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Uber vs. Lyft: A Tech Strategy Should Mirror the Business and Never Stop Evolving

March 9th, 2020

As far as the general marketplace is concerned, Uber and Lyft are competitors who do the exact same thing. But for technologists, the two companies approach development, infrastructure and technology in distinctly different ways. Their strategies provide a series of lessons for organizations on why technology deserves a personalized touch when it comes to driving business growth.

Consider regulatory filings for Uber and Lyft. Lyft is all about the transportation services business. Uber is more ambitious, with plans to build a marketplace and technology platform that moves beyond ride sharing transportation with additional offerings like Uber Eats and Uber Freight.

The Right Partner is Everything

Both Uber and Lyft are heavily invested in the cloud, but Uber employs a hybrid approach with a highly automated infrastructure, co-located facilities and partnerships with multiple cloud vendors. The company uses third-party cloud computing services and global data centers to quickly and efficiently scale handle business needs without compromising its focus on building better products or taking on additional costs.

Lyft, on the other hand is betting big on Amazon Web Services for its cloud architecture, with spend estimated at some $300 million over the next few years to ensure it can withstand business surges.

The Power of Machine Learning and AI

Uber mentions artificial intelligence, machine learning and the word algorithm numerous times in its IPO filing. Why? Data science will factor heavily in its business plans. Consider, to leverage the company’s research, patented technologies, open source software and data from countless customer trips, it will need machine learning and artificial intelligence to automate and verify millions of data points each year, and ultimately help make better market decisions.

Lyft isn’t as forthcoming about which tech it plans to use in its IPO filings, but the company does mention that rides “inform our machine learning algorithms and data science engines.” Data insights likely facilitate a better, more efficient ride experience for its customers. They probably help the company match local market demands, optimize routes, etc. as well.

Technology as Business Enabler

Uber has built its own proprietary payment, routing and marketing technologies using real-time algorithm-based decision-making for Uber Eats, Uber Freight and its other offerings. The tech has influenced quite a few of its marketplace capabilities. From its ability to use trend data to predict supply and demand, to algorithms that evaluate variables like weather, time, distance, traffic, and meal prep times for Uber Eats, the company has made technology a key driver in not only how it operates, but its plans to grow and continuously improve business.

Lyft also uses historical data and machine learning capabilities in many of the same ways to predict driver availability as well as allow it to match rider supply and demand. Algorithms and data science will help to inform service levels, increase efficiency, facilitate product development, even increase driver earnings.

Never Stop Evolving

Business and technology have one important thing in common: They should never stop evolving. Both Uber and Lyft know that continuous improvement is key for their success, and technology is a key enabler.

Uber uses DevOps and agile software development to keep both tech and business fluid. The company embraces experimentation, testing, prototyping, and constantly refines its products to ensure its customers get the best possible service. City districts or neighborhoods become ideal focus groups with which to gather feedback, try out new features, increase performance, gauge how they’re doing, and of course, make any corrections necessary.

Lyft’s business model organizes product and development teams that integrate data science, analytics, design and engineering. The company also uses a lot of open source software, which is often quite innovative, as it exists outside the constraints of normal tech and software development cycles and is continuously developing and improving.

Both companies are very future-forward. For instance, Uber and Lyft are both investing in autonomous vehicles, which may impact their transportation networks substantially in the not too distant future. The companies approaches differ, however.

Uber’s Advanced Technologies Group is focused on autonomous next gen transportation, and the company is partnering with well-known manufacturers like Toyota and Volvo. Lyft’s open autonomous vehicle platform will connect its network with partners.

For example, Lyft partnered with Aptiv to deploy a fleet of autonomous vehicles in Las Vegas. The program has logged more than 50,000 trips since it began last year. In addition to partnerships, Lyft is also building its own autonomous vehicle system in hopes that a combination of partnerships and intellectual property will be a competitive advantage in the marketplace.

The cloud, AI, and machine learning will factor heavily in both Uber and Lyft’s future business growth and operation plans. They will need talent with advanced level skills in all of these technologies, and the marketplace likely won’t be able to provide that talent at the volume these companies – and others – will need. That means they’ll have to leverage another kind of development partnership to build the skills they need: a learning and development partnership.

Business strategies and technology don’t mean much without skilled talent to execute and deploy them. Active partnerships with experienced, proven training organizations like DevelopIntelligence will be invaluable not just for disruptive, industry-bending companies like Uber and Lyft, but for any organization that plans to move in step with the times and use technology to grow in the global marketplace. Training is like business and like technology – to be effective, it must never stop evolving.

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The Right Training Investments Are Worth Every Penny

March 9th, 2020

The importance of data, it’s measurement and use in making more effective business decisions is now universally accepted across almost every industry. The training industry, however, still lags in its efforts to measure workforce development investments.

Unfortunately, there are no universal standards or metrics with which to measure how well people learn, and the common learning measurement practices companies engage in are often more subjective than objective. So, while it’s not impossible to get hard numbers to say, this training program created this business impact, many training leaders don’t bother – it’s too much work. They fall back on their certainty that development has a positive impact on the workforce, and therefore it has a similarly positive impact on the business.

Unfortunately that attitude isn’t cutting it these days. Resources are tight, budgets are even tighter, and leaders have to prove their worth with more than theories and beliefs. We know that talent is key to a successful business, and we know that training that talent moves the business needle forward, but we have to be able to quantify how much. Learning leaders know it too, and their measurement practices are evolving.

Before You Measure, Set the Right Goals

One of the most important things when measuring training impact is to set the right goals before an intervention begins. Further, training goals and business goals should be closely aligned. Explicit goal setting makes it easier to determine if a training program is a success.

Let’s say an organization has contracted a technical training company like DevelopIntelligence to create and deliver a customized class on Java programming for entry-level developers. Before the class begins, learning leaders should have identified what they hope to accomplish long term. What business problem will this training help to solve? How will this cohort use the Java programming skills they’ll acquire in class on the job? Are there certain business critical projects where this skill will be necessary?

If an organization is has trouble identifying the right goals, a good training partner will help them to tease out the goals that make the most sense, and then create customized learning solutions to achieve them. Planning in advance allows the training partner to better identify any skills’ gaps within the organization, design a program aligned to a company’s business objectives, and deliver the biggest outcomes and ROI.

Further, to add more depth to a value determination around training ROI, learning leaders might want to follow up in three months or a certain period of time to see how well learners have been implementing what they learned on the job, and what impact their performance has had on their department, project, etc.

If You Don’t Train Them, Someone Else Will

Another consideration leaders need to consider when it comes to training as a business investment is rooted in organizational culture and training’s impact on talent. Basically, if you don’t build a learning culture in your organization, making it a priority to frequently offer targeted training programs, technical talent will leave and find an organization that will.

According to data from the Execu/Search Group’s 2019 report “The Employee Experience: 4 Ways to Attract, Engage, & Retain Employees in Today’s Competitive Market,” 86 percent of professionals said they would change jobsif offered more opportunities for professional development.

Technical professionals know that to thrive they must continuously learn new skills and keep up with the rapid pace of technology. It’s really not even a question of thriving in the workplace, it’s a question of surviving in the workplace. A tech pro’s value is linked to his or her ability to provide value by creating business applications for emerging technology and/or ensuring the tech infrastructure an organization already has in place runs smoothly and efficiently.

Training and Culture Work Hand in Glove

As a practice, measurement gets easier the more its done. There may even be metrics learning leaders can adapt from related aspects of an organization’s talent management practice. Training impacts most facets of the employee lifecycle from recruiting to engagement, performance, advancement and retention, all of which play a role in organizational culture. Consider, a State of the ROI of Learning Report from Udemy for Business revealed that 30 percent of high engagement companies spend an average of $2,000-$2,500on each employee annually. Only 7 percent of low engagement companies make the same investment.

More important, learning must be well positioned as a promoted asset as soon as talent enter an organization to reap benefits associated with increased performance, more effective onboarding and employees becoming productive faster. “Since we don’t know exactly what skills the future will demand, a growth mindset can spell the difference between keeping current with the latest shifts instead of struggling to catch up,” said a June 2018 Forbes’ article. “It’s critical to invest in learning from day one, even for employees just entering the workforce. From programming languages to personal development, continuous learning will…foster an agile and innovative workforce.”

While the frequency and quality of training offerings is key, it’s not the only important factor in establishing a thriving learning culture. Culture means creating not only the right training content and delivery systems – or aligning with the right training partner to provide those things – it means giving technical talent the time and space they need to fully engage with learning when they need it.

For instance: Does your organization encourage mentoring and peer-to-peer learning? Does it empower and enable leaders and employees to experiment and try new things knowing that it’s safe to make mistakes? Does it reward these behaviors, knowing that they are the foundation for growth and self-improvement? Do leaders actively participate in training programs? There is nothing as effective as the workforce seeing a specific behavior actively modeled from the top of the house.

Conclusion

Things move so quickly, providing the right training and delivering it the right way when people need it is one of the only ways organizations can keep pace with technology change. In a strong learning culture, technical talent will be empowered to pursue opportunities to learn whenever they need them.

Learning leaders can build a culture where learning thrives by establishing partnerships with training providers that will work closely with them to provide the latest, high quality training on emerging and established technologies, and then setting specific goals they can measure to prove that strategic training interventions have a clear ROI for the business.

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Train for These 5 Machine Learning and AI Roles Now

March 9th, 2020

Tech companies aren’t the only organizations actively looking for experienced talent in emerging technologies like artificial intelligence (AI) and machine learning. Leaders across a mélange of industries like financial services and various types of consulting are strategically sourcing top technical talent to power future business goals – which means competition is stiff for technologists with these skills.

Unfortunately, because AI and machine learning are still evolving rapidly, the market doesn’t have the talent yet. It’s a classic case of high demand, low supply, so savvy organizations are ramping up their internal learning strategies and securing key partnerships with learning solutions providers to develop what they can’t find externally.

To exacerbate the issue, most companies don’t have the internal learning resources and expertise needed to create highly technical training programs to bring their current talent up to scratch. So, they take shortcuts. Poaching is common, and even top players like Google and Facebook routinely pinch talent from other companies.

For organizations that don’t have the capital to compete with salaries and other benefits, training is a viable solution to secure these high-level skills. Here are five machine learning and AI roles you need to train for now to ensure your organization doesn’t get left behind.

Cybersecurity Engineer

Sometimes it seems like, another day, another hack, right? There’s no shortage of stories about well-known global organizations falling prey to security breaches, and their customers valuable information – not to mention their reputations – takes the hit. It’s why cyber security engineers have topped many lists for in demand tech talent for the past few years.

These engineers design and implement security systems to stop external threats. They need a broad base of knowledge and skills to develop security plans and policies. They also identify system vulnerabilities, maintain those security systems, track and respond to security incidents, investigate breaches, and otherwise assess and reduce an organization’s risk and stress in an occasionally dangerous marketplace.

Machine Learning Engineer

Machine learning engineers are in extremely high demand and have been for the past few years thanks to the increasing use of AI and machine learning across various industries. Companies are trying to figure out how to optimize and automate their businesses, and make their day-to-day work lives easier and more productive. These highly skilled programmers work with complex data sets and algorithms to train intelligent systems and develop AI machines that give businesses a competitive edge in the global marketplace.

Chief Data Scientist

Contrary to some beliefs, advances in artificial intelligence and machine learning will not make the chief data scientist role obsolete. On the contrary, AI and machine learning are just one subset of data science. One estimate suggests that by 2020, there will be more than 2.7 million data science and analysis jobs available in the US alone as organizations try to make sense of and use the deluge of data most have to contend with daily. Filling those jobs, however, will be a challenge without training.

The chief data scientist needs a broad range of skills including research and data collection, and should know how to establish an AI architecture, develop new software applications and strategies as well as create statistical models. Those in the role will make use of AI to simplify repetitive tasks so they can focus on identifying and communicating data science-related business opportunities.

Artificial Intelligence Architect

AI architects are in high demand as well, and require not only considerable experience to be effective, the role needs a deep and vast array of skills to help develop frameworks that support enterprise technology and shared service functions that use AI and cloud computing. Unlike in some other AI roles, technical expertise and strong business acumen are a great combination, but not one that’s thick on the ground for recruiters.

AI architects have to not only create and maintain architecture using AI technology frameworks, they must be able to translate client needs into actionable business solutions. Like the chief data scientist, those in this role have to analyze and leverage data to make business decisions, which requires a deep and vast understanding of AI applications, multiple programming languages like Python, specific infrastructures and machine learning. It also helps if these technologists are plugged into all of the latest trends.

Artificial Intelligence Research Scientist

It’s the artificial intelligence researcher scientist’s job to understand and develop AI systems through a long-term, academic lens. An individual in this role often partners with other technologists to develop models for trading, market and alternative data. The person may even help recruit other AI talent and represent their companies in external forums.

Research scientists are often more focused on scientific discovery than with workable applications for their findings. Their focus is on innovation and experimentation, which is why partnerships with fellow technologists who can apply their discoveries in a practical way are so critical. Many are experienced in both data and computer science, so they can contribute in a data science sense and write code when necessary. The nature of their work is emerging, just as AI is emerging, so their skills must constantly evolve as well.

Conclusion

Senior-level engineers with knowledge and expertise in AI and machine learning have more job opportunities than most. They’re in high demand, and organizations need alternate strategies outside recruiting to secure this kind of talent. That’s why highly customized, high-quality technical training is so important.

Companies have to find ways to develop in their workforce what the market cannot provide. Further, choosing the right technical training partner is not just about finding the classes or programs around the desired subject matter. It’s about setting talent up for success using proven learning and development strategies like deploying experienced practitioners as teachers, and creating intense labs where theory takes a back set to practice as learners write code, and engage in targeted peer-learning exercises with their fellow participants.

These kinds of labs and peer learning opportunities tap into another skill set that all of the aforementioned technical roles need – soft skills. Each of these valuable technologists must be able to effectively communicate their findings to various stakeholders up and down the chain of command. Learning solutions providers like DevelopIntelligence can hone technologists’ ability to communicate complex ideas quickly and simply while simultaneously developing deep technical knowledge and problem-solving capabilities. This is what businesses need to survive today and thrive tomorrow.

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Five Steps to Take to Give Your AI Project a Fighting Chance at Success

February 18th, 2020

In 2018, industry research firm Gartner made a rather disheartening prediction: Some 85 percent of AI projects are doomed to failure. Wow. That’s a lot.

Keep that number at the back of your mind. Now consider how many questions an organization must answer when it comes to this kind of project implementation: Do we have enough data? If not, does that data exist, and how will we gather and categorize it? Are the algorithms powering our systems sound? At a foundational level, what makes more sense now that we’re considering implementing AI? Should we adopt certain features from existing software platforms, or build a customized model?

The answer to that last question is, it depends. The decision to replace known legacy systems and processes with a new AI solution is not an easy or a quick one. Customization is always a good idea when it comes to maximizing results for a specific organization, but there are obvious drawbacks in both the associated risk and the expenses related to appropriate staffing, development, testing and deployment.

After all, investments in the time and training needed to develop successful AI projects don’t come without cost. Further, when you start from scratch, there are no guarantees because there is no previous foundation of success from which to borrow.

To increase the likelihood of AI project success, organizations can do the following five things:

1.   Lay the right foundation: Assessment is key before you begin an AI project. Employees and customers already know how products and processes work, which means they have expectations about what you can deliver. This makes the decision to take a chance and change a legacy system in favor of an AI implementation even more difficult.

This initial assessment phase takes time and can be costly, but it’s critical that leaders realistically assess what exactly they have to build in order to lay the foundation for project success. The results of that assessment will come in handy. According to a study from Pactera Technologies, a global technology company, some 77% of top tech leaders indicate that pushback from senior management, and their failure to impress CIOs, often stand in the way of their efforts to get new AI projects up and running.

2.  Determine what’s right for the business: To mitigate some of the risks associated with a new AI project, and reduce the chance of failure, ask – and answer – key questions:

  • Why do we want to use AI?
  • Are we trying to build new, better products?
  • Are we trying to increase our time to market?
  • Are we trying to use AI to increase efficiency?
  • Are we trying to use AI to decrease security or compliance risks?
  • Are we trying to use AI to increase profitability in specific areas of the business?
  • Would another type of project produce a similar, positive result?

The answers to these questions will help to drive a focused AI project strategy.

3.  Measurement matters: At the end of the day, AI is another technical tool the business has to achieve critical milestones or objectives. So, as is the case with any new project, measurement is important. Leaders have to establish early what success looks like, how they will track the AI project or experiment, and at which intervals, and then have a transparent method in place to audit and report out developments regularly.

There’s an old adage that says, what gets measured matters. Tech leaders don’t want a lack of measurement to make it easy for their peers to abandon the AI project at the first bump in the road.

4.  Train and collaborate: AI tools are evolving rapidly, but to use them effectively, technical professionals need data science skills. Because this technology is still evolving, however, it’s tough to hire individuals with the depth of skill that companies require. So, the logical solution is to train existing talent.

If your organization doesn’t have the skills needed in house, look for a training company that has deep expertise in AI and real-world experienced instructors to offer. It’s also important that the aforementioned company act as a business partner, so collaboration is critical. Technical training partners like Colorado-based DevelopIntelligence, for example, can facilitate an organization’s change management efforts as well as create a customized curriculum that will provide the necessary AI skills technical talent need to meet specific business goals.

Training alone is not enough. Your training partner should be willing to become intimately familiar with your organization’s business strategy, and align any training offered to the specific individuals who take it. That way, the people in those key roles will be able to advance the organization’s business strategy and goals more quickly post-training.

5.  Celebrate the win: Because AI projects are often fraught with difficulty, when they succeed, it’s definitely cause for celebration. So, when projects work, make sure senior leaders know it in a timely fashion. Keeping stakeholders abreast of developments will help to increase and sustain their support and engagement for the next project iteration.

AI projects are not intended to provide quick solutions. They’re best suited to enhance and help build a long-term business strategy. To increase the chances that your AI project will be a success – and keep that dismal 85 percent failure specter at bay – keep these five steps in mind. Communicate consistently, set expectations early and realistically, establish the business case, and outsource to find the training and support your organization may lack. Good luck!

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How Does the CTO Decide What Technical Training Is Needed?

December 11th, 2019

A CTO, or chief technology officer, evaluates short- and long-term organizational needs, and makes capital investments in technology to help that organization reach its business goals. It’s the highest executive position for technology in a company, and it’s a strategic role. That does not mean, however, that it’s a stagnant one. Learning is very much a part of the picture, especially when it comes to directing training efforts for technical teams.

The CTO will likely work with the CLO, or chief learning officer, or other organizational learning leader to decide what training would be most beneficial. But to choose the right courses or build a curriculum that will produce business impact, the CTO has to do several things.

Maintain a Pulse on the Organization and the Industry at Large

The CTO has to solve technology-related issues and make sure an organization’s software products solve key problems for its intended audience. That requires an evolving knowledge of emerging technology trends and industry changes, and intimate knowledge of what is going on in the business as well as what strengths and weaknesses exist in the organization’s technical team.

Where do they excel? Where is there room for improvement? What new skills will help to optimize current workflows and future proof the organization? Holistically, what will accelerate the team’s growth? After all, it’s the developers and engineers who will execute the organization’s vision, deliver on potential, and achieve the business results that will prove technology’s value – and secure additional funding for more training.

For instance, if it’s a retail organization, machine learning will be critical as ecommerce and online sales channels must be built and optimized to suit rapidly changing customer and product needs. Machine learning has applications for a lot of the things customers encounter in everyday life: online recommendations and offers, linguistic rule creation so that companies will know what customers are saying about them on social media, even fraud detection.Python is one of the best programming languages for machine learning. Does the team have the skills it needs to handle it?

What about big data? It’s important for many companies in a variety of industries, and it’s a critical technical training area given the increased scrutiny on data privacy and security. The CTO must determine where is the current team lacking when it comes to skills around proper data collection, storage and data mining? How are their skills in Apache Spark, R, Java, C and other relevant programming languages? That information will inform how training is built, delivered and received.

A savvy CTO will even ask technical team leaders and members for input. Why not? They’re in the trenches. They know better than anyone what they lack to succeed on the job. VMWare polls it’s engineer population annually to identify what training offerings they want. If those align with the company’s business goals and product offerings, the company will offer that training.

Lead with Data, and Keep an Eye on the Future

Taking a data-driven approach to training can be powerful, but it requires goal setting and establishing key metrics to determine whether or not business goals and objectives have been met, and training engagements are successful. Then, based on established goals, the CTO can iterate what training will produce the expected results.

This requires that a CTO actively monitor the technical team, and solicit feedback in order to identify what skills are lacking. A data-driven training strategy will allow the technical team to prioritize its resources and energy to reach quantifiable goals, and create space to discover innovative ways to get work done efficiently, achieve business success, and sustain a competitive edge.

The CTO knows how to break down the dynamics of a technical problem. He or she will also know from a strategic perspective how best to use technical talent to push the business forward, and ensure the technical team is learning, growing, and building valuable relationships. The right training is a key part of that.

Choose the Right Training Partner

This part of a training strategy is almost as important as the strategy itself. It goes without saying that the right training partner will have the very latest offerings taught by expert practitioners and skilled facilitators. But the right training vendor will also act as a business partner.

Technical training company DevelopIntelligence will not only handle logistical details around scheduling and planning time for training so that technical talent have the mental space to learn, the organization’s guidance can be invaluable when it comes to redistributing the technical team’s work, or simply choosing the optimal time to conduct training in the first place.

The right training partner can also help fill gaps in a CTOs training knowledge in the same way the CLO would to maximize the relationship, and respect everyone’s time and investment. The training partner will insist on a deep understanding of the organization’s training goals and business objectives. They will inquire what are the established measurements by which to determine success for the learning engagement? If need be, the right training partner will even help to create those goals and success measures.

For instance, what should a training cohort know how to do by the end of the class? How should they be applying their new skills on the job? What follow up should be arranged to refresh classroom knowledge, and keep those valuable peer learning conversations going once learners are back in the workplace?

The right training partner will ask a lot of questions, and invite the CTO to share their concerns about the business and about the training they hope to receive. Why? At the end of the day, the training vendor has to provide value.

The CTO will demand it because the best training is rarely done simply for its own sake. It’s done to fulfill a need, whether it’s a current business need, or one that will be needed to shape an organization’s profitable and productive future.

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Do You Have the Right Training Partner to Meet Your Annual Goals?

November 20th, 2019

Training and strategic business objectives are not mutually exclusive. Ideally, there will be close alignment between the two – if an organization wants to get its money’s worth.

Unfortunately, that doesn’t always happen, and it can be especially problematic when training is technical in nature. Technology changes so quickly, organizations can’t afford not to train their technical talent if they want to remain innovative and competitive. Most organizations also can’t afford to deliver training that is ineffective.

Like most executives, learning leaders have to justify their department’s expenses. To do that they must set goals and meet them, and those who are working with limited staff – which is a good number – often rely on vendors to pick up where their internal resources leave off.

It makes the training vendor partnership a critical one, and savvy learning leaders know how to navigate it well. That includes everything from ensuring the quality of the actual training product to finding a vendor that can provide strategic consultation and help to create that oh so vital learning-business alignment.

That alignment begins with goal setting. Ideally, organizations should set their training goals as far in advance as possible. “We recommend at least two quarters in advance no matter what the organization is so you can be prepared for business planning,” said Rya Tillman, customer success manager for DevelopIntelligence.

Every organization’s training needs will be different, but anticipating those needs in advance helps to create the best plan of action. Understanding what an organization needs to accomplish long term will also help the right technical training vendor craft customized learning solutions to achieve those long-term goals. “Planning in advance allows your training partner to better identify any skills’ gaps within the organization, design a program aligned to your business objectives, and deliver the biggest outcomes and ROI,” Tillman explained.

But all of that good stuff hinges on choosing the right training partner. That’s not always easy. A training partner has to do more than just provide training. An effective partnership requires that a vendor really dig into an organization’s strategic goals. The vendor has to listen, collaborate, learn along with a company, and most important, care about the company’s success.

Tillman said many training vendors appear to listen attentively, but only provide cookie cutter solutions that fail to deliver the impact needed for a successful outcome. “I would recommend looking for a partner who understands your business objectives and understands that the training audience is unique in every organization.”

A training partner should offer training in different modalities, and whether an organization wants in-person or virtual training, that partner should be nimble as learning needs and goals change throughout the year. Tillman said one way learning leaders can ensure they’ve selected the right training partner is to have as many calls up front as needed so the vendor knows exactly what are the primary business drivers for training as well as the expected outcomes for engagements. For instance, will new hires be expected to demonstrate a certain level of proficiency in a programming language? How will the vendor measure that proficiency?

Sometimes even when you do that pre-work and ask those questions, things may still go awry. How a vendor responds post-class or engagement is important, too. For instance, does the vendor pivot quickly to shape training based on class feedback? “A company that is confident in its abilities will usually offer some kind of money back guarantee or will redo the class,” Tillman explained. “You want to work with somebody who can provide that guarantee up front.”

The right partner will also offer a great deal of experience in specific types of technology, and vendors’ knowledge, products and services should always be evolving. They should provide new offerings on a quarterly basis, not run the same classes over and over, year after year. Ultimately, whether it’s multiple day, in-person-led bootcamps or a one-day virtual course, a training partner should align with an organization’s needs, not the other way around. So, look for a partner that can deliver the best ROI without sacrificing quality. “At DI, we’re so confident in our services our ROI is guaranteed,” Tillman said.

A company will know it has chosen the wrong training partner if any of the following happen:

  • A class fails to deliver the expected outcome. This may indicate the vendor didn’t listen and fully understand the strategic business objectives behind that learning. Therefore, they may have offered the wrong content.
  • The training partner fails to listen to your organization’s needs or fails to understand skills gaps. Again, this will result in the wrong class offerings, or the wrong instructor selection.
  • The learning partner continues to deliver the same offering without measuring and monitoring improvements. A good learning partner will always offer course adjustments and continuous program improvements each time a course runs even if it’s the same content. Measurement is a key part of making that happen.

Training vendors that are confident in their abilities will offer some type of class, money or ROI guarantee. A truly valuable training partner will own up to mistakes if things go wrong, and make an effort to right those wrongs, whether that’s redoing a class for free or reworking customized content.

“The main thing is you’re not just hiring a vendor,” Tillman said. “You want a strategic partner that acts as that extended arm to your organization, not just somebody you’re hiring to throw a class on here and there.

“A good partner will act more in a consultative manner rather than just, ‘here’s a technical training class. We hope it meets your goals,’” she explained. “At the end of the day it’s finding that partner who really cares and takes the time and effort to understand the company, the goals at hand, what that end outcome needs to be, and how to get there.”

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Four Ways to Create a Successful Technical Training Program

June 28th, 2018

There are few sure things in life, but there are a lot of probables. You’ll probably pay taxes, you’ll probably waste time looking at social media on your phone, and at some point, someone will probably make you angry. However, when it comes to technical training, you can be sure that your chances of creating a successful program – one where your audience learns, enjoys their time learning, and then applies their new knowledge on the job immediately – increase if you do the following four things.

Kerry-Ann Douglas-Powell, an application training specialist for Ontario’s Ministry of Community and Social Services, said whether learning leaders are running an internally developed technical training program or building one with a vendor’s help, the first, and probably the most important thing needed to promote program success is to: (more…)

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10 Myths About Professional Training

December 5th, 2017

Do those in charge of hiring really value IT certifications? Is e-learning really as effective as in-person training? Many people have an opinion on the positives and negatives surrounding professional development, but what are the facts? In this infographic, the team at findcourses.com has delved into ten common myths surrounding professional training and development. (more…)

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A Look at Learning in 2018

October 30th, 2017

Although learning leaders will continue to grapple with some evergreen challenges in 2018—balancing global and local control, for example—changes in technology and talent will require learning leaders to reinvent themselves and their functions. Here are three questions to ask–and some fresh thinking to consider—in charting a course for the coming year.

Learning Governance

How do we execute on enterprise learning so that we seamlessly align with our businesses AND realize efficiencies at a global level?

We’ve heard and heeded the call to revolutionize learning for some time now. We risk irrelevance unless we’re positively influencing performance in a way that benefits the bottom line. This often means that we’re putting more energy into cultivating learning programs driven by the business side of the house, a task made easier given the ubiquity of free learning content.

Taking this to heart without executing on a purposeful strategy, however, can lead a thousand flowers to bloom across an organization without much of a discernable vision or a cost-effective way of governing them. Holding the reins too loosely undermines our efforts to impact the bottom line and often creates confusion and frustration for learners who are looking for a frictionless learning experience like the learning they do in “real life.”

As organizations assess these challenges, some are bringing learning more directly into the global fold. Others are experimenting with the development of next-generation federated models to define and leverage the benefits of global and local ownership of key learning drivers. As 2018 plays out, we’ll continue to see new ways of navigating this balancing act.

The Evolution of Learning

How do we leverage the profound changes driving career learning in the talent marketplace?

In the last month, we’ve heard about free career training, tools, and scholarships from Grow with Google. We also learned that the newly launched Woz University is providing skills training to get people into tech positions quickly and affordably. Meanwhile, the momentum continues to build behind white-collar apprenticeships and their role in filling skills gaps. Work changes fast, but we can now inexpensively train, upskill, and reskill with agility.

These career learning solutions inspire a host of questions. How can we leverage apprenticeships to source new talent that’s tailor-made for us? How can we grow our candidate pool by engaging with candidates in these learning pools, much as we would recruit at college campuses? How can we improve retention by reskilling, upskilling, and facilitating in-house career changes using these resources? What are the impacts of training and tuition reimbursement policies? What’s the potential impact of this learning on our leadership pipeline? The career learning marketplace has significant implications for learning and development and cements its role as a key enabler of talent strategy. It’s critical to stay ahead of this game.

Staffing the Next Generation Learning Practice

Is it time to consider a new role for learning practitioners?

Curation is currently a hot topic. It helps employees manage the avalanche of content that rains down upon them. It’s also now considered a core competency in the learning practitioner’s toolset, alongside instructional design, coaching, instruction, and other skills.

Until very recently, that vision worked fairly well. Today, however, the digital learning content realm is enormous and constantly changing. In addition, the burgeoning career learning marketplace is beginning to demand more attention than an ad-hoc internet search.

Perhaps it’s time to consider hiring for the role of Learning Broker: a “guidance counselor” or “concierge” for employees who is intimately familiar with the external learning marketplace. The person in this new role would stay abreast of developments in digital learning content and learner experience platforms. He or she would also establish relationships with providers of local and regional career learning opportunities. Internally, he or she would provide a single point of contact to employees in crafting learning pathways and plans, defining cost-sharing arrangements, and providing referrals to external resources. As organizations look more frequently to outsourcing learning solutions, the market will demand more devoted attention than we’ve given it, and a dedicated resource may be in order.

Although we’ll be looking at these three areas in the year to come, the old saw still applies: the more things change, the more they stay the same. Skills, technology, and markets may change, but the key challenge for learning leaders remains: anticipate where the market is headed, and plan accordingly.

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Does Job Specification Improve or Hinder Performance?

October 25th, 2017

Specialization is a common differentiation strategy in the business world. Finding a niche market and dominating it with specialized products or services has been an effective competitive strategy for over a century. Marketing gurus since the 1980s have preached the virtues of specialization. Employees have been told a similar story: Develop a specialized skill set for employment security.

There are numerous illustrations of successful companies that specialize. There are lending institutions specializing in home loans and construction companies specializing in commercial property, for example. But there’s a downside.

A business striving to corner a niche market may sacrifice their capacity to be agile. These companies replicate their specialism internally. They segment and organize the enterprise around functions or clusters of activity. This division of work is not dissimilar from the Ford Motor Company assembly line 100 years ago. Forming people around specific functions—while undoubtedly efficient—creates challenges in flexibility, responsiveness, and adaptability.

A barrier to agility is job specification. The time-honored practice of erecting clearly defined boundaries around jobs makes superficial sense. Narrow and clearly defined job design is about control—controlling the process and output of the worker. By restraining the work of the job-holder, employees can be held accountable for a few clearly defined tasks by management.

So what’s the price to pay for this clarity and accountability?

An agile enterprise has three workforce characteristics:

  • A highly skilled workforce.
  • A high degree of flexibility within its workforce.
  • Employees are in a continual state of honing and improving their skill set.

Job specification impedes these fundamentals, particularly the last two. The inherently inflexible job specification can, for instance, put the brakes on internal mobility. Learning skills beyond the explicit limits of the job-holder’s position description isn’t encouraged, and even discouraged. This learning barrier raises the question: Can the enterprise achieve the three agile workforce characteristics and—simultaneously—reap the benefits of job specification?

Flexible Deployment

An alternative approach—flexible deployment—doesn’t abandon job specification altogether. Flexible deployment means accumulating a range of experiences and retrofitting skills and competencies outside the scope of one’s job specification. In other words, it’s deploying the job-holder’s current specialized skills in a variety of ways beyond their job description. Flexible deployment builds new capabilities upon the foundation of specialization.

A professional public speaker can now diversify into giving presentations online. Or, a mechanic can work with customers to sell more products, leveraging off their product knowledge. In both cases, the specialist in deploying their current skill set in different contexts. They subsequently broaden their capacity. This is in the mutual interests of the individual and organization.

Flexible deployment doesn’t, however, mean becoming a jack-of-all-trades. It isn’t about transitioning from specialist to generalist.

Through flexibly deploying their capabilities the employee appreciates and understands a bigger scope of operational activity outside their job limits. The systematic deployment of competencies across an enterprise leads to organizational agility. Being adaptable and maneuverable contributes to greater responsiveness, increased speed, and—ultimately—more agility.

Where did job specification originate?

Job design and scientific management

Scientific management was the genesis of job design. Specialization has its origins in Frederick Taylor’s scientific management philosophy. Taylor broke the assembly line up into a series of specialist tasks and treated each component separately in his analysis of how performance could be boosted.

The driver for specialization was reducing waste and increasing efficiency. By identifying the best way (What Taylor referred to as the ‘one best way’) of performing a task, wastage in time, resources, and effort is abated.

Taylor studied each job in the factory to determine the least amount of time and effort required to complete it. Standardized methods of job performance were central to Taylorism. Each job on the assembly line would be meticulously planned in advance, and employees were paid to perform particular tasks in the way specified by management.

So, the present day people management practice of job specification originated from Taylor’s job specialization. A job specification entails breaking down a job into its simplest component parts and assigning them to a job-holder to perform the tasks in a consistent and efficient manner.

There are several obvious advantages to designing work around a job specification. Breaking tasks into small elements—with clearly defined repetitive processes—lessens the skill requirement of the job itself. It also decreases discretionary effort in the execution of the tasks and therefore lessens costs. Training timeframes are short and standardized, recurring tasks are broken into simple parts, and the success of the learning experience is likely to be high. But job specification has drawbacks in the transformed workplace we now work in.

Breaking a job into small and simple component parts can make the work dull and repetitive. Boredom can lead to lower levels of engagement and higher levels of absenteeism. Job specialization is ineffectual in dynamic and unpredictable marketplaces. In these volatile environments, the workforce needs to adjust its approach to respond quickly to changing circumstances. Selling products or services in a new market with a different culture, for instance, requires agility. A ‘one size fits all’ approach won’t work.

Taylor’s philosophy of scientific management paved the way for automating and standardizing work, virtually universal in today’s workplace. The concept of the assembly line—where each worker performs simple tasks in a recurring fashion—is Taylorism in action. Job specialization eventually found its way into service industries, too. One of the biggest success stories of the application of scientific management principles is the McDonald’s franchise operation.

McDonald’s was the first fast-food restaurant to incorporate the divisions of specialization; one person takes the orders while someone else makes the burgers, another person applies the condiments, and yet another wraps them. With this level of efficiency, the customer generally receives a product or service with reliable quality.

If specialization can be applied successfully in McDonald’s restaurants and is now a feature of many fast-food franchise systems, how is it problematic for agile performance?

Specialization encumbers adaptive behavior. Job specification hampers agility. Engaging people in repetitive and dull work is challenging. The job specification puts invisible blinders on the job-holder. People can’t see the forest for the trees. Flexible deployment is the antidote to these problems.