Turning Innovation into Business Growth: A Practical Guide to Technology Strategy

Innovation doesn't necessarily mean creating a new product or process or being the first in using the latest technology. For most organisations, real growth starts with uncovering a problem, identifying customer needs, and coming up with a more efficient way of delivering value.

Technology like digital platforms, data, automation, and AI can help you optimize your operations and seek out new avenues of growth. However, if your new tech isn't aligned with your business, is difficult for your employees to utilize, and you can't measure its effectiveness, your innovation investments will fall short.

A good way to begin might be to frame the question as, "What is the business challenge we are trying to address, and how will we know when we've succeeded?"

What Is Technology-Driven Business Growth?

Business expansion through technology: this type of growth is when companies leverage digital capabilities and tools for growth by either enhancing their performance, using the technology to serve customers, or to offer new products and services.

A service company might automate reminders to consumers for missed appointments. A manufacturing company might automate reports on machine conditions from sensors in the manufacturing process. A small but expanding enterprise might use a customer relationship management package to organize and follow-up customer data.

The tools are different, but the same idea applies: The tool is trying to accomplish a specific business need.

Before opting for a solution, organizations need to know what the current process looks like, what its flaws are, and if technology is the best solution to fix those flaws. Other times, a change in processes can be the best solution, rather than using software.

Five Ways Innovation Can Support Business Growth

1. Improve Existing Processes

As organizations get larger, people, manual tasks and inefficient processes can add up to a burdensome workload. Repeating data over and over, slow approvals, siloed systems and report chaos are some of the things that can take time away from your teams.

A company might start by mapping out a process that repeats itself and finds some additional work, mistakes, or waiting. Then it might compare the following potential improvements: redesign, automation, or a more effective software.

For instance, a business that manually compiles weekly reports could automate data gathering but still have humans review the end numbers. This could cut down on drudgery but wouldn't eliminate the need for oversight.

Measure The key here is to have the original process measured before changes are made. If there is no baseline, how do you know the improvement brings significant value?

2. Make Better Use of Data

Data from sales, customer comments, operational systems and online channels can offer some interesting insights into how well your business is doing. If the data is clear and meaningful, it can aid decision making.

Analytics can show variations in customer demand, common service problems or disparities in product performance. Artificial intelligence can further these insights by detecting patterns, enabling forecasting and helping with a few decisions.

But more data doesn't necessarily translate to better decisions. Companies need to have a clearer sense of what they are looking for and also whether the data they have collected is sufficient for achieving those aims.

Data access and privacy issues, security, and how to interpret outputs. AI generated recommendations are not just good to go but should be cross checked to the business.

3. Develop Products Around Customer Needs

Solutions to customer problem innovation is more likely to generate value.

Companies can explore user needs through interviews, support requests, product feedback, user behavior, and low-cost experiments. These are useful channels for teams to separate the real demand from their assumptions.

Let's say a software company is thinking about a new reporting feature. Instead of jumping straight into developing a whole product, they might interview potential users, test a stripped-down version, and see if it actually helps them accomplish a genuine task.

By providing feedback you can show what features are useful, confusing and what we can do without. This allows the business to change its plans before committing large development time to the project.

While not removing risk, it provides companies with a way of operating with evidence, not passion.

4. Evaluate Emerging Technologies Carefully

Artificial intelligence, the Internet of Things, robotics and smart automation are opening new opportunities in many fields. Their value to a business depends on the problem to be solved, the quality of application and whether the organization is ready for the technology.

In factories, connected sensors monitor equipment and AI tools check for anomalies. Robots can be used to take on intractable or arduous work, while digital systems can enable greater integration across operations.

AIoT is the convergence of AI and connected devices. AI in the physical world: Physical AI is AI systems that operate in the physical world such as robots and intelligent machines. Businesses researching or implementing into the physical space can learn from this Physical AI and AIoT Engines Overview and Applications.

The decision to implement a technology is not solely dependent on what it can do. Integration requirements, maintenance costs, cybersecurity, the cost of workforce training and continued maintenance can all contribute to making an initiative worth it.

5. Build a Culture of Learning

Markets, customers and technologies evolve. Organizations that experiment and learn can adapt more successfully.

Information from the front line: front-line employees see problems that executives might not. Providing employees with a practical method of reporting concerns may also help organizations identify opportunities for improvement.

There still must be limits to experimentation. Teams must be clear about what they are testing, how they will know when they've succeeded, and how to learn from failure.

A so-called failed experiment may be valuable if it is clearly defined, kept within its bounds, has known expense, and the tale is captured on tape. The aim is not to chase the new but to determine which new to chase.

Obstacles to Growth Enabled by Technology

Even high potential innovation projects can fail when implementation issues are ignored.

No clear goals: Projects that lack a business reason risk turning into costly experiments. Every project needs to solve a defined problem and be able to measure success.

Incompatibilities: New devices could not integrate easily with current technologies. Organisations should consider integration, data access, data security and maintenance needs before settling.

A technically advanced solution might not provide value if it is hard to use or not well understood. Explain it to the teams and train them on it.

Budget limits: Cost of package, infrastructure, implementation, training, support, maintenance and future enhancements, all need to be included in the total cost. These should be weighed against the expected benefits.

The fact that a technology project has been finished does not mean that business performance has been affected. Companies should measure their actual results and have the courage to stop or modify projects that are not working.

A Practical Framework for Implementing Innovation

Innovation doesn't have to be daunting. Businesses can overcome uncertainty through innovation.

Step 1: Pick a problem to solve. Select a problem related to customer experience, operations, product, or revenue.

Step 2: Set a benchmark. Measure how you are currently performing with appropriate metrics like processing time, error rate, customer churn, operating costs or down time.

Some are technological and some are process solutions. Think about which ones will be the most cost-effective, the risk involved, what kind of integration will be necessary, and the skill level that will be required to maintain the solution.

Step 4: Pilot on a small scale. Pilot in a small number of cases with clear success criteria. Use input from the pilot stakeholders.

Step 5: Consider the evidence. (Then compare against baseline). Calculate any unanticipated expenses, mistakes and inefficiencies, training and disruptions to the operation.
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