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How to accelerate business change using data and AI

AI poses as many challenges as opportunities. Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI.

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By Bret Greenstein

Most senior leaders would agree that adapting in real time to customer and market needs is vital for achieving their visions and goals. And to develop this capability, they’d also likely agree they need data and artificial intelligence (AI).

The examples are all around us. The city of Chicago uses 12 variables, including high daily temperatures, to prioritize which of the city’s 7,000 “high-risk” restaurants it should send its 35 food inspectors to. The AI solution found violations a week earlier than they otherwise would have.

We helped an insurer create an AI system that provides underwriters with more precise estimates of risk, as well as the likelihood applicants will accept a specific price quote. The insurer can adjust how the AI environment makes its approval and pricing recommendations to ensure compliance with changing corporate priorities around risk vs. revenue.

We’ve also seen AI help predict customers’ emerging needs as markets change. If the economy goes into a recession, informed analysis could help organizations not only cut costs but also provide the products and services customers might need in a downturn.

And, of course, there are the businesses that have used data and AI to create new revenue streams and business models that drive lasting competitive advantage. For game changers like Uber, data is at the core of the company and constitutes value, not cost.

Facing up to AI realities

But there’s another hard fact that would draw consensus from many business leaders today: the unprecedented effort required to move an enterprise in an AI direction. The fact is, AI-enabled business change requires as much alteration to corporate culture, organizational structures and processes, and workforce roles and skills as it does new technology.

For example, too many organizations still treat data as an expense and a security risk. Technical or organizational siloes make it difficult to pool information in flexible data lakes. Many businesses also lack the skills to manage AI-enabled analytics amid rising privacy and ethical concerns. Others are unable to provide audit trails on how AI decisions were made or deliver AI-enabled analytics quickly enough.

AI success factors

Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI:

  • Senior leadership is passionate about the value of data. CIOs can play a big role here. They need to work with the CEO and business leaders to resolve the tension between business managers who want more access to data and the pressure to restrict such access to reduce cost and risk.
  • Data scientists embedded with business users. By working more closely with business users, data scientists can better understand the business context behind their requests. Business managers may say they need “X,” but what they really need is a solution to a problem, which may or may not be “X.”
  • The establishment of an AI oversight role. Because AI systems learn and improve, they can produce different results at different times based on the data they’re given and the algorithms applied. This means they require closer oversight than traditional systems that are coded, tested and summarily released – and then left alone.In some ways, managing an AI system is like raising a child. You need to be sure they’re not being trained with intentionally or unintentionally biased data or by malicious users. If a loan application is denied, for example, you need to know the decision was ethically sound.
  • Plenty of high-quality data. The more high-quality data, the better and more quickly the AI system can learn. To supply that data, organizations must refine their processes for ongoing data preparation, integration and pipelining. This essential groundwork is the hardest part in building an AI system.
  • Modernized infrastructure. An infrastructure based on application programming interfaces (APIs) and Agile development techniques makes it easier to quickly deliver new AI-based applications. This more flexible infrastructure enables organizations to better access needed data from outside the enterprise, and more effectively monetize the resulting insights by sharing them across the ecosystem.For instance, we worked with one of the world’s largest and busiest airports to revamp its technology infrastructure to increase airport efficiency and performance. The organization combined data and AI to unlock more capacity to serve airlines and reduce passenger misconnects.

Our final recommendation: Start now. AI can be difficult and complex, and it’s hard to catch up once you’ve fallen behind. Even modest successes will teach you a lot, and the real danger in digital is being a laggard.

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The importance of data access for digital initiatives

A new report from MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

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In a global survey of over 1,700 line of business employees in organizations with at least 250 employees, MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

The resulting report — The State of Business and IT Innovation — reveals four key ideas that IT leaders need to know in order to drive digital innovation forward.

These four key findings are:

  • Collaboration is key 
    • 68% of respondents believe IT and LoB users should jointly drive digital innovation.
  • Keep up the pace 
    • 51% expressed frustration with the speed at which IT can deliver projects.
  • Integration challenge
    • 37% cite security and compliance as the biggest challenge to delivering new digital services, followed by integration (i.e. connecting systems, data, and apps) at 37%.
  • Data access
    • 80% say that in order to deliver on project goals faster, employees need easy access to data and IT capabilities.  

“This research shows data is one of the most critical assets that businesses need to move fast and thrive into the future,” said MuleSoft CEO Brent Hayward

“Organizations need to empower every employee to unlock and integrate data — no matter where it resides — to deliver critical, time-sensitive projects and innovation at scale, while making products and services more connected than ever.”

Want to read through the whole report? Download it from MuleSoft

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Where is the financial value in AI? Employing multiple human-machine learning approaches, say experts

According to a new study, only 10% of organizations are achieving significant financial benefits with AI.

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AI is everywhere these days — especially as we work to fight the spread of COVID-19

Even in the “before times,” AI was a hot topic that always found itself in the center of most digital transformation conversations. A new study from MIT Sloan Management Review, BCG GAMMA, and BCG Henderson Institute, however, prompts a crucial question:

Are You Making the Most of Your Relationship with AI?

Finding value

Despite the proliferation of the technology and increased investment, according to the report, just 10% of organizations are achieving significant financial benefits with AI. The secret ingredient in these success stories? “Multiple types of interaction and feedback between humans and AI,” which translated into a six-times better chance of amplifying the organization’s success with AI.

“The single most critical driver of value from AI is not algorithms, nor technology — it is the human in the equation,” affirms report co-author Shervin Khodabandeh.

 

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From a survey of over 3,000 managers from 29 industries based in 112 countries — plus in-depth interviews with experts — the report outlined three investments organizations can make to maximize value:

  • The likelihood of achieving benefits increases by 19% with investment in AI infrastructure, talent, and strategy.
  • Scalability. When organizations think beyond automation as a use case, the likelihood of financial benefit increases by 18%.
  • “Achieving organizational learning with AI (drawing on multiple interaction modes between humans and machines) and building feedback loops between human and AI increases that likelihood by another 34%.”

According to report co-author Sam Ransbotham, at the core of successfully creating value from AI is continuous learning between human and machine:

“Isolated AI applications can be powerful. But we find that organizations leading with AI haven’t changed processes to use AI. Instead, they’ve learned with AI how to change processes. The key isn’t teaching the machines. Or even learning from the machines. The key is learning with the machines — systematically and continuously.” 

Continued growth

While just 1 in 10 organizations finds financial benefits with AI, 70% of respondents understand how it can generate value — up from 57% in 2017.

Additionally, 59% of respondents have an AI strategy, compared to 39% in 2017, the survey found. Finally, 57% of respondents say their organizations are “piloting or deploying” AI — not a huge increase from 2017 (46%). 

One of the biggest takeaways? According to co-author David Kiron, “companies need to calibrate their investments in technology, people, and learning processes.”

“Financial investments in technology and people are important, but investing social capital in learning is critical to creating significant value with AI.”

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Bringing DX to the food supply chain in a pandemic

In a new paper, supply chain stakeholders share how COVID-19 has affected the transformation of the sector.

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There’s little doubt that COVID-19 had a profound effect on the food supply chain.

As one example, just think back to roughly March of this year, when virus transmission was rapidly picking up speed. Remember the reports of food and beverage companies only producing their most popular or essential products? Or how it would take slightly longer than usual to restock certain products? What about the rush to integrate — or quickly improve the efficiency of — digital and e-commerce. 

Panning out a bit, think about food safety and quality professionals. The need to stay safe — and in many cases, stay at home — meant performing the very hands-on job of monitoring, auditing, inspecting at a distance, i.e. digitally. 

When the food supply chain was hit by storages, delays, breakdowns, and lockdowns, the end result was — like in so many sectors — a rapid digital transformation.

As The Food Safety Market — an SME-powered industrial data platform dedicated to boosting the competitiveness of European food certification — elaborates in a new discussion paper, “technology has played an important role in enabling business continuity in the new reality.”

The paper — Digital Transformation of Food Quality & Safety: How COVID-19 accelerates the adoption of digital technologies across the food supply chain — features industry experts from companies like Nestlé, Ferrero, PepsiCo, McCormick & Company, and more discussing the effects of the pandemic on the supply chain.

A few highlights from the paper:

  • John Carter, Area Europe Quality Director for Ferrero put the issue of food access into perspective at the start of his interview:

“The production of food defines our world. The effects of agriculture on our daily lives are so omnipresent that they can be easy to overlook; landscapes and societies are profoundly influenced by the need to feed our growing population. But much has been taken for granted. Only occasionally are we forced to consider: ‘where does our food come from?'”

  • Ellen de Brabander, Senior Vice President of R&D for PepsiCo provided insight on the cost benefits of digital transformation:

“The need for customization is a big driver for accelerating digital transformation and moving away from a ‘one size fits all’ approach. This means that the cost to develop and produce a product must be lower and digital technologies provide a clear opportunity here.” 

  • Clare Menezes, Director of Global Food Integrity for McCormick & Company brought up one area where digital tools need to go:

“There aren’t any areas where digital tools “fail”, but there is a need for tools that ‘prove out’ predictions around where the next integrity event will play out and how it could lead to quality or food safety failure. These tools are an obvious candidate for AI given the number of PESTLE factors that might come into play.” 

Want to read all of the interviews? Check out the paper here.

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