According to two recent Gartner reports, 85 per cent of AI and machine learning projects fail to deliver, and only 53 per cent of projects make it from prototypes to production. Yet the same reports indicate little sign of a slowdown in AI investments. Many organisations plan to increase these investments.
Many of these failures are avoidable with a little common-sense business thinking. The drivers to invest are powerful: FOMO (fear of missing out), a frothy VC investment bubble in AI companies with big marketing budgets, and, to some extent, a recognition of the genuine need to harness AI-driven decision-making and move toward a data-driven enterprise.
Instead of thinking of an AI or machine learning project as a one-shot wonder, like upgrading a database or adopting a new CRM system, it’s best to think of AI as an old-fashioned capital investment, similar to how a manufacturer would justify the acquisition of an expensive machine.
The manufacturer wouldn’t be focused on the machine as a shiny new toy, in the same way that many organisations look at AI and machine learning. The purchasing decision would consider floor space, spare parts, maintenance, staff training, product design, and marketing and distribution channels for the new or improved product. Equal thought should go into bringing a new AI or machine learning system into the organisation.
Here are six common mistakes organisations make when investing in AI and machine learning.
Putting the cart before the horse
Embarking on an analytics program without knowing what question you are trying to answer is a recipe for disappointment. It is easy to take your eye off the ball when there are so many distractions. Self-driving cars, facial recognition, autonomous drones, and the like are modern-day wonders, and it’s natural to want those kinds of toys to play with. Don’t lose sight of the core business value that AI and machine learning bring to the table: making better decisions.
Data-driven decisions are not new. R.A. Fischer, arguably the world’s first “data scientist,” outlined the essentials of making data-driven decisions in 10 short pages in his 1926 paper, “The Arrangement of Field Experiments” [PDF]. Operations research, six sigma, and the work of statisticians like Edwards Deming illustrate the importance of analysing data against statistically computed limits as a way of quantifying variation in processes.
In short, you should start by looking at AI and machine learning as a way to improve existing business processes rather than as a new business opportunity. Begin by analysing the decision points in your processes and asking, “If we could improve this decision by x per cent, what effect would it have on our bottom line?”
Neglecting organisational change
The difficulty in implementing change management is a large contributor to the overall failure of AI projects. There’s no shortage of research showing that the majority of transformations fail, and the technology, models, and data are only part of the story. Equally important is an employee mindset that is data-first. In fact, the change of employee mindset may be even more important than the AI itself. An organisation with a data-driven mindset could be just as effective using spreadsheets.
The first step toward a successful AI initiative is building trust that data-driven decisions are superior to gut feel or tradition. Citizen data scientist efforts have mostly failed because line-of-business managers or the executive suite cling to received wisdom, lack trust in the data, or refuse to yield their decision-making authority to an analytics process. The result is that “grass-roots” analytics activity—and many top-down initiatives as well—have produced more dabbling, curiosity, and résumé-building than business transformation.
If there is any silver lining it is that organisational change, and the issues involved, have been extensively studied. Organisational change is an area that tests the mettle of the best executive teams. It can’t be achieved by issuing orders from above; it requires changing minds and attitudes, softly, skillfully, and typically slowly, recognising that each individual will respond differently to nudges toward desired behaviors. Generally, four focus areas have emerged: communication, leading by example, engagement, and continuous improvement, all of which are directly related to the decision management process.
Changing organisational culture around AI space can be especially challenging given that data-driven decisions are often counter-intuitive. Building trust that data-driven decisions are superior to gut feel or tradition requires an element of what is termed “physiological safety,” something only the most advanced leadership organisations have mastered. It’s been said so many times there’s an acronym for it: ITAAP, meaning “It’s all about people.” Successful programs often devote greater than 50 per cent of the budget to change management. I would argue it should be closer to 60 per cent, with the extra 10 per cent going toward a project-specific people analytics program in the chief human resources officer’s office.
Throwing a Hail Mary pass early in the game
Just as you can’t build a data culture overnight, you shouldn’t expect immediate transformational wins from analytics projects. A successful AI or machine learning initiative requires experience in people, process, and technology, and good supporting infrastructure. Gaining that experience does not happen quickly. It took many years of concerted effort before IBM’s Watson could win Jeopardy or DeepMind’s AlphaGo could defeat a human Go champion.
Many AI projects fail because they are simply beyond the capabilities of the company. This is especially true when attempting to launch a new product or business line based on AI. There are simply too many moving parts involved in building something from scratch for there to be much chance of success.
As Dirty Harry said in Magnum Force, “A man’s got to know his limitations,” and this applies to companies too. There are countless business decisions made in large enterprises daily that could be automated by AI and data. In aggregate, tapping AI to improve small decisions offers better returns on the investment. Rather than betting on a long shot, companies would be better off starting with less glamorous, and less risky, investments in AI and machine learning to improve their existing processes. The press room might not notice, but the accountants will.
Even if you are already successfully using AI to make data-driven decisions, improving existing models may be a better investment than embarking on new programs. A 2018 McKinsey report, “What’s the value of a better model?”, suggests that even small increases in predictive ability can spark enormous increases in economic value.
Inadequate organisational structure for analytics
AI is not a plug-and-play technology that delivers immediate returns on investment. It requires an organisation-wide change of mindset, and a change in internal institutions to match. Typically there is an excessive focus on talent, tools, and infrastructure and too little attention paid to how the organisational structure should change.
Some formal organisational structure, with support from the top, will be necessary to achieve the critical mass, momentum, and cultural change required to turn a traditional, non-analytic enterprise into a data-driven organisation. This will require new roles and responsibilities as well as a “center of excellence.” The form that the center of excellence (COE) should take will depend on the individual circumstances of the organisation.
Generally speaking, a bicameral model seems to work best, where the core of the AI responsibilities are handled centrally, while “satellites” of the COE embedded in individual business units are responsible for coordinating delivery. This structure typically results in increased coordination and synchronisation across business units, and leads to greater shared ownership of the AI transformation.
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