Organizations today increasingly use the combination of hardware, software, networking, and analytics that is IoT to gain insights from the data collected through varied and numerous connected points. So far, most companies have used IoT to increase efficiencies and reduce expense. Currently, however, some companies are using IoT to develop subscription services and digital products that are in turn driving new profit streams. A proper business model for monetizing data with the help of IoT products is based around marketing the knowledge obtained from the data extraction of the IoT products to third parties. This can even go as far as to offer the IoT product to as many users as possible, especially when such an offer is free of charge thus creating an attractive data offering for third parties. What all products seem to have in common - is that they always have a benefit for the customer. Similarly, they are also able to offer benefits for third parties at the same time, thanks to the aggregated data.
Data monetization can be implemented, for example, by using cars as “data collectors”. Sensors attached to cars continuously generate a wide variety of information, such as the quality of the road or the weather. This data can be offered for sale to corresponding companies. Another example is weather services, which typically demand a large amount of measurement data to provide precise forecasts. Cars can provide this measurement data via their sensors – which is particularly relevant for weather services in regions where there are hardly any measuring stations. This gives them the opportunity to access significantly more data and thus optimize their weather forecast. This creates a win-win situation: the owners of the cars use their vehicles as a measuring station, disclose measurement data and receive money for it. With the purchase of the data, the weather service can specify its weather forecast – and thus increase customer satisfaction. Data is not just limited to enhancing business efficiency or improving service, it can be used for regular sources of revenue for the businesses as well as for third parties. Organizations today are looking forward to investing in data that helps them better understand their customers and boost their marketing effectiveness.
Resilience, visibility, agility for scalability, and accessibility are going to be the main themes for enterprises operating in the new normal. More decision-makers are realizing the potential of IoT and are more likely to use it to monetize data collection. Many leading businesses on the internet have viewed two main phases in IoT. The first phase–where the focus–is to generate traction and the second phase is focused on monetizing.
What are the ways to enable IoT monetization? Let’s consider a few ways:
While perpetual licenses are slowly declining, the number of subscriptions has increased significantly in recent years. Instead of selling their hardware, manufacturers remain owners and offer customers the use of their devices and other services, including maintenance and support. As-a-Service subscriptions are usually offered on an annual basis and are subject to renewal. Subscriptions are particularly becoming widespread as the recurring sales of the models can be planned in the long term.
The pay-per-usage model promises what the name says: users only pay for what they actually need when they need it. Usage is recorded over a specified period and measured using a previously agreed metric. A corresponding system or process is implemented for monitoring. The exact costs can then be calculated on this basis. They increase or decrease depending on the resource utilization. Other features of a PPU pricing model include an advance flat rate and the possibility of fees and discounts in the event of over or under-utilization.
With this pricing model, customers pay once for a product. They can then use it for as long as they like. The maintenance and care of this solution is entirely in the hands of the operator and is usually covered by a maintenance contract. Even if this is no longer continued, the product remains the property of the customer and the rights to use it do not expire. Of course, it can happen that the access to newer versions and support is tied to a different maintenance contract.
Experts know; machines compute. Machines can learn only what experts teach them. The human eye is superior to any machine in recognizing patterns and only the human brain can assign meaning to patterns. The man/machine loop enables faster, continuous learning and more accurate predictions.
In an ecosystem, the focus is a shared platform by producers of hardware and software, service providers, and other IoT businesses, rather than a service or a product. Such a model allows the platform promoter to gain monetary benefits from end customers as well as other platform users. Platform users are charged for the listing as well as the end customer must give a share when a product is sold to the promoter. A shared platform benefits the participants in multiple ways.
In the era of digital transformation, organizations are facing a scale of change that is unprecedented in human history. The rate at which we are gaining data and metrics about our customers and the actions they take is increasing at an accelerating rate, thus demanding companies to always stay resilient and adaptive. The Internet of Things has the potential to introduce connectivity on new levels in the future world. While numerous agile start-ups are emerging, we are still at the phase where successful monetization is challenging. The rewards of a connected world will certainly be worth the wait, once organizations can gradually start to derive profitable value from the IoT.
As we discussed in the previous article, technology is evolving rapidly, and the internet has changed the way people communicate and handle routine tasks. The world is ultimately connected, and the answers to the most intricate questions can now be found on the internet. From cars that did only the job of transporting people from one point to another now pull off feats unheard of before. They can talk to us and other cars, make autonomous decisions and do more. We also have driverless cars that have almost bridged the gap between science and imagination.
Connected vehicles attract tons of challenges and problems during and even post-development. While you may think that the problems mostly stem from external factors such as road safety, the reality is that the concerns are internal as well. For a connected car to do its job right, its devices such as sensors, imaging devices, embedded systems, circuits, cloud solutions and more should all be working intact and in tandem with each other.
With intelligent automation enabling businesses to catch opportunities and make fast moves to tap potential, a new future is being written for the automotive industry. Companies that have spent decades refining the art of vehicle production are currently also reinventing themselves. However, taking a realistic look at the connected car industry also reveals multiple challenges and roadblocks that will need to be addressed. Let’s have a look at some of them!
All regions have specific regulations which have to be taken into account. Mentioning Europe, all vehicles must be equipped with eCall, an emergency notification system. The U.S. and other countries are expected to follow suit. Foreseeing this and accepting the challenge can prepare manufacturers in advance, thus saving a lot of cost and time.
The data privacy issue is being profoundly discussed and debated. Customers’ data is being shared with the car manufacturer and the mobile service provider. Currently, there’s little clarity on whether the carrier can or cannot share the data with external third parties for profit. In most cases, the automotive data is not protected and can be shared with third parties. For instance, data on media content we use in our car could potentially be shared with e-commerce representatives, facilitating targeted advertising on our digital devices.
Connected cars need to be connected not just to the internet but to the satellite for navigation and location tracking purposes as well. This enables drivers, fleet owners, and other stakeholders to trace the location of the vehicle and establish other safety protocols.
How many apps are enough? Car manufacturers are anxious about compromising safety by offering too many apps, which may cause driver distraction and open the company to additional liability. On the other hand, they need to do what it takes to remain competitive in the market. However, some companies have already come up with possible solutions, trying to meet the customers’ requirements and combine the necessary tools in one app.
One of the primary responsibilities of connected cars is to connect all the modules and components of a car and digitize them for optimum insights generation. When devices and sensors take over the responsibility of monitoring car and components health, they can consistently generate data and information on probable malfunctions, faults, errors in components, service requests, upgrade recommendations, and more. But for that to happen, connected cars need the right applications, processing algorithms, and hardware peripherals.
The role of advanced tech is to make life easier and be inclusive to its users. That's why regionalizing languages is essential. If a common language is deployed in this technology, people from around the world – from different cultural and geographical backgrounds – would find it difficult to use the technology. And with connected devices already coming with a significant learning curve, an additional barrier in the form of language will only add to the confusion.
Why is mindset do critical? It’s high time the users embraced software-driven innovation: some people are still not aware or ready to pay extra for the connected services. However, we should bear in mind that technology has become an integral part of our surrounding, and the challenges of the modern life demands us to optimize driving as well.
Car dealerships will need to become more tech savvy as they will have to spend more time with their customers showing them the advanced technology in their new vehicles. The dealership model will have to keep pace with the introduced changes, spending time and money getting new skills and knowledge.
Another significant challenge for the automotive industry is achieving reliable connectivity. Maintaining broader coverage and network security are complicated and require an excellent understanding and long-standing relationship between automakers and mobile network operators. The smallest lapse in connectivity services can ruin the customer experience and could potentially lead to inappropriate reaction in case of emergencies.
Big data is a considerable advantage when it comes to the Internet of Vehicles technology, but providers face significant challenges in managing the constant data flow. The more infrastructure goes online the faster and more reliable data processing will be required. Insufficient storage or network delays can hinder cloud computing and damage the system.
Connectivity features are introduced to redefine our relationships with our cars. The era of apps turned cell phones into multifunctional devices with unimaginable capabilities. What we need to overcome the major challenges is to focus on the consumer needs which is bound to dramatically change the automotive industry.
The modern world is constantly evolving. Take a precise look around – technologies have already become an inseparable part of our lives, bringing both relief and acceleration. If you either are a devoted driver or involved in the automotive industry you can’t help noticing that the concept of vehicles is getting connected, automated and redefined. The Internet of Things (IoT) has empowered organizations to transform their attitudes to conducting business and generating revenue. Moreover, even those industries that have been able to survive on the same business model for a long time realize it’s high time they changed since in this case technology is not only enabling sustainability but increasing customer satisfaction and quality of experience.
Modern vehicles are extremely intelligent, with a wide range of sensors that collect and process data from in and around the vehicle. In addition, advances in communication technologies allow vehicles use this data to communicate with each other and their surrounding infrastructure. Not only are connected vehicle solutions able to support traffic management but help tackle traffic congestion and road safety concerns in urban settings as well. Why is high-speed connectivity essential in this case? As the car becomes a more tailored transportation method, it demands rapid data sharing and management, and as a result the mentioned innovations can’t be implemented without reliable connectivity. What are the latest connected vehicle innovation trends and how do they influence the automotive industry?
Vehicle diagnostic testing is essential to identify vehicle faults and notify drivers before it becomes a hazard. How does it work? Connected vehicles utilize smart sensors to continuously monitor fuel levels, tire pressure, engine health, and more. In addition, combining advanced analytics with this data provides critical insights into vehicle conditions and enables preventative maintenance. Moreover, telematics functions enabled by diagnostic sensors allow fleet operators to monitor all aspects of their vehicle fleets from a single point.
The developed solutions provide travelers with real-time road condition information in smart cities. Moreover, innovations in the internet of things (IoT) allow vehicles to communicate with a wider range of public infrastructure. Can the applications of vehicles-to-infrastructure go beyond traffic management? You’ll definitely be surprised to learn that they are able to assist with accident alerts and parking availability!
Vehicle-to-everything enables vehicles to communicate with other vehicles, infrastructure, and pedestrians. Thus, autonomous vehicles can improve their navigational performance while contributing to the overall road safety. However, the cost associated with the mentioned systems greatly impacts their adoption.
Connected cars use the internet in order to receive vehicle software updates, contact emergency services, connect with other devices and infrastructure, and more to be mentioned. These assets are especially of great advantage in areas with limited connectivity, such as tunnels and mountainous areas. However, the bandwidth and latency limits of conventional communication methods limit the performance of autonomous vehicles.
Technologies such as artificial intelligence (AI), digital and data analytics platforms, 5G, blockchain, cloud, and telematics are enabling intelligent functions in CVs. AI is used to perform the driver’s tasks, more so in autonomous cars. Digital platforms, 5G, and the cloud enable the seamless collection, sharing, and processing of massive data sets from various sources and in multiple formats. The application of blockchain will ensure data security. Properly analysed, the data gathered provides a better understanding of the entire vehicle ecosystem and helps build new products and services.
As we see, the opportunities for connected car markets are huge –from mobile mechanics to other client-centric mobile services. Real-time location information and trip history for connecting with friends and family anytime can be viewed from any device, which means real-time connectivity. However, there are lots of obstacles on the development path. Which ones and how to overcome them? Stay tuned to learn more! We are about to reveal very exciting news!
Without any doubt, the Blockchain and AI are the two extreme sides of the technology spectrum: one fostering centralized intelligence on close data platforms, the other promoting decentralized applications in an open-data environment. However, if we find a smart way to make them work together, the total positive externalities could be magnified in a blink. The future for businesses that apply this combination of technologies with well-managed neural networks, with accurate data-points which optimize the Blockchain networks will set the absolute standard for all business of the future.
Artificial Intelligence, specifically the neural networks of learned data-points will be able to manage and monitor the Blockchain with the utmost efficiency. Smart business strategies, on the other hand, will build optimum margins.
The Blockchain is a pretty complex technology and its development and maintenance require powerful hardware and software. Nevertheless, AI provides plenty of new possibilities on this matter. As we know, one of the main advantages of the Blockchain is its security of data storing and processing. AI can assist in collecting smart insights about target audience going as far as analyzing the smallest details of customer behavior and using this data to improve the system’s performance with machine learning algorithms. We have started exploring such possibilities only recently but we are already fascinated by the fact how many possibilities it hides. In marketing automation, for example, we can collect insights in real time and send them to secure decentralized database. AI will later use this information for customized smart campaigns.
On top of that, modern AI systems feed on data. In order for algorithms to learn, they need to collect and process information, and through that action get new possibilities. This, however, gives us reasons to be concerned with possible privacy issues. Right now we’ve seen that companies who want to implement AI algorithms in their interactions with clients are frequently questioned about the security of their measures.
The Blockchain helps to make AI more transparent, and therefore, trustworthy. Since there is no single storage that could be targeted by hackers, it significantly increases the system’s safety. As I mentioned before, the Blockchain, by its nature, is a complex innovation which requires not only high programming skills but also powerful tools to handle all the processes. AI computers, along with self-learning assistants, can lend a hand with writing a code and implementing it.
Here are the examples of use cases that point out what the Blockchain and AI can do together.
Similarly, it can monitor the migration of people, groups, and the percentage of terrorist health issues depending on those movements. As AI gets this information, predictions become faster that help government agencies make better decisions regarding immigration policies and health concerns.
Besides, I will highlight a few more domains where one can track down a great combination of AI and the Blockchain. I believe we will face the further developments and emerging benefits.
Surely, digitalization has introduced complicated digital rights to the IP management spectrum, but when intelligent AI finds out the rules of the game it can point out the players who break international copyright law. Mentioning IP contract management, the Blockchain technology triggers immediate payment methods to artists and authors.
Although global organizations like NATO and the UN won’t disappear, the Blockchain technology and AI could both contribute to the development of direct democracy. The Blockchain and AI can transfer big hordes of data globally, tracing e-voting procedures and displaying them publicly so that citizens can engage in real-time.
Smart contracts could also take center stage where transparent information is essential in financial services. Financial transactions may no longer rely on a human “clearing agent” as they’d become automatized, performing more efficiently and faster. However, since confidence in transactions remains dependent on people, AI can step in to monitor human emotions and predict the most optimal trading environment.
SMART ENERGY, SMART BUILDINGS
Moreover, green-friendly AI and the Blockchain help reduce energy waste and optimize energy trade. For example, an AI system governing a building can predict energy use by taking into consideration such factors as the presence and number of residents, seasons, and even traffic details.
Mentioning that the role of AI in Healthcare is dramatic will be an underestimation. AI and Machine Learning can introduce those changes that have an essential impact on healthcare processes and administration. While there is a lot we have to overcome to reach the stage of AI-reliant healthcare, there is sufficient potential in the technology today to push governments, healthcare institutions, and providers to invest in AI-powered solutions.
So far, Artificial Intelligence and Machine Learning are supposed to make the work of healthcare providers more logical and streamlined than repetitive. The technology is helping reshape personalized healthcare services decreasing the time to search for information that is critical to decision making and triggering better care for patients. Artificial Intelligence in Healthcare has immense potential to improve costs, the quality of services, and access to them.
Automated Image Diagnosis with AI/ML
Medical image diagnosis is another AI use case in Healthcare. Besides, one of the most essential trouble spots that medical practitioners have to encounter is handling with the volume of information available to them, thanks to EMRs and EHRs. This data also includes imaging data from procedure reports, pathology reports, downloaded data, etc. Moreover, in the future, patients will send even more data through their remote portals, including images of the wound site to check if there is a need for an in-person checkup after a treatment period.
These images can now be potentially scanned and assessed by an AI-powered system. X-rays are only one piece of the puzzle when it comes to medical imaging. We also have MRIs, CT scans, and ultrasounds.
Oncology and Pathology
Whenever we talk about Artificial Intelligence in Healthcare, we shouldn’t leave deep learning aside. Researchers are using deep learning to train machines to identify cancerous tissues with the precision comparable to a trained physicist. Deep learning presents unique value in detecting cancer as it can help aim at higher diagnostic accuracy in comparison to domain experts.
Machine learning in Healthcare can help reshape the efforts in pathology often traditionally left to pathologists as they often have to work on multiple images in order to reach a diagnosis after finding any trace of deviations. With assistance from machine learning and deep learning, pathologists’ efforts can be streamlined, and the accuracy in decision making can be increased.
While these networks and AI-powered solutions can assist pathologists, we need to emphasize that artificial intelligence is not replacing physicians in this domain any sooner. Deep learning networks can only become so nifty when they get experience and learning over a period, just as physicians do.
AI in Healthcare, specifically in pathology, can help substitute the need for physical samples of tissues by improving upon the available radiology tools – making them more accurate and detailed.
Errors and frauds scar the landscape of healthcare. Therefore, one of the most critical of using AI in Healthcare is providing the security of data and possible solutions. Fraud and breach detection traditionally depended on reviewing systems manually. However, using Artificial Intelligence in Healthcare for monitoring and detecting security anomalies can create trust as the foundation for more digital disruption in the healthcare area.
AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognized opportunities for clinical practitioners and health service organizations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. With modern Medicine facing a significant challenge of acquiring, analysing and applying structured and unstructured data to treat or manage diseases, AI systems with their data-mining and pattern recognition capabilities come in handy. While leaving the communication of serious matters and final decision making to human clinicians, AI systems can take responsibility for routine and less risky diagnostic and treatment processes. The intention here is not to replace human clinicians but enable a streamlined high-quality healthcare delivery process. Healthcare delivery has over years become complex and challenging. A large part of the complexity in delivering healthcare is because of the voluminous data that is generated in the process of healthcare, which has to be interpreted in an intelligent fashion. AI systems with their problem solving approach can address this need. Their intelligent architecture, which incorporates learning and reasoning and ability to act autonomously without requiring constant human attention, is alluring. Thus the medical domain has provided a fertile ground for AI researchers to test their techniques and in many instances; AI applications have successfully solved problems with outcomes comparable to that of human clinicians. As healthcare delivery becomes more expensive, stakeholders will increasingly look to solutions that can replace the expensive elements in patient care and AI solutions will be sought after in these situations.
The role of Artificial Intelligence in Healthcare is not constrained by the mentioned above examples. As trends come up and physicians look for more innovative ways to improve healthcare services and experiences for patients, we will have novel concepts turning into reality. Despite the fact that the healthcare space is the fertile soil for changes, it will be a while before these systems can be made affordable and available to all healthcare institutions.
It is well-known that outsourcing has always been ‘threatened’ by forces–only for it to achieve new levels of scale and innovation. For instance, with the increasing use of cloud computing, companies will very reluctantly prefer having their own set of IT assets and infrastructure. A simple “lease and use” model is something which helps them to be independent from maintaining and handling the issues related to their own IT infrastructure and at the very same time, it is cost-effective too.
Considering the fact that revenues for a majority of outsourcing service providers came from application development, application maintenance and implementation, and the fact that there was no software to install, maintain or develop in the cloud model – the cloud was seen as a threat to the outsourcing model. Today, outsourcing service providers have converted the cloud threat to an advantage by offering services to organizations who want to build their own private clouds, or those who want to migrate from traditional on-premise solutions to the cloud. In summary, outsourcing will remain, and will continue to remain a formidable force – though the scale and the type of services to be offered in the future will significantly differ.
Having said this, how would the future of outsourcing really look like? Given the speed with which we see a shift in trends and technologies in IT industry, it is really tough to take a stance and comment about where is IT Outsourcing heading to? Still few macro trends are there that we can see on the horizon, and which can form the core components of outsourcing business models in the near future. Below are the few trends, which we are bound to play a very important role in shaping the future of IT outsourcing industry.
Started with “Software-as-a-service”, ”Infrastructure-as-a-service” to “Platform-as-a-service”, thanks to the cloud, we have achieved the state where the model has been changed to “Everything-as-a-service”. Today, almost every service can be offered in a virtual way. Consumers, hence, will not worry where the service is coming from. The word ‘offshore outsourcing’ may lose its relevance and a more appropriate word such as ‘virtual sourcing’ may eventually take its place. Services will be consumed via a self-service model, and will require minimum intervention from the service provider. While today we already have Storage-as-a-service, Communications-as-a-service, Network-as-a-service and Monitoring-as-a-service, the future will represent a catalogue driven model, where customers will pick and choose services off the shelf with defined expiry dates.
Most IT services are now commoditized, and this trend is likely to continue in the future. What will define outsourcing service providers is the ability to offer automated self-service platforms which will allow customers to use the platforms of the service providers for performing tasks such as testing or development. Organizations will have the option of creating their own tasks on these self-service platforms, and monitor them using sophisticated tools provided by service providers. For example, a customer may use a regular platform of a service provider to centralize processes, and apply common standards and rules to ensure consistent practices across global locations. Using steps and processes clearly defined in the platform, global organizations can apply automated technologies to enforce enterprise policies.
In the future, we may see organizations take an investor like approach and fund joint initiatives with outsourcing service providers, where both sides invest to create a favorable risk-reward ratio. The co-creation can be in the form of products or service-led innovation, where the client organization looks to make investments to boost the service provider’s capability to develop products or innovative services and reduce time to market. This will not be similar to marketing alliances, but will involve significant amount of research and time to improve a process or system. The same product or service may then be offered to other clients of the service providers, with the revenues and profits shared equally between the service provider and the client. For example, a large airline can use/offer its knowledge and expertise to a cloud hosting company in building a core solution for other small airlines with built-in frameworks, in association with a technology service provider.
While crowd sourcing refers to the art of using the power of the crowd to solve problems or tasks, it has not been harnessed to its full potential in the world of outsourcing. Going beyond the usual tasks of crowd sourcing, outsourcers will search for the help of the community in developing an algorithm, code a new program or create a new method to build a better IT architecture. To do this, outsourcers may set up a managed crowd sourcing model with the help of service providers, who may define the way the process must work through a well defined workflow or template. Crowd sourcing has the potential to dramatically alter the way outsourcing is done today, as it allows outsourcers to tap individual talent located in different corners of the world. A good example of crowd sourcing is Silicon Valley start-up, Kaggle. This company has the world’s largest community of data scientists, and they compete with each other to solve complex problems. Prizes are given to whoever offers the best solution. Crowd sourcing can also help outsourcers develop quality solutions, as the community can rank the best solution for a particular issue or problem.
With the growing adoption of cloud computing and the popularity of the “pay for what you use’ approach, one can expect outsourcing agreements to be defined by outcome-based models where the service provider gets a huge opportunity to demonstrate its capability based on the level of efficiencies achieved or the market share gained. This is a win-win situation for both the service provider and the client, as it focuses on the value generated by the deal. However, as customer value is not a static asset, service providers will need to continuously reinvent themselves as markets evolve, technologies change, economies rise or fall and most importantly change based on what customers want and expect. If they succeed in doing so, they can easily convert small projects into engagements and engagements into long term relationships.
When starting your business, you undoubtedly will look for help. Even when you have experience and knowledge of your industry, there will be areas in which you’re lacking the insight and skill you need — not to mention, pulling together a team helps keep your workload manageable and improves your overall product and operational capacity.
But who should be on your team? Obviously, hiring people who have skills you lack is important, but what is the ideal mix of talents that will ensure your business gets off the ground, and will help you get the attention of investors? As it turns out, many investors are looking for a specific mix when they evaluate startups for a potential investment, and take the team you assembled into account when making your decisions. This means that you can’t simply bring in your friends and family just because they want to “help,” but that you need to identify the individuals who will bring the most to the table in the long term.
So who are those people? They usually fall into several categories, and depending on your focus, you may need one or more individuals in each category.
Your chief technologist should be smart and hungry. Early-stage companies are always, always at the “make it or break it” stage. There is simply no rest for the weary and your technologist will have to shoulder a huge portion of that burden. Without this person, there is no product. You need someone whose intelligence exceeds your own and whose hunger to be the driving force behind bringing a product from concept to creation is overwhelming.
Pick a product perfectionist. You might have a vision. Your technologist can build it. But you need a product person who understands the topography of the market — precisely how your product is going to fit, what will drive demand, how you can iterate to generate and harness excitement. You need that person to be a forecaster, both a realist and a dreamer, who can give you reasonable assurances about the right direction to take the product and company at different points in time. Alternatively, just like finding the right cofounder, pursue a complementary hire (e.g. if you’re the dreamer, find a pragmatist).
Don’t hire people you don’t need. From the very outset and for some considerable time afterward, early startups are quivering on the brink. That’s perfectly appropriate. While certain hires are buoyancy later in a company’s development, there’s no doubt that in the beginning phases, they’re ballast you don’t need. That’s why you usually do not need to hire a general counsel, finance lead or PR person until you hit a more stable patch.
You need a builder.
The process-oriented person would, in turn, loop in a fourth team member with the technical skills to actually build the product. Especially in the technology world, this builder would be a developer or engineer. After acquiring this employee with technical expertise, you now have an idea, research and information backing up that idea, the shell of a product, a system in place to get the product built and the technical skills to do so.
The Sales Animal
Startups with brilliant ideas often forget that someone needs to sell them. Having a strong salesperson on the founding team helps minimize the risk.
In case you are not a technical co-founder, don’t get discouraged, there are plenty of ways you may contribute! Overall, in a start-up, there is a lot to do other than just code. Developing code creates a product, but developing a product and business often times does not center around the code. Therefore, focus on what you can do to strengthen the work of your counter-part by making every other aspect of the business stronger. There are so many things you can do; don’t think of it as “adding value” — you can be, in fact, pure, distilled value.
– User experience & product design: Use the product, play with the product, figure out what works and what doesn’t. Talk to users, get their feedback. Really think about who your users are and make sure that you and your partner are addressing them and their needs (accessibility? mobility? security? other stuff?) in the development of your technology.
– Roadmap/release planning: With your partner(s), sit down and hash out your general road map, release schedules, targeted features per release. As a startup, this is likely to be loose and you may find that you revisit it every few days – but generating and keeping a general schedule gives you all something to work from.
– User interaction: Man the tech support email account, accept and validate bug reports, prioritize feature requests, keep up with the Twitter feed, the Facebook fan page, banter a little–answer questions, be nice, make users feel good/smart, manage their expectations. It really only takes an hour a day to keep up with, but it’s a garden that needs tending and can really pay off in a very loyal user base. If your tech is good, but your users think your startup’s a jerk, they’ll bounce to the next sexy thing as soon as it arrives (many do that anyway, but if they remember how awesome you are, they’ll be back!).
– Test! Test everything you can. Use the heck out of your product. You can do a lot of useful manual testing, you also probably have the ability to learn a little bit of scripting to automate and/or bang on the product even harder. If you don’t already know how, ask your technical cofounder to give you a tutorial on your source control system and show you how to build the software and/or how to deploy a test environment. Report bugs, prioritize them together, track them, and verify that they’re fixed. Test on multiple environments that may consume too much Dev time. I.e., every platform possible.
– Beta programs: If you have these, run them. Solicit testers, write instructions and release notes, pull together mailing lists. Be sure to solicit feedback. Abuse Google forms.
– Localization: Localizing your product? Handle getting things translated. If you’ve got a good technical founder, they give you the string files for translation & can get you up & running with a test environment where you can run and test the your product with translated files when your translators return them.
– Stay abreast of the competition and news in your space. Know who’s doing what and when. Know which competitor’s slagging you when and where, even if you choose to ignore & high road. Don’t obsess over it, but be aware enough not to be taken unawares. Filter chaff & noise and share with your co-founder(s) as needed (this applies to all bullet-points here, actually).
– Track and manage financials: Handle accounting matters or deal with the person who does. Pay taxes, payroll, HR (if applicable), etc.
– Track and manage legal issues: Try to stay abreast of laws and regulation that effect your company. Handle meetings with lawyers, keeping the business in good standing, etc.
– Manage Biz Dev: Handle all incoming queries and meeting scheduling, from bigger fish companies sniffing you out to VC to sales calls to etc. Allow and encourage your partner to hand these off to you to manage/schedule if they get contacted directly and they believe the query’s worth pursuing. It’s likely that people will often attempt to go around you to your partner, but allow your partner to shunt initial queries to you if at all possible–be his/her umbrella all you can. If you’re in a place where you need to make rain, go make some rain.
– Manage Press Contacts: Arrange contacts with the media, help with initial drafts of messages that come from your tech team. Set up alerts and review news, gossip, and mentions related to your company on Twitter, Facebook, Google News alerts, and so on. If your product’s ready for it, keep aware of the contests related to your industry and enter your product whenever and wherever possible.
– Manage Vendors: Pay them, negotiate fees down if you’re bringing in a lot of business, evaluate new ones for new services and redundant backups for existing services.
– Write Stuff: Technical documentation, blog posts, test plans, web site updates, internal how-tos & policy docs (for sharing, potential new partners, compliance, just so’s you don’t forget, etc.), press releases, customer support FAQs, drafts of any legal docs that you may need to work with lawyers for: patents, privacy policies, terms of service, etc., PowerPoint presentations (if you absolutely, positively cannot avoid them), pretty metrics graphs to show investors, etc.
– Strategy: Managing all of the above gives you a pretty good instinct for how/where your startup should move and how quickly. You’re dealing with a lot of external and internal inputs that really help your gut guide you – you know where the market’s going, you know what your customers (say they) want, you know how your partner(s) are feeling and what he/she is capable of accomplishing in X amount of time.