are a small price to pay for the benefits of doing business on the cutting edge.