In the stampede to build an AI strategy, executives fall into four main traps.

It’s almost impossible to pick up a trade journal, hear a start-up pitch or listen to a quarterly earnings call without hearing the two magic letters: AI. Over the past few years, interest in artificial intelligence has rocketed with no sign of abating. But in the stampede to build an AI strategy, executives fall into the following four main traps.

1. Thinking that artificial intelligence is one single technology. 

As boards and corporations are flush with AI fever, they tend to speak of AI as one all-encompassing technology. Even the question, “What is our AI strategy?” presumes that AI is one silver bullet. But on the contrary, the AI we see today is the result of many layered technologies (e.g. computer vision, natural language processing, generative adversarial networks, and more). There is no single AI technology – rather there is the application of AI techniques to train computers to solve problems so repetitive tasks are reduced. This can take forms ranging from computers recognising images and voice commands, to decision trees built to deliver the best options for a customer, to driverless vehicles navigating the road.

As an enterprise endeavours to apply AI to its business, it’s important to understand that to “do AI” requires a more focused approach. The key is to understand the business’ core competencies, assess the customer and the gaps, as well as prioritise the processes that specific AI technologies can make more efficient.

If your business has a large loss component (such as in banking), then fraud detection using AI could be the investment yielding the highest return. An example would be Citibank’s partnership with machine learning start-up Feedzai.

If your business has a large servicing component (such as in healthcare and hospitality), then customer service automation could be an area of first priority. KLM airlines receives thousands of customer queries a day on average – twice as many in times of weather disturbances. By automating its customer service operations through a partnership with DigitalGenius, KLM is able to answer the most common questions without the support of human service agents.

2. Missing the data link.

Artificial intelligence only functions with sufficient, high-quality data. However, data by itself is useless. What makes data meaningful is its ability to affect action through insight. To begin, ask yourself, how well is your company capturing data? Remember, at the heart of it, AI is about finding patterns and making consequent predictions based on large sets of structured or unstructured data (images, text, speech, etc.).

Here is a four-step checklist to evaluate your company’s data health. Is it “COMA-tose”?

1. Is your data compiled in a central repository and is it accessible? Many industries suffer from antiquated IT systems. Others are burdened by legacy systems brought about by mergers and acquisitions – such that IT systems don’t talk to each other. Of course, all this assumes you are collecting data to begin with: demographic data, transaction data, analytical data and so on.
2. Is your data organised in a meaningful way? Chronological, demographical, geographical, by product – whatever allows you to detect patterns and develop a profile or story for your customer and your business.
3. Is your data mobile? Can data move easily from one database to another? This again tends to be a challenge for antiquated IT systems, or legacy industries when no “bridge” exists between databases.

When social media began, capturing customers’ tweets, Facebook posts and social activity became a challenge because technology didn’t exist to capture it. Today, companies like Walmart and Lenovo use solutions like Sprinklr and Brandwatch to capture and act on social media data, as well as bring it into their data fold. The advent of the cloud has also boosted data mobility, allowing data to be moved in a near seamless fashion.

1. Are you able to analyse your data? This has a lot to do with data quality. The saying “garbage in, garbage out” illustrates how important it is to get quality data that you can analyse; if not, your output is questionable. Data cleansing can take a lot of time so ensure that your data input parameters are tight.

Many companies today, regardless of industry, encounter challenges with collecting and compiling data. It’s common for companies that are not digitally native, i.e. not born in the past 10 years, to see data collection as peripheral. Certain industries lend themselves well to collecting data, such as in banking. A tier 1 bank I worked in had tonnes of data but was the product of many mergers, so compiling data amongst different databases was a challenge.

It’s also critical to avoid data silos. This can happen when companies are organised by business units or geographies and those units build their own data strategies independent of each other. I saw this at an insurance company I worked in. Building a data collection strategy at the enterprise level is key.

3. Neglecting to build sufficient talent and an adequate organisational structure.

For all the doom and gloom AI amasses in the media related to job cannibalisation, it actually presents a golden opportunity for the labour market. Demand is great and talent is scarce. New career opportunities have and will continue to present themselves. As companies gear up to meet the age of data, non-digitally native companies need to invest in data-savvy professionals – those that know how to leverage the benefits of data (data strategists), construct them (data engineers), manipulate them (data scientists) and optimise them (data visualisers and modellers).

But don’t just go out there and hire as many engineers and statisticians as you can find. Today I see companies rush to hire data scientists only to lay them off a year or two later. Training needs to take place and organisations need to be redesigned to accommodate a new data-centric way of working. This also requires nontechnical talent: strategists with an in-depth understanding of the business as well as AI are invaluable.

One approach to this new discipline is to create your “AI Marines”, a task force and centre of excellence whose job is to rotate through the functions as internal consultants and evaluate opportunities. These will be highly trained, specialised experts with deep experience in strategy, data engineering and data science. While on rotation in various functions, they will also train the in-house talent for the work to be done post-assessment.

4. Forgetting to build a “police force”.

Data is an ever-evolving organism. The sources and content of data change constantly. Companies must remain vigilant in sourcing, capturing and revalidating data and their corresponding models. Each model that we would roll out at the tier 1 bank had to go through a rigorous model-validation committee comprised of legal, risk, compliance and data executives to ensure bias didn’t creep in and that we were using variables appropriately. We also evaluated our models annually to prevent adverse effects and ensure performance and reliability.

From insight to action

The power of AI lies in its ability to take any repetitive task and make it more efficient, freeing us to deliver more creative output and better customer experiences. AI also has the ability to move businesses from insight to action and monetise data to improve ROI.

For organisations that can harness the power of AI, the pay-off can be significant. The company that understands that AI is more than one technology, ensures its data is COMA-tose, aligns its organisation and manages these AI efforts well, wins a competitive advantage and builds a defensible business ready for the AI age.

The post What Companies Tend to Get Wrong About AI appeared first on Artificial Intelligence.

Those who cannot learn from history are doomed to repeat it.” – George Santayana

In early September, the media went abuzz with news of Amazon becoming the second US company to surpass the $1 trillion market cap. However, the news of Amazon’s success has eclipsed certain interesting facts:

• 2017 was the worst year ever for traditional retail stores thanks to e-commerce businesses like Amazon.
• More than 662 traditional retail businesses filed for bankruptcy in 2017 — a 30 per cent increase from 2016.
• Traditional retailers were the second biggest job loser in 2017 — contributing to a loss of over 36,000 jobs.
• Abercrombie & Fitch, American Apparel, Bebe, J.C. Penney, Macy’s, Sears, and RadioShack are just a few of the high profile traditional retailers that had to shut down stores due to internet companies like Amazon cutting into their revenue.

  Now, there’s no point crying over spilt milk. Experts believe that 2018 will end up being as difficult as 2017 was for traditional brick-and-mortar stores. All indications point to the fact that traditional brick and mortar stores will continue to go into obscurity for as long as the internet continues to thrive.

  What is most noteworthy, however, isn’t the fact that many of these businesses are going under but the fact that they ignored the internet, which eventually was responsible for their going under, for so long until it was too late. Eventually, smaller, underfunded, and bootstrapped startups like Amazon came over and ate their lunch.

  While not much can be done to help the traditional brick and mortar stores that are going under, there is another technological revolution — similar to the kind that forced many of these businesses to go under — on the horizon, and it is in artificial intelligence.

  I believe artificial intelligence will be as disruptive as the internet was, and, just as was the case with the internet, many big players are, and will continue, ignoring artificial intelligence. In fact, as reported here on TNW just recently, a recent research paper shows that China is on track to outspend the US on AI research by the end of 2018.

  There are sparks in AI but we need fire

  As was chronicled here on TNW just a few months ago, sparks are already being created in the private sector when it comes to artificial intelligence:

  Among big companies, automaker Volkswagen (VWAGY) is already accelerating research and work on AI in an attempt to make self-driving cars a reality. In banking, a good portion of JPMorgan Chase’s (JPM) $10 billion tech budget for 2018 was assigned to AI, and their hiring decisions show that they are taking AI serious. They have already rolled out AI-powered services to help their clients better invest. In healthcare, Philips (PHG) has an AI strategy in focused on releasing AI-powered solutions to assist medical professionals in better assisting patients.

  Among startups, BioSig Technologies (BSGM) is using signal processing to develop a system that acquires and manages electrocardiographic and intracardiac signals in patients undergoing electrophysiology (EP) procedures. Phrasee is using artificial intelligence to analyze successful campaigns and create copy for organizations such as The Times and Domino’s. Vivacity Labs is using AI camera technology to gather data on transport systems and provide data for smart cities.

  While the above are all examples of good use of artificial intelligence, both among successful mega corporations and startups, we have just barely scratched the surface.

  Research from Adobe shows that only a measly 15 percent of enterprises are using artificial intelligence, and data from MIT Sloan Management Review shows that, while large companies with over 100,000 employees are the most likely to have an AI strategy, only half of them have one.

  In essence, it doesn’t seem the government is going to take a lead when it comes to artificial intelligence, and while the private sector is indeed making moves, adoption is still very slow. One thing is clear, however: we can’t afford to ignore artificial intelligence.

  Here are some of the reasons why we can’t ignore AI:

  1. Information can be processed, analyzed, and used faster: Due to AI’s adaptiveness, it is easy to process, analyze, and make available timely information at a much faster pace.

  For example, in fields such as healthcare where lives of users could be at stake, health information can be quickly analyzed to provide more timely solutions and in a way that reduces mix-ups, misdiagnoses, and other medical errors.

  2. AI can help fight crime better: When we look at how much resource is being wasted on fighting crime, a strong case is made for investing more into research on AI applications for fighting crime.

  For example, it was only recently that it was in the news that a Chinese fugitive was singled out and arrested from a crowd of 60,000 people attending a pop concert. Guess how he was arrested? He was detected by an AI-powered facial recognition system.

  There are several advantages to using AI to fight crime: just a few include the fact that it minimizes resources used, works faster and with better precision, and can be automated.

  3. AI can help make transportation more efficient: Self-driving cars, smart traffic systems, and self-managed fleets are just some of the ways artificial intelligence can help ensure a more efficient transportation system.

  4. Financial systems can benefit from AI: Banks and financial institutions can use AI to improve compliance, operational efficiency, develop effective investment strategies, and automate user activities.

  Conclusion

  I could go on and on, but the potential applications of artificial intelligence are limitless. While our government isn’t doing as much as they can when it comes to AI, the private sector has a lot at stake — we will have a bigger price to pay if we sit and do nothing.

The post Artificial intelligence is complex, but we can’t afford to ignore it appeared first on Artificial Intelligence.

These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. An online summary of the report is available, and a PDF of the report is accessible here (22 pp., PDF, free, no opt-in). The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4.

The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs. The leading companies who excel at using AI to plan new businesses and streamline existing processes all have solid senior management support for each AI initiative.

Key takeaways include the following:

• 72% of respondents in the technology, media, and telecommunications industry expect AI to have a significant impact on product offerings in the next five years. The technology, media and telecommunications industry has the highest expectations for AI to accelerate new product and service offerings of all industries tracked in the study, projecting a 52% point increase in the next five years. AI-based improvements are expected to deliver Business Process Outsourcing (BPO) gains in the Financial Services and Professional Services industries as well. The following graphic compares expectations for AI’s expected contributions to business offerings and process improvements over the next five years by industry.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• Customer-facing activities including marketing automation, support, and service in addition to IT and supply chain management are predicted to be the most affected areas by AI in the next five years.Demand management, supply chain optimization, more efficient distributed order management systems, and Enterprise Resource Planning (ERP) systems that can scale to support new business models are a few of the many areas AI will make contributions to the in the next five years. The following graphic provides an overview of operations, IT, customer-facing, and corporate center functions where AI is predicted to contribute.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• 84% of respondents say AI will enable them to obtain or sustain a competitive advantage. 75% state that AI will allow them to move into new businesses and ventures. The research shows that AI will be the catalyst of entirely new business models and change the competitive landscape of entire industries in the next five years. 69% of respondents expect incumbent competitors in their industry to use AI to gain an advantage. 63% believe the pressure to reduce costs will require their organizations to use AI in the next five years.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• Despite high expectations for AI, only 23% of respondents have incorporated it into processes and product and service offerings today. An additional 23% have one or more pilots in progress, and 54% have no adoption plans in progress, 22% of which have no current plans. The following graphic provides insights into the current adoption of AI with survey respondents.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• By completing a cluster analysis of survey respondents based on AI understanding and adoption questions, four distinct maturity groups emerged including Pioneers, Investigators, Experimenters, and Passives. 19% of the respondent base is Pioneers or those organizations who understand and are adopting AI. The study says that “these organizations are on the leading edge of incorporating AI into both their organization’s offerings and internal processes.” Investigators (32%) are organizations that understand AI but are not deploying it beyond the pilot stage. Experimenters (13%) are organizations that are piloting or adopting AI without deep understanding. Passives (36%) are organizations with no adoption or much knowledge of AI.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• Pioneers and Investigators are finding new ways to use AI to create entirely new sources of business value. Pioneers (91%) and Investigators (90%) are much more likely to report that their organization recognizes how AI affects business value than Experimenters (32%) and Passives (23%). One of the most differentiating aspects of the four maturity clusters is understanding the differences and value of investing in high-quality data and advanced AI algorithms. Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms and ten times more likely to comprehend the development costs of AI-based products and services.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• Organizations in the Pioneer cluster excel at analytics expertise versus competitors and have exceptional data governance processes in place, further accelerating their AI-driven growth. Pioneers are excellent at change management, citing their senior management’s vision and leadership as a foundational strength in accomplishing their AI-based initiative Early adopter Pioneers are also adept at product development, capable of changing existing products and services to take advantage of new technologies.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• 61% of all organizations interviewed see developing an AI strategy as urgent, yet only 50% have one done today. The research found that regarding company size, the largest companies (those with more than 100K employees) are the most likely to have an AI strategy, but only half (56%) have one. The following graphic compares the percentage of respondents by maturity cluster who say developing a plan for Al is urgent for their organization relative to those that have a strategy in place today.

Boston Consulting Group & MIT Sloan Management Review, Reshaping Business With Artificial Intelligence

• 70% of respondents are personally looking forward to delegating the more mundane, repetitive aspects of their jobs to AI. 84% believe employees will need to change their skill sets to excel at delivering AI-based initiatives and strategies. Taking this approach provides career growth and a chance to become more marketable for many whose jobs that are being increasingly automated. Cautious optimism regarding AI’s effects on employment dominates early adopter organizations, not dire fatalism. The bottom line is that AI is providing opportunities for career growth that will only accelerate in the future. Those that seize the chance to learn and earn more will end up having AI removing the mundane tasks from their jobs, leaving more time for the most challenging and rewarding work.

The post How Artificial Intelligence Is Revolutionizing Business In 2017 appeared first on Artificial Intelligence.

The artificial intelligence market is predicted to triple in 2017, becoming a $100 billion industry by 2025. In order to stay competitive, companies are turning to new AI software and solutions in their business operations. For example, Google recently launched an AI-powered job search feature right on its search results page.

As a small business owner who has personally benefitted from AI tools without spending thousands of dollars, I believe it’s critical for you to get educated and start reaping the benefits of AI. Doing so will not only mean a competitive advantage for your business but will also add a strong value driver to your company.

To support a corporate transition to AI, here are five ways to boost your machine learning and AI strategy in 2017:

Personalize your customer service.

Prompt and efficient customer support strengthens brand loyalty and client satisfaction. Therefore, what could be better than an opportunity to improve customer service while decreasing costs? Chatbot technology, based on recent advances in natural language processing and continuous learning algorithms, can perform better than many human operators. Advancements in chatbot technology have already shifted preferences of 44% of U.S. consumers to non-human consultants, according to data from the 2016 Aspect Consumer Experience Index.

Companies like DigitalGenius augment the existing customer support communication with an intelligence layer of proposed answers that humans can either approve or edit before sending. These types of augmented intelligence applications allow humans to mitigate risks of error. They are also gaining adoption across enterprise companies due to their sensitivity to the cost of making mistakes when dealing with customer support.

Speed up your data and predictive analytics.

Business leaders are pressed to make important marketing, sales and investment decisions on a daily basis. However, getting actionable insights from data using traditional data modeling methods may take weeks. Contemporary AI-powered predictive and business analytics solutions solve this problem via automatic interfaces that allow even non-technical users to get instant models, patterns and meaningful insights from data. IBM Watson Analytics, based on Watson’s question answering machine, is one of the most advanced solutions that allows users to receive data-driven responses to questions regarding any aspect of the business, such as sales, finance, human resources and marketing. Integrating predictive analytics can help managers leverage the full power of big data in business operations.

Refine your human resources management.

Hiring the right people in the right positions is a hard task, especially if HR managers are bombarded with thousands of CVs and cover letters. Sometimes, implicit biases can creep into job opening descriptions and the job interviewing process, and qualified candidates might be overlooked.

“Automatic data mining and ML solutions can give a boost to your hiring strategy by helping HR specialists identify the best candidates for a position and thereby decreasing human subjectivity and error in decision-making” Artur Kiulian, author of Robot Is The Boss, told me.

Automate your marketing.

AI solutions promise to automate many marketing tasks, such as email marketing and lead management. Instead of performing simple and repetitive tasks, marketers can focus on producing creative ideas. AI companies like Marketo are already offering AI-enabled systems to build marketing campaigns, attract and retain customers more efficiently by using predictive analytics, provide sales forecasting, identify potential clients and predict user behavior. And cloud-based tools like LUCY can help perform in-depth marketing research based on machine-learning algorithms that build flexible marketing strategies and models of target markets.

Quickly detect fraudulent activity.

Fraudulent transactions can lead to huge losses for all major businesses. Traditional security methods are good in protecting networks and communication channels, but what about fraud prevention and unmasking it in the making? Modern anomaly detection algorithms leverage the power of machine learning to learn patterns of fraudulent transactions and behavior. Algorithmic security solutions can help companies discover suspicious transfers between individuals and intercorporate connections to prevent corporate espionage and insider trading.

AI’s rapid rise is inevitable, and the question now is not whether to adopt AI or not, but how fast managers can do so. At the same time, AI strategists should be thoughtful in the adoption of AI solutions to maintain business processes and pay attention to potential pitfalls with this technology.

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