Organisations often block their own path towards scaling by delaying innovations purely out of their loyalty to their core businesses. Unfortunately, fixed organisational structures and legacy operating models result in frailty, disabling the sight of potential market changes. Enterprises hesitate to build products or services with new technology as they are unsure of whether the growth rates would satisfy their shareholders.

Today, to compet in a VUCA environment, businesses and their change agents must consider relooking and reconsidering how they engage and conduct business with their customers and leverage innovative technologies to enhance or introduce products/services to the market.

With rapidly changing consumer buying patterns and preferences, enterprises are increasingly focusing on digitization to evolve their business models, they are being mindful of the efficiency of business operating units, which enables them to pinpoint areas that need additional focus or restructuring quickly. Some of the possible initiatives organisations can opt for are:

• Growth hacking:Organizations need to be agile and iterative in their approach to keep up with the changes in the industry. Having a growth mindset can enable management, embrace challenges, show resilience while working through obstacles, and bounce back from impediments sooner, leading to overall higher achievement. Organizations backed by a growthhacking mindset will be ableto foster innovation and generate higher financial returns.
• Retooling organization:Companies should ‘avoid putting all eggs” in one basket regarding technology infrastructure. The focus should be on adopting technology like building a Lego structure, where individual components are replaced if the scalability and validity for the current and future needs of the business aren’t met.
•  Adopting new age innovation rather reinventing: Applications of AI is evolving within the industry at a fast paced, which enables organizations to evaluate quickly, adapt, pilot and scale within the organizations. Reinventing AI would mean wastage of resources of time, instead organization precious resources can be spent on identifying the right opportunity to evaluate and scale the benefits of AI.The global COVID-19 pandemic has crushed standards and redefined how business is conducted, affecting most enterprises in some way or another. At the same time, enterprises were already leveraging data science and AI in the past few years.  A significantly greater number of organizations are now looking for ways to harness them to reinvent themselves. Key-focused areas remain in strategy building, decision-making and governance setup, business planning and budgeting, funding decision making, managing performance and company culture, risk management, and more.For businesses, resiliency will become even more significant than efficiency as they move forward and data science will help companies maintain. For instance, retail stores and restaurants that were more dependent on brick-and-mortar sales before the pandemic had to make drastic changes to survive and sustain. While some were forced to shut shop, the rest kept steering ahead with new business models to adapt and thrive. Data science helped companies stabilise their organisations, build new processes, establish new communication channels and workflow, adapt to the remote working environment, recognise (and adapt to) changing consumer patterns and identify the emerging trends by using AI and machine learning.Traditionally, legacy companies used to focus only on their core business. With the new wave of transformation and new opportunity post the pandemic, these prominentestablished players are reinventing themselves and creating businesses in new areas with a very different mindset and culture than their traditional organizations.The new digital era demands asignificant change in traditional thinking and focusing on the practical approach of collaboration, competition, and innovationthat can combine data science, AI,and business acumen to conceive, build and bring new digital products to market at scale.

The post KEY TO SCALE IN VOLATILE MARKETS: RE-INVENTING BUSINESS MODELS WITH THE SUCCESSFUL DEPLOYMENT OF AI AND DATA SCIENCE BY ANEES MERCHANT EXECUTIVE VICE PRESIDENT – APPLIED AI & DIGITAL AT COURSE5 INTELLIGENCE appeared first on Artificial Intelligence.

Asset-intensive organizations are pursuing digital transformation to attain operational excellence, improve KPIs, and solve concrete issues in the production and supporting process areas.

AI-based prediction models are particularly useful tools that can be deployed in complex production environments. Compared to common analytical tools, prediction models can more easily amplify correlations between different parameters in complicated production environments that generate large volumes of structured or unstructured data.

My regular talks with executives of production-intensive organizations indicate that AI use is steadily rising. This is in line with IDC’s forecast that 70% of G2000 companies will use AI to develop guidance and insights for risk-based operational decision making by 2026. The figure is less than 5% today.

Please — do not be distracted by visions of a powerful “central brain” that can manage the entire organization. Typical, everyday use cases mostly leverage cognitive AI embedded in planning and scheduling tools. It is also used in quality- and maintenance-predictive models.

What delivers immediate value — and very reasonable ROI — are solutions that leverage AI-powered engines that recognize images and sound, numeric values from vibrations, temperatures, and processes. We currently see most of these use cases in pilots or isolated implementations.

Customized vs. Standardized AI-Powered Solutions

From a scalability perspective, there are two main groups of digital projects that leverage AI in production areas. Each delivers value. However, they each offer different time to scale and time to accuracy.

Customized Solutions: AI-powered solutions based on complex learning processes are highly customized. They may leverage neural networks and deep learning for image recognition, or supervised learning to build predictive models.

It takes a relatively long time to fine-tune a solution to provide 90% accuracy. These are usually predictive solutions that model the behavior of material as it goes through a production process (e.g., breakage predictions for a paper belt or steel slab).

Gülsün Akhisaroglu, the global IT director for Hayat Holding, a hygiene and paper products manufacturer, told me: “It took us almost two years to achieve 90% accuracy.”

This sounds like industrial scalability could be a real challenge. However, for this project, the auto-learning mode was applied — substantially accelerating progress toward 99% accuracy.

Even in highly customized models, it may be difficult to find the root causes of problems. To resolve such issues, analysts and material engineers must use intelligent solutions that show when, how, and why problems occurred.

Said CIO Akhisaroglu: “We decided to evaluate deep learning algorithms to discover any meaningful patterns. We selected eight promising algorithms out of the 92 we analyzed.”

Engineers, developers, and data analysts have several digital and hardware tools and solutions at their disposal that are based on contemporary technologies. However, in many cases, these tools and solutions are inadequate. Production environments can be vastly different.

It is not a matter of simply capturing the right parameters and signals to improve the quality of outputs and the final accuracy of the model. Working conditions may vary as well. Different methods of maintaining, adjusting, and operating production assets may significantly impact the quality of model outputs. The journey toward perfection may be winding and rocky.

ROI, of course, must be extremely compelling. My experience tells me that fast solution prototyping is essential. A model’s functionality should be tested quickly, in a maximum of 3–4 weeks. Lead time between the start of development and deployment of a solution (getting accurate and reliable outputs) can take months due to the learning process and model adjustment.

This is why the ideal production type for this kind of deployment is a highly asset-intensive environment — where a single stoppage can cause millions of dollars in damage.

Standardized Solutions: These are refined, highly scalable solutions based mostly on image-recognition principles. The accuracy of the final output strongly depends on the number of anomaly samples: The more samples, the more accurate the model.

For basic quality control tasks, it may take 4–6 not-OK (“NOK”) samples to teach a system run via a camera positioned on the production line. This is fully sufficient in high-speed production. Theoretically, such a solution may even provide 99.99% accuracy. However, real life shows this rather theoretical value is reached only during simple quality inspection tasks.

Size and surface integrity play a big role in whether such solutions can be effectively utilized. The smaller and simpler it is, the more effective the control outputs.

Solutions that leverage AI-powered tracking and analysis of each assembly step, including cycle-time analysis, seem very promising. Such solutions can identify production anomalies and bottlenecks, improving throughput by tens of percentages.

They can also significantly speed the discovery of quality issues — in some cases reducing discovery time to minutes. Standardized solutions may easily achieve an ROI target of 1–2 years. Time to Scale and Time to Accuracy may be as little as days — or even hours.

Don’t Waste Time — Just Start!

Enterprises should have realistic expectations about leveraging AI in production, quality control, and maintenance. AI is not a miracle drug that solves every emergency. You can forget about a “supreme power” that may turn against you, like in some “war of the robots” novel.

AI can, however, provide a solid range of use cases. Your focus should be on what can be achieved by AI-powered solutions — and how much effort and money you can invest in them.

In many situations, the benefit is not only obvious KPIs (e.g., production line availability or overall equipment efficiency) — it is also secondary impacts that improve sustainability and quality, resolve problems in the production process, and boost customer satisfaction.

As always, the creation of digital silos must be avoided. To unlock the full power of data, AI-powered models must be integrated with enterprise systems like manufacturing executive systems, ERP, and advanced analytics tools. Data can be analyzed in more than one area and contextualized. Different analytics solutions can be combined to squeeze out unexpected insights.

Start now!

As you push forward, however, do not underestimate organizational, technology, and management support. CIO Akhisaroglu offers this final insight: “Looking back, we lost a lot of time in starting these pilot projects. We should have started earlier and been more proactive in collecting data from all available problem-related resources. We faced many challenges with servers, databases, and processes, but it is clear that we worked harmoniously to meet our needs quickly and effectively.”

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In my series, I’ve looked at the different ways in which data can be deployed to help people make decisions. Over time, more of the decision-making process has shifted from people manually collating data from different sources in their heads to using data sets that can be automatically joined together. This networked approach to data makes it easier for people of all skill levels to work with data.

This has evolved to make more use of automation over time. By making it easier for individuals to link up data sets and form connections between these assets, businesses have been able to spread analytics to more users within their organizations. This is now being taken further with machine learning.

Spotting patterns in data can be easier for machines that can work with huge sets of data from multiple systems. For businesses that have data spread across many different ERP systems, this can make it easier to reconcile interactions around the world with customers as well as managing the supply chain that links products, services and providers together. For large enterprises that have grown through acquisition or mergers with other businesses, this internal infrastructure can have volumes of data that could provide great insights if they can be joined in the right ways.

Building out how to use insight across the business

For companies with multiple applications in one area, getting consistent data can be a massive challenge. Even when the same application is used, the requirements for national or regional variations can often mean lots of customization has taken place. Linking up each record so that the complete picture is visible is therefore not easy.

Taking manual work out of this process can help speed up getting access to results. Using machine learning and automation, getting that single overview of all customer activity from multiple systems is possible. However, this should only be the start for how that data can be used.

For individuals, the value from this automation is how quickly they can bring in their own data and have it integrated with this centrally managed data set. Can they join up their own information on customers — complete with their own customer identifiers or information sets — with other data sets automatically, and then get recommendations on how to present this data back out for their own use? How about for sharing with other groups that have their own questions to answer?

The role for machine learning and automation here is that each of these tasks should not require a data scientist to come in and prepare the data for others to use. Instead, automating the internal preparation process should make it easier for all users to get the answers that they are looking for.

Similarly, sharing this data outwards is a challenge. Simply delivering a report or spreadsheet of your findings can help you, but it won’t necessarily help others to use the results. At best, you end up having to share the same sets of data for people to go through, which results in duplicate work and additional maintenance burden; at worst, discrepancies in the data lead to wasted time or mistakes in judgement. Instead, networking people with these data sets can help them find out what insights they actually want to discover.

Getting insights out to more people with Machine Learning

For machine learning implementations, getting this link between business goals and the decisions that people make every day relies on data. Machine learning can be used to automate the process for analyzing new data against current information, and then recommending potential decisions to those that are involved. However, this has to be seen in context.

Traditionally, trained data scientists are required to create the right frameworks for machine learning. Machine learning implementations can be based on supervised development and training of algorithms and sophisticated analytics models to reflect an activity. Once these initial projects are set up, the data science team has to run more real-world data through their models and then score them to judge how accurate they are. Following this work, the team will work to find the best fit for all the data that is involved and retrain the models to be more accurate. This process is manual and depends on data scientists with an advanced set of skills.

Today, companies are looking to automate more of the steps involved in sorting the data, creating these models and scoring them for accuracy. There are several reasons behind this: first is that there is a dearth of skilled data scientists available for companies to recruit. More than 2.7 million data science and analytics roles will exist by 2020 according to research by IBM, and demand for these skills will grow by 39 percent. This also means that there is a premium for roles with these skills, putting average salaries well over $80,000 and roles with specialist skills over $150,000.

While these particular skills will continue to be in demand, there are also opportunities to automate a good portion of this process decreasing the need for human intervention across the whole process. At the very least this means that data scientists can accomplish more with similar or less effort; but, more likely, it can remove the need for specialist skills and decrease the sophistication of skills required. As a result, automation can extend these capabilities around machine learning to more Line of Business teams.

All of a sudden, instead of having a handful of data scientists working on complex models for months, we now have many people throughout the organization able to bring data together, spot patterns and opportunities, and make predictions about the business on their own and in less time.

Does this replace the data scientist? Absolutely not. Using machine learning to automate the analytics process invariably means reducing its sophistication and limiting what can be achieved. But that doesn’t diminish the value of automation. On the contrary, the value is in the greatly increased scale. By helping set guard rails on what can be done with data, data scientists can help those line of business teams to help themselves. Now, many more people are producing insights across the organization. At the same time, those data scientists can make use of their full set of capabilities to look in more detail at harder problems or more varied issues where the potential to automate workflows has not yet been considered.

There is still enormous benefit in taking some of these capabilities around machine learning, making them vastly more consumable through automation, and then scaling out the results to the broader organization to make more use of advanced analytics. Just as data scientists are initially needed to get machine learning working properly, automation relies on data science principles and efforts at the beginning to set the right course for future users to benefit.

By looking at the whole process and other teams’ sets of data within a whole network, machine learning can be used to scale up analytics across the whole business. This will include making recommendations that affect wider workflows and activities in the best way possible rather than looking solely at the individual or the team.

Networking people and data

This approach to analytics should provide better levels of insight to everyone across the business. However, it relies on everyone being able to get those insights delivered to them with accuracy and timeliness. Networking people together around sets of data should make it easier to collaborate, rather than relying on those individuals seeing reports or shared documents.

It’s possible to go through business processes in more detail and look at the role that data can play. This can then encourage more use of data within the business as part of a feedback loop. The mainstream adoption of machine learning and analytics can make the results available faster, but ultimately it comes down to helping the people involved to work more effectively. Scaling up this increase in effectiveness comes down to making analytics easier to consume across the business.

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