Informed i’s Weekly Business Insights

Two big shifts are under way in the world of software development. Since the launch of Chatgpt in 2022, bosses have been falling over themselves to try to find ways to use generative artificial intelligence (AI). Most efforts have yielded little, but one exception is programming. Surveys suggest that developers around the world find generative ai so useful that already about two-fifths of them use it.

The profession is changing in another way, too. A growing share of the world’s engineers come from emerging markets. There is no standard definition of a developer, but one way to assess this is to look at the number of users of Github, a popular platform for storing and sharing code. In 2020 the number of users living in poorer countries surpassed those from the rich world.

These shifts matter because software talent is greatly treasured. Salaries are high. The median wage of a developer in America sits in the top 5% of all occupations, meaning that coders can earn more than nuclear engineers.

New technologies have often aided developers; the internet, for instance, ended the time-consuming task of answering questions using textbooks. Generative ai looks like a bigger leap forward still. One reason why it can be especially useful for developers is the availability of data. Online forums, such as Stack Overflow, hold enormous archives of questions asked and answered by coders. The answers are often rated, which helps AI models learn what is helpful and what is not. Coding is also full of feedback loops and tests that check if software works properly. ai models can use this feedback to learn and improve.  The consequence has been an explosion of new tools to help programmers.

AI’s helpfulness is still somewhat limited, however. When Evans Data, a research firm, asked coders how much time the technology tends to save them, the most popular answer, given by 35% of respondents, was between 10% and 20%. Some of this is from churning out simple “boilerplate” code, but the tools are not perfect. One study from GitClear, a software firm, found that over the past year or so the quality of code has declined. It suspects the use of ai models is to blame. A survey by Synk, a cybersecurity firm, found that more than half of organisations said they had discovered security issues with poor ai-generated code. And ai still can’t tackle the thornier programming problems.  The next generation of tools should be better.

Much of this seems to give inexperienced engineers a leg up. They will be able to do more complex tasks more quickly and some of the work they used to do may be picked up by laymen. A rising trend towards “low-code-no-code” platforms, which allow anyone to write software, will also be boosted by ai.  Another result of the coding upheaval is that junior developers in rich countries will face more acute competition from abroad.  Offshore capabilities have also been growing more sophisticated.

What all this means for developers is still unclear. One vision is of ai and offshoring taking Western software developers’ jobs en masse. That seems far-fetched. Huge amounts of technical know-how are still required to string pieces of code together and check that it works.

A more optimistic view is one in which the most boring parts of making software are done by computers while a developer’s time is spent on more complex and valuable problems. This may be closer to the truth. For customers, meanwhile, the trends are welcome. IT managers have long said that their bosses want ever more digitisation with ever tighter budgets. Thanks to ai and offshoring, that may no longer be too much to ask.

As of late September, about 3 million metric tons of unmilled rice was in storage in Chhattisgarh, some 800 miles southeast of New Delhi, alone.  Images of decaying rice have gone viral on social media, with the country’s stockpiles reaching an estimated record amount just as this year’s harvest was set to begin.

In May 2022, to keep domestic prices in check and ensure a steady supply for a program that provides free grain for 800 million people, Prime Minister Narendra Modi began imposing restrictions on food exports, at first banning shipments of wheat and later adding curbs on rice and sugar. The moves by India, the world’s leading rice exporter, roiled international markets and angered farmers adversely affected by falling prices.

Following this year’s successful monsoon growing season, Modi’s government in September relaxed some of the restrictions—the most significant change being an end to the export ban on non-basmati white rice introduced in July 2023.  But there is still an export duty on parboiled rice and a minimum price imposed on shipments abroad of the white variety of the grain, and nothing has changed for exports of wheat and sugar.

Modi must strike a delicate balance, ensuring the welfare of farmers and keeping a lid on consumer food prices, which have increased at an average rate of about 8% this year. Ahead of this year’s general election, farmers had been protesting, demanding a legally guaranteed floor on prices. Although Modi won a third five-year term in June, his Bharatiya Janata Party lost dozens of rural seats in the vote.  Prior to state elections in October and November, the government has made other policy tweaks to placate farmers.

India’s rice exports from April to August, at 6.4 million tons, were down by 24% from the same period a year earlier, government data show. The curbs contributed to higher global prices, affecting buyers across the Middle East, Southeast Asia and Africa.

Rice stocks held by the Food Corporation of India (FCI), which buys grain from farmers at guaranteed prices and distributes to the poor for free, stood at about 32 million tons on Sept. 1, up 39% from a year earlier.  To clear some of the surplus, the government has decided to sell as much as 2.3 million tons of the rice from the state reserves to ethanol producers—a controversial practice in a country that’s No. 111 out of 125 nations counted in last year’s Global Hunger Index.

The curbs on outbound shipments may have needlessly hurt farmers, as they don’t solve high domestic food prices. That’s because a significant share of the retail cost of food comes from processing, packaging and transportation.

Geoffrey Hinton, a computer scientist whose pioneering work on deep learning in the 1980s and ’90s underpins all of the most powerful AI models in the world today, has been awarded the 2024 Nobel Prize in physics by the Royal Swedish Academy of Sciences.  Speaking on the phone to the Academy minutes after the announcement, Hinton said he was flabbergasted: “I had no idea this would happen. I’m very surprised.”

Hinton shares the award with fellow computer scientist John Hopfield, who invented a type of pattern-matching neural network that could store and reconstruct data. Hinton built on this technology, known as a Hopfield network, to develop backpropagation, an algorithm that lets neural networks learn.

Hopfield and Hinton borrowed methods from physics, especially statistical techniques, to develop their approaches. In the words of the Nobel Prize committee, the pair are recognized “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

But since May 2023, when MIT Technology Review helped break the news that Hinton was now scared of the technology that he had helped bring about, the 76-year-old scientist has become much better known as a figurehead for doomerism—the idea that there’s a very real risk that near-future AI could precipitate catastrophic events, up to and including human extinction.  

Doomerism wasn’t new, but Hinton—who won the Turing Award, the top prize in computing science, in 2018—brought new credibility to a position that many of his peers once considered kooky.

Hinton’s views set off a months-long media buzz and made the kind of existential risks that he and others were imagining (from economic collapse to genocidal robots) into mainstream concerns. Hundreds of top scientists and tech leaders signed open letters warning of the disastrous downsides of artificial intelligence. A moratorium on AI development was floated. Politicians assured voters they would do what they could to prevent the worst.  Despite the buzz, many consider Hinton’s views to be fantastical.

The excitement around generative AI (gen AI) and its massive potential value has energized organizations to rethink their approaches to business itself.  Building on McK’s interactive “The data-driven enterprise of 2025,” help executives think through seven essential priorities that reflect the most important shifts that are occurring, what the main complexities are, and where leaders can focus their energy to realize the data-driven enterprise of 2030.

1.      Everything, everywhere, all at once.  By 2030, many companies will be approaching “data ubiquity.”  Data leaders will need to adopt an “everything, everywhere, all at once” mindset to ensure that data across the enterprise can be appropriately shared and used. 

2.      Unlocking ‘alpha’.   The problem with mass adoption of AI is that many organizations are using the same tools or developing similar capabilities, which means they’re not creating much competitive advantage. To unlock “alpha” (a term investors use for obtaining returns above benchmark levels) with gen AI and other technologies, data leaders need to have a clear focus on data strategies that can deliver competitive advantage.

3.      Capability pathways: From reacting to scaling.  The ease of use of many basic tools and their increasing availability have generated a proliferation of often-disconnected use cases, pilots, and features. To enable the scale required to operate data-driven businesses in 2030, data leaders will need an approach that accelerates how use cases provide impact while solving for scale through an architecture that can support the enterprise. To achieve this, data leaders need to build “capability pathways,” which are clustered technology components that enable capabilities that can be used for multiple use cases.

4.      Living in an unstructured world.  For decades now, companies have been working with structured data. That’s just 10 percent of the data available, however. Gen AI has opened up the other 90 percent of data, which is unstructured (for example, videos, pictures, chats, emails, and product reviews).  This windfall of data can greatly enrich companies’ capabilities, especially when combined or integrated with other data sources.  Data leaders will need to invest in building new capabilities such as natural-language processing to help convert the unstructured data so that LLMs can “understand” and use it, as well as in testing and recalibrating LLMs continually as models and corresponding data sources are updated.

5.      The ability of companies to achieve their data and AI vision by 2030 will rely substantially on leadership.  To get on the right track, companies need to find leaders who are skilled in three major areas: governance and compliance; engineering and architecture; and business value.

6.      The talent profiles of organizations will likely look very different in 2030.  These require AI leaders to develop a clear view of what new skills are needed.7.      Guardians of digital trust.  Risk has become much more of an area of concern with the rise of advanced technologies—most notably AI and gen AI. Governments are moving quickly to roll out new regulations, and companies are evaluating new policies.  Three types of risk stand out: new types of attacks, broadening landscape for risk, and new ‘unknowns.’

As the markets celebrate the success of gen-AI and green-tech start-ups, many experts are urging established companies to emulate those ventures by committing to radical innovation—by disrupting themselves before someone else does. But for a lot of incumbent companies, that’s just not a feasible strategy.  To the contrary, when managed well, the innovation process can both leverage and transform a company’s existing operations. It requires careful management. A new approach is complex and nuanced but hold promise. The organizations have managed to overcome this complexity by following 3 stage process.

1. Exploration: Find Your Start-Ups.  Innovators often work for decades to tackle grand challenges and turn visions of the future into reality. Many of those efforts will end in failure; only one or two will go on to change the world. Start-ups are hardwired for this journey.  Unfortunately, this model doesn’t work for the management and stakeholders of established companies, which are designed to provide existing goods and services reliably and to adapt to customer preferences. Corporate governance and capital controls are in place specifically to ensure this.  Those constraints drive many incumbents to adopt a limited innovation strategy: focusing on incremental improvements to existing businesses or collaborating with just one or two like-minded partners on pursuing a narrow ambition. That can be dangerous.  The incumbent innovators the authors studied avoided this by following three practices in the first stage of their journey: setting up multiple partnerships, establish hubs, and groom intrapreneurial talent.
2. Commitment: Leverage Your Advantages.  Once a venture yields a breakthrough, hopes and expectations begin to soar.  Instead of framing their commitment as an either-or investment decision, smart incumbents see it as a carefully managed escalation of their involvement. When working with start-ups, they will shift from a relatively loose, hands-off relationship to a more collaborative one. At this stage they’ll help the smaller venture find creative ways to remove roadblocks and prepare to scale up.  To determine whether an innovation project has cleared all the hurdles necessary for larger investment, the incumbent needs to address four questions: Is the business model viable?  Do we have an ecosystem that will support growth?  How ready are our customers to make purchases?  And how can we win support from other stakeholders?
3. Getting to Scale: Move Fast.  Once a new venture’s business model becomes viable and the interest of a critical mass of users or customers is clear, the adoption of an innovation tends to take off exponentially, accompanied by a frantic race to market. So it’s vital for incumbents to be organized to rapidly mobilize their resources and scale innovations up quickly.  At this stage investment is the only factor preventing a new venture from realizing its full potential. While other obstacles may still crop up, the risk of not committing is now greater than the risk of committing. Unfortunately, at just this point incumbents often develop cold feet and start bogging the new venture’s leadership team down with last-minute questions and hurdles that hamper execution.  Four actions can help leadership teams of incumbent ventures avoid obstacles to scaling up: make the CFO a direct stakeholder, pitch a conservative case to the board, beware the differentiation and synergy traps, and put an entrepreneur in charge.

Barra, a General Motors lifer, took the helm at GM in January 2014. Just a few weeks into the gig, she found herself navigating a catastrophic recall of millions of GM-made cars due to faulty ignition switches, some of which had caused fatal accidents. Barra spent much of her debut year on Capitol Hill, testifying in front of lawmakers who grilled her about why the defects had gone unfixed for so long.

It was the kind of experience that might make you want to quit before year two—but this January, Barra celebrated year 10. Leading the company through the recall crisis gave her momentum to reform GM’s culture and reorganize it to prepare for an electric-vehicle revolution; it also helped her earn the No. 1 position on Fortune Most Powerful Women list from 2015 through 2017. Since then, she’s led GM to its strongest financial position since its 2009 bankruptcy, when the government was compelled to provide the automaker a financial lifeline. And this year, she reclaims our top spot.

Along the way, Barra joined an elite group. Hitting the decade mark is a rarity for any public company CEO, and even more so for female leaders: The average tenure of a Fortune 500 CEO is 7.2 years for men and just 4.5 years for women. Among the 55 women who currently lead Fortune 500 companies, Barra is one of only nine who have been CEO for a decade or more.

To earn longevity, of course, you have to deliver results. GM brought in $171.8 billion last year, a nearly 10% increase year over year, and its best performance in 17 years. Late last year it reached an agreement with the United Auto Workers that ended contentious contract negotiations and a costly, historic strike. Even with the strike’s drag on productivity, GM raked in profits of $10.1 billion, and its stock is up nearly 35% so far in 2024—a testament to Barra’s leadership.

That said, Barra’s biggest mark on the company remains, well, a question mark. The CEO has committed GM to the ambitious goal of going all-electric by 2035—and its efforts have sputtered a bit of late. (The company recently backed away from a previous target of having the capacity to produce 1 million EVs in North America by 2025; it delivered only 38,355 EVs in the region in the first half of 2024.) GM has also run into challenges in autonomous vehicle development, another priority of Barra’s. 

But as Barra says herself, transformation is not a straight line. The CEO remains committed to her stated goal of zero crashes, zero emissions, and zero congestion. It just might take a while to get there. Fortune caught up with Barra—interviewing her while she was in a car, naturally—to ask about the last 10 years, today’s challenges, and the road ahead.

What’s the secret to your longevity?  Part of it is having a great team. But also it’s being agile and continuing to understand how the environment is changing, not waiting and letting things happen to you, but being proactive.

You came into the CEO role at a tough time in the company’s trajectory. How did that first year inform your leadership?  [The crisis] was an opportunity to demonstrate that we’re going to do the right thing for our customers, be transparent, and do everything in our power to make sure this doesn’t happen again. We lived the values. We put policies in place saying we want people to speak up—if you see an issue, you need to say something. I always say to employees that the best time to solve a problem is the minute you know you have one. Because problems don’t usually get smaller, they get bigger.

Your first year is pretty easy to define: You had to focus on righting the ship. How do you think about your subsequent years as CEO, going into this current era?  As a leadership team, together with our board, we decided we’re not going to wait to be disrupted. We’re going to transform.  Then every year we just continue to adjust the strategy and look at: How do we go through this transformation?

Selecting the right candidate for an executive role ranks high on the list of the most consequential organizational decisions. Every selection decision is a prediction, but instead of ensuring those predictions are accurate, companies selecting leaders make easily correctable errors. Here are seven:

1. Selection Decisions Are Left to Chance.  Selection decisions made on instinct and gut feeling.  We need to consider the implications for your organization’s selection methods. How many people are hired because they effectively wooed the interviewers? What biases are inherent in interviews that hinder an organization’s ability to be objective?
2. Face‑to‑Face Interviews Reward Likability.  The biggest impediment the authors see to effective selection is an overreliance on face‑to‑face interviews, and almost every company uses them.  Simply put, face‑to‑face interviews are a waste of time unless they have a clear focus, use meaningful assessment criteria, and employ effective questioning. Likability in interviews has a disproportionate impact on how a candidate is weighed against others.
3. Unstructured Interviews Show Poor Validity.  Unstructured interviews cannot meaningfully predict future performance because they rely on the subjective opinions and interpretations of both the interviewer and the candidate. Structured could be equally problematic because of two reasons. First, typically no credible research has been conducted to explore the range of responses an interviewer might encounter and which of them is more predictive of job performance. Second, interviewers introduce their own preconceptions and biases when interpreting a candidate’s responses.
4. Companies Mislead Candidates and Damage Their Brand.  Companies want applicants to feel valued during the process, and this can result in inadvertently misleading candidates about their chances if communication doesn’t set clear expectations.  Mismanagement of the process in this way is damaging to the candidate as well as the company’s recruitment brand.  Instead send a disclaimer to candidates that clearly outlines expectations for the process.
5. 360-Degree Assessments Are Subjective at Best, Biased at Worst.  Research has shown that individuals demonstrate flaws and biases when evaluating others, and aggregating these flawed judgments doesn’t correct these errors.  Violating confidentiality can also diminish a hiring company’s brand and will lead to the early withdrawal of otherwise excellent candidates.
6. Traditional Search Firms Have Conflicts of Interest.  Search firms that identify candidates and then determine their capabilities are guilty of a serious conflict of interest. They are most profitable when they can quickly recommend candidates, secure their commission, and then move on to the next assignment. They are implicitly incentivized to advocate for candidates who are already on their rosters rather than identifying new applicants who might be better suited for a role.  We need to split sourcing and assessing between independent parties to have the best, rather than the first, candidate who will be hired.
7. Confidential Reference Checks Are Unreliable.  Every failed executive received glowing references from someone who vouched for them, claiming to know them well. So why did they fail? How can it be that a referee — someone who is prepared to put their reputation on the line to vouch for a candidate — can get it so terribly wrong?  The reason is simple: People aren’t that good at picking people — even people they know well. The characteristics observed by a referee may not translate to an elevated role in a new company with a new boss, different expectations, and unique systems and processes.

Whether it’s meeting people face-to-face at a conference or building connections online, cultivating a professional network is a crucial step in reaching one’s career goals. However, many business leaders still make all-too-common networking mistakes that undermine their efforts.  Below, 17 Forbes Coaches Council members share their best advice on what to do differently to avoid these potential networking pitfalls.

1. Strive To Give Value.  Many view networking as an opportunity to receive something of value. This is a mistake. Always make it your goal to give value during networking events. Whether it’s providing advice, sharing a story or giving a referral, delivering value to others demonstrates your ability to contribute. This in itself makes you memorable and likable.
2. Consider What You Can Offer.  A common mistake is focusing solely on what you would like to get from networking relationships. One tip is to think about what you can offer—in terms of support, help and information—to your networking relationships. Are there doors you can open for them? Is there a course or book you’ve read that might be helpful to them? The most effective networkers think of it as building relationships.
3. Build Rapport.  One common mistake business leaders make when networking in person or online is that they mainly focus and talk about themselves and their business. Instead, they should be building rapport, connecting with others and looking into ways they can add value to others.
4. Focus On Meaningful Relationships.  One common mistake leaders make is focusing too much on pitching themselves instead of building genuine connections.
5. Prioritize Listening. One mistake business leaders often make when networking is focusing too much on selling themselves rather than building genuine relationships. Instead of pushing their agenda, they should prioritize listening and engaging with others by asking open-ended questions.
6. Lead From A Confident Context.  One common mistake leaders make when networking is not believing in themselves. Remember: Content is king, but context is the kingdom. What’s the story, or context, you’re telling yourself as you network? Leading from the context that you are worthy of connection and belonging will go a long way to calm anxiety and nerves. 
7. Converse Authentically.  Many business leaders like to speak to be heard, so unfortunately, some aren’t as good at listening. Instead of meeting people and instantly talking about what you have to offer, converse with people authentically to gain insight into who is in the room and their needs.
8. Make It Relational, Not Transactional.  Networking is relational, and you should demonstrate through your communication that you want to expand your sphere of influence and thought, rather than stacking a deck of business cards. Serving as a resource and adding value to other leaders can be rewarding.
9. Focus On Mutual Benefit.  Networking yields the best results when business leaders focus on building relationships of benefit to both parties instead of trying to extract gains only for themselves. It’s important to spend time learning how to offer value to new connections. This approach yields mutually advantageous connections that can be sustained over the long run.
10. Let Go Of Your Agenda.  Let go of the agenda and follow the energy; genuine connections will guide you to the right places, even if it logically doesn’t make much sense.
11. Start A Generative Dialogue.  Networking becomes much more effective when we do not lead with who we are and what we do (essentially our résumé). This feels like a sales pitch and a monologue. The most effective and genuine networkers I know always begin a conversation—a generative dialogue—with a person or group. Bring your curiosity, explore how to help and let the conversation evolve. It’s fun and not stressful!

The others are: 

Be Genuinely Interested In Others

Collect As Many Business Cards As You Can Instead of Giving An Many As you Can

Reframe Your Networking Mindset

Lead With Vulnerability

Follow Up After The Initial Connection

Have Two-Way Conversations

Navigating generational gaps isn’t always easy, but it is necessary if you want to hire–and retain–top talent. By surrounding yourself with workers who have life experiences different from your own, you’ll have access to more perspectives, empower unconventional ideas for solving problems, and learn more about your business’s specific needs.  For Inc.’s annual CEO survey, more than 1,000 leaders of Inc. 5000 companies shared what they’ve learned from their younger employees, their own children, and Gen-Z in general. Here are some of their best lessons.

1. Lead with kindness.  “From my children, I’ve learned the profound impact of kindness and respect in leadership. They consistently show love and compassion to each other and to my wife and me, teaching me that genuine respect fosters a more willing and motivated team. Forcing someone to follow directives isn’t effective; rather, when you lead with kindness and show respect, people are far more likely to follow you willingly and with enthusiasm.” 
2. Empower your employees.  “The ‘do as I say because I said so’ mentality doesn’t work anymore. Younger generations have outgrown that style of fear-based control. They don’t want to be bossed around–they want to be empowered and inspired. They seek role models and mentors from their leadership that they can aspire to be like.” 
3. Value personal growth.  “My oldest son is autistic. When you have a child with a disability, you realize very early that your parenting experience will be different from your friends and neighbors. Milestones (e.g. first step, first word, first book) will happen at their own pace and in their own order. I learned not to evaluate my son’s growth relative to others. This has had a profound effect on my leadership. I evaluate my teams based on growth. Are they getting better? Are they learning new things? Are they working together better?”
4. Protect your curiosity.  “As they have grown up, my children have taught me the importance of remaining always curious and never believing you already know the answer. I like to say, ‘Dummy up.’ When you come from a point of curiosity and wonder (like kids), you may still come to the same conclusions, but with so many new insights.
5. Let employees fail (and learn).  “You don’t know what you don’t know. Sometimes I observe my son or a new employee making a decision that I know is wrong based on my experience. However, it reminds me that there was a time when I didn’t know the right decision either. Everyone needs the space to make mistakes and learn from them. Allowing people to fail is crucial for their growth and helps them make better decisions in the future.” 
6. Be direct.  Being direct does not mean being rude. It means providing a level of clarity that is understood by the child or employee.”
7. Attitude is everything.  “Leadership starts from the top and starts with your attitude. My kids don’t care about my bad day, or if I didn’t get enough sleep last night and am tired. They just know it’s time to play and want to hang with Dad. Employees are the same — they don’t want a leader who isn’t fully present and ready to go.”
8. Keep it simple and truthful.  Do what you say you will do; say what you are going to do; and always keep your word. In a child’s eyes, life is that simple and they are so correct.”  Transparency, candor, and consistency are key.”
9. Love what you build.  “I remember telling my kids that YouTube was its own company before Google bought it for a billion dollars. They were shocked, wondering why anyone would sell something so cool, and questioning why they’d need a billion dollars when they could own YouTube. It was interesting seeing things through their eyes: sometimes the real value is in loving what you build and enjoying the journey, not just aiming for an exit. You don’t have to sell if you love what you do. Sometimes, you’re worse off if you give it up.”

Over the past three or four years, startups and small businesses have confronted unique challenges like the lingering effects of the pandemic, fluctuating inflation rates and global supply chain disruptions. Surviving, much less thriving, has required herculean effort and resilience. As we head into the last quarter of the year, what insights can we gain from the successes of small businesses in 2024?

Growing from a startup to a successful and sustainable business demands years of dedication — a truth the author can wholeheartedly attest to as he look toward 20 years of entrepreneurship. The average startup’s growth rate plummets from an impressive 268% in year one to 71% by year three, so what should we anticipate for year five? Year 10? You don’t necessarily need to go across state lines, chase outside investments or go public to achieve regular and repeated growth as a startup. Here are four important lessons he has learned from leading a company that continues to thrive and be recognized for its growth after 17 years in business.

1. Let your customers be your business compass.  Your customers are the foundation of your success. What’s best for your customer is best for your business, and what benefits your customer will ultimately benefit your business. Every decision and move you make should reflect their needs, wants and pain points. When you truly listen to your customers so they feel heard and supported, you can shape your products, services and overall business model to consistently deliver value. Couple this with a steadfast commitment to exceptional customer service and you’ll see lasting loyalty and create customers for life.
2. Embrace automation, but preserve human connection.  Time is one of your most valuable resources as a startup founder, and streamlining routine day-to-day tasks with technology frees you up to focus on innovations and strategies to better serve your customers.  Embrace automation wherever you can, but your goal should always be to complement the critical human elements of your business rather than replace them. Customer service is one example of an area where I refuse to compromise on genuine human connection because our relationships with our customers are our greatest asset.
3. Success is built on relationships.  Relationships are foundational to business success and nurturing them is critical to your growth journey. Investing in and celebrating your employees, vendors, partners, customers and community will strengthen your business from the inside out.
4. Establish trust through expertise.  A lasting competitive edge is more than your product or service — establish your brand as an authority that customers can rely on for accurate and valuable guidance and expertise. Solidify trust and your reputation by becoming a go-to expert in your field.